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THE USE OF DIGITAL IMAGE PROCESSING TO FACILITATE DIGITIZING LAND COVER ZONES FROM GRAY LEVEL AERIAL PHOTOS
A THESIS PRESENTED TO THE DEPARTMENT OF GEOLOGY AND GEOGRAPHY
IN CANDIDACY FOR THE DEGREE OF MASTER OF SCIENCE
By
JOAN M. BIEDIGER
NORTHWEST MISSOURI STATE UNIVERSITY MARYVILLE, MISSOURI
April 2012
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DIGITAL IMAGE PROCESSING
The Use of Digital Image Processing to Facilitate Digitizing
Land Cover Zones from Gray Level Aerial Photos
Joan Biediger
Northwest Missouri State University
THESIS APPROVED
____________________________ Thesis Advisor, Dr. Ming-Chih Hung Date
____________________________ Dr. Yi-Hwa Wu Date
____________________________ Dr. Patricia Drews Date
____________________________ Dean of Graduate School, Dr. Gregory Haddock Date
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The Use of Digital Image Processing to Facilitate Digitizing
Land Cover Zones from Gray Level Aerial Photos
Abstract
Aerial imagery from the 1930s to the early 1990s was predominantly acquired using
black and white film. Its use in remote sensing applications and GIS analysis is
constrained by its limited spectral information and high spatial resolution. As a historical
record and to study long-term land use/land cover change this imagery is a valuable but
often underutilized resource. Traditional classification of gray level aerial photos has
primarily relied on visual interpretation and digitizing to obtain land cover classifications
that can be used in a GIS. This is a time consuming and labor intensive process that can
often limit the scale of analysis.
This research focused on the use of digital image processing to facilitate visual
interpretation and heads up digitizing of gray level imagery. Existing remote sensing
software packages have limited functionalities with respect to classifying black and white
aerial photos. Traditional image classification alone provides limited results when
determining land cover types derived from gray level imagery. This research examined
approaching classification as a system which uses digital image processing techniques
such as filtering, texture analysis and principle components analysis to improve
supervised and unsupervised classification algorithms to provide a base for digitizing
land cover types in a GIS. Post processing operations included smoothing the
classification result and converting it to a vector layer that can be further refined in a GIS.
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Software tools were developed using ArcObjects to aid the process of refining the vector
classification. These tools improve the usability and accuracy of the digital image
processing results that help facilitate the visual interpretation and digitizing process to
gain a usable land use/land cover classification from gray level imagery.
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TABLE OF CONTENTS ABSTRACT…………………………………………………………………………. iii LIST OF FIGURES……………………….………………………………………….vii LIST OF TABLES………………………………………………….………...….…. viii ACKNOWLEDGMENTS……………….…………………………………..………..ix
CHAPTER 1: INTRODUCTION……………………….……………………………1 1.1 Research Objective…………………………………………………………… 4 CHAPTER 2: LITERATURE REVIEW…...……………………………………….. 5 2.1 Historical Aerial Imagery Uses and Importance……………………………... 5 2.2 Classification Problems of High Resolution Panchromatic Imagery………….6 2.3 Statistical Texture Indicators…………………………………………………. 9 2.4 Image Enhancements and Filtering……………………………………….….. 13 2.5 Image Segmentation and Object-based Image Analysis……………………... 15 CHAPTER 3: CONCEPTUAL FRAMEWORK AND METHODOLOGY……... ….17 3.1 Description of Study Area………………………………………………….… 17 3.2 Description of Data…………………………………………………………… 17 3.3 Methodology………………………………………………………………….. 21 3.3.1 Conceptual Overview…………………………………..……………….. 21 3.3.2 Software Utilized…………………………………………………………22 3.3.3 Preliminary Image Processes……………………………...………….…. 24 3.3.4 Unsupervised Classification.……………………….…………………… 27 3.3.5 Supervised Classification……………………………………………….. 33 3.3.6 Image Enhancement and Texture Analysis……………………....………35 3.3.7 Object-based Image Analysis………………….…………………………38 3.3.8 Post Processing and Automation…………………………………………40 3.3.9 Accuracy Assessment………………………………………...……….….43 CHAPTER 4: ANALYSIS RESULTS AND DISCUSSION……………….………46 4.1 Manual Digitizing…………………………………………………………….. 46 4.2 Unsupervised Classification………………………………………………….. 48 4.3 Supervised Classification…………………………………………………….. 53 4.4 Image Enhancements and Texture Analysis……….………..………………... 57 4.5 Object-based Image Analysis………………………………………………… 63 4.6 Post Processing and Automation……………………………………………... 65 4.7 Classification Accuracy and Results…………………………………………. 70 CHAPTER 5: CONCLUSION………………………………………………………81 5.1 Limitations of the Research…………………………………………………... 81 5.2 Potential Future Developments………………………………………………. 81
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APPENDIX 1: ERROR MATRIX TABLES……………………………………….. .84 APPENDIX 2: VECTOR EDITING TOOLBAR .NET CODE……………………..101 REFERENCES..………………….…………………………………...……………... .106
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LIST OF FIGURES Figure 1 – Aerial photo of Ogden study area.………..………………………………… 18 Figure 2 - Overview of study areas in relationship to the state of Utah……………….. 18 Figure 3 – Aerial photo of Salt Lake City study area…………..………………..…….. 19 Figure 4 - Ogden DOQQ study area…………………………………………………… 20 Figure 5 - Salt Lake City MDOQ study are……………………………………….…… 21 Figure 6 – Main workflow processes…………………………………………………... 23 Figure 7 – Ogden dendrogram of ISODATA clustering 10 classes..………………….. 29 Figure 8 – Ogden dendrogram of ISODATA clustering 25 classes…………………… 30 Figure 9 – Ogden dendrogram of ISODATA clustering 100 classes………………….. 31 Figure 10 – Distances between classes from Salt Lake City dendrogram…………..…. 32 Figure 11 – Training sample distribution for the Ogden image………………………...34 Figure 12 – Training sample distribution for Salt Lake City image………………….... 34 Figure 13 – Unstretched images compared to contrast stretched images……………… 36 Figure 14 – Post processing ArcGIS Model…………………………………………… 41 Figure 15 – Polygon raster to vector, smoothing, and smooth simplify………………...42 Figure 16 – Classification using visual interpretation of the Ogden image……………. 49 Figure 17 – Classification using visual interpretation of the Salt Lake City image…….49 Figure 18 – Ogden image ISODATA classifications………………………..………… 51 Figure 19 – Salt Lake City image ISODATA classifications………………………….. 53 Figure 20 – Minimum distance and support vector machine classification of the Salt Lake City image……………………….…………………………………... 57 Figure 21 – Minimum distance classification of the Ogden image with high pass filter 58 Figure 22 – Minimum distance classification of the Ogden image with low pass filter. 59 Figure 23 – ISODATA 10 spectral classes Halounova image…………………………. 61 Figure 24 – SCRM object-based segmentation images………………………………... 64 Figure 25 – Ogden object-based classification image and post processing system
vectors……………………………………………………………………... 68 Figure 26 – Salt Lake City object-based classification image and post processing system vectors………………………………………………………….… 69 Figure 27 – Ogden pixel based classification and post processing system vectors……. 69
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LIST OF TABLES
Table 1 – First level classification Ogden land use/land cover classes………………… 25 Table 2 – First level classification Salt Lake City land use/land cover classes………… 27 Table 3 – ISODATA overall accuracy results for Ogden and Salt Lake City study areas ………………………………………………………………………………………….. 50 Table 4 – Training sample statistics from original Ogden image…………….………… 55 Table 5 – Training sample statistics from original Salt Lake image…………………….56 Table 6 – Ogden image overall accuracy and level 1 completion time………….………72 Table 7 – Salt Lake City image overall accuracy and level 1 completion time…….…... 74 Table 8 – User’s accuracies for individual land use/land cover types Ogden study area. 76 Table 9 – User’s accuracies for individual land use/land cover types Salt Lake City study
area…………………………………………………………………………... 77 Table 10 – Overall accuracy ranges for classification groups………………………….. 78
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ACKNOWLEDGMENTS I would like to thank Dr. Ming-Chih Hung for chairing my thesis committee and for
all the support, encouragement and guidance he has given me along the way. I would
also like to thank Dr. Yi-Hwa Wu and Dr. Patricia Drews for serving on my thesis
committee and for their contributions in developing this thesis. Last but certainly not
least I would like to thank my husband Barry for encouraging me through many long
nights and weekends while I completed this work. Without your support and love I
would never have been able to finish this thesis.
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CHAPTER 1: INTRODUCTION
Aerial imagery from the 1930s to the present is a primary data source used to study
many natural processes and land use patterns (Carmel and Kadmon 1998, Kadmon and
Harari-Kremer 1999). Early aerial imagery from the 1930s to early 1990s is
predominantly black and white (panchromatic) film photography meaning there is only
one band of data. This type of imagery contains limited spectral information unlike
today’s satellite digital sensors, which offer more spectral information even in the
panchromatic band.
The Aerial Photography Field Office (APFO) is a division of the Farm Service
Agency (FSA), of the United States Department of Agriculture (USDA). The APFO,
located in Salt Lake City, Utah, has one of the nation’s largest collections of historical
aerial imagery dating back to the 1950s. Film from the 1930s through 1940s was sent to
the national archives. APFO has over 50,000 rolls of film of which over 60% is black
and white (Mathews 2005). This historical aerial imagery is a valuable, largely untapped
resource. The film format of the imagery makes it unavailable to GIS and imagery
analysis programs unless it is scanned and processed to digital format. There is
widespread interest from the public and other government agencies in making this
imagery available and usable in digital format.
Recently, more historical imagery from the 1950s to 1990s is being scanned to digital
format for use in change detection projects for the Farm Service Agency (FSA).
According to Brian Vanderbilt (personal communication, 01 Sep 2009) FSA is interested
in studying agricultural loss patterns over long periods so that processes of change can be
more fully understood. One of the challenges with these types of projects is that land
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use/land cover classification with the imagery usually involves visual interpretation and
manual digitizing, due to the difficulty of using digital image processing techniques with
the historical panchromatic imagery. Manual digitizing is a time consuming process for
multiple years of imagery, as each photo requires its own analysis. There are not enough
image analysts within FSA to manage the increasing workload for projects requiring the
use of historical imagery. Another concern is that study areas are limited in scale because
of the time and resources needed to digitize land cover types on the imagery. There is
interest and need to explore digital options for land cover classification so that the use of
these historical imagery datasets can be expanded.
The ability to facilitate digitizing of land cover types on historical aerial imagery
would make it a more usable resource to study long term land use/land cover changes.
Classification of this type of imagery is very labor intensive which often limits the size of
study areas. If the imagery could be utilized on a more broad scale, we can gain greater
historical perspective on changes such as agricultural loss over time. Increased accuracy
and repeatability of results obtained by using digital image processing could make the
results of long-term change detection projects more valid rather than having to rely on
varying levels of image interpretation skills if a project requires several image analysts to
interpret imagery for a project.
Historical aerial imagery offers a unique opportunity to study long-term patterns of
land use/land cover change by offering the analyst a more extensive historical perspective
on geographic processes such as land use/land cover change, urban expansion and
vegetation patterns (Kadmon and Harari-Kremer 1999, Awwad 2003, Alhaddad et al.,
2009). Producing a thematic map through image classification is one of the most
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common imagery analysis tasks in remote sensing. Image classification techniques such
as unsupervised and supervised classification, NDVI, spectral signatures, and spectral
band combinations have limited usability with panchromatic aerial imagery as they rely
heavily on spectral information, which is limited with this type of imagery.
Visual interpretation of imagery does not rely on spectral information alone to classify
imagery. Visual interpretation makes use of scene qualities such as texture, shape,
arrangement of objects and context of elements in an image. The human visual system is
very efficient at pattern recognition and in many ways is superior to existing machine
processing methods, but on the other hand inherent subjectivity and the inability of the
eye to extract complex patterns can limit interpretation. Digital image processing
techniques that incorporate the use of texture, tone, shape, pattern recognition and object-
based image analysis can be used to enhance traditional methods of supervised and
unsupervised classification especially with gray level aerial imagery (Caridade et al.,
2008).
A great deal of research has been done on the most effective ways of classifying
multispectral imagery and mapping the results (Jensen, 2005). There is relatively little
research on how digital image processing of historical panchromatic imagery can
improve or reduce manual interpretation for image analysis and GIS analysis. In this
thesis research, digital image processing techniques including texture analysis,
convolution filters, and object-based image analysis were considered in respect to how
they can improve the classification of panchromatic aerial imagery and how this
improvement can facilitate digitizing and in some cases possibly eliminate it. A post
processing system involving image smoothing, raster to vector conversion, polygon
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smoothing and simplification, and custom polygon editing tools for use in ESRI’s
ArcMap GIS software was used to improve an initial digital image classification. The
post processing system can be used to improve most digital image classifications. The
quality of the baseline land use/land cover classification was the main factor in how
efficient it was to create a usable thematic layer.
Continued study in this area could yield new approaches to land cover classification of
gray level imagery. If historical imagery has the ability to be used effectively in a digital
environment, then more of it may be scanned and become more readily available, which
would benefit the geospatial community.
1.1 Research Objective
The objective of this project is to establish a working model that utilizes digital image
processing to facilitate or assist the user with digitizing land cover zones from gray level
aerial photos. This study approaches the problem of digitizing land cover zones by first
classifying the aerial photo and then by establishing a post processing system employing
vector layers for use in a GIS.
There is limited research available in using digital image processing to enhance the
classification process of gray level aerial photos and the digitizing process. Digital image
processing may not be able to completely replace visual interpretation of this type of
imagery, but it may be able to make the process more efficient.
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CHAPTER 2: LITERATURE REVIEW 2.1 Historical Aerial Imagery Uses and Importance
Historical imagery as referred to in this study refers to imagery acquired by an aerial
camera mounted in an airplane. The photography has been directly imaged onto film and
is also referred to as analog photography as opposed to modern digital imagery. This
historical imagery is black and white and may be referred to as either panchromatic or
gray level.
Black and White, gray level, and panchromatic are terms which refer to imagery
composed of shades of gray. The imagery used in this study has a pixel depth of 8 bits
where the binary representation assumes that 0 is black and 255 is white. Between 0 and
255 raw pixel values are grayscale and the digital numbers correspond to different levels
of gray. For example a digital number of 127 will correspond to a medium gray in the
photo. This panchromatic imagery has a single band where digital numbers represent the
spectral reflectance from the visible light range. Historical panchromatic imagery
contains brightness values but has limited spectral information available in the visible
wavelengths (0.4-0.7µm), unlike the panchromatic band of a satellite image such as
Landsat 7, which generally is sensitive into the near infrared wavelengths (.52-.0.9µm)
(Hoffer 1984).
Historical aerial photographs are a valuable and important data source for studying
long term (20 – 80 year) change processes such as land use/land cover change and
vegetation and environmental dynamics. These historic photos present a snapshot in time
that may offer insight into the current state of land use/land cover change processes and
what patterns may have affected their growth and stability. Much of the imagery
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available for long-term analysis is black and white aerial photography (Carmel and
Kadmon 1998, Hudak and Wessman 1998, Caridade et al. 2008). The historical record
that has been captured from aerial photography provides a long temporal history to work
with and provides an extensive frame of reference in which to assess the magnitude of
land use/land cover change. Advances in GIS, photogrammetry, image analysis, and
digital image processing have increased the potential to use historical aerial photography
for many types of change analysis including land use/land cover change (Okeke and
Karnieli 2006).
Gray level historical aerial photos used to produce land cover maps are generally
created through techniques such as visual interpretation and manual digitizing (Carmel
and Kadmon 1998, Kadmon and Harari-Kremer 1999). This is a very time consuming
and labor intensive process. This fact has a tendency to limit analysis to small areas. The
digitizing itself is generally dependent on the ability of the interpreter and may lead to
results that are not objective due to skill level and human bias (Kadmon and Harari-
Kremer 1999). The assumption is often made that manual interpretation is 100 %
accurate but assessing the accuracy of this method is difficult according to Congalton and
Green (1993) and Carmel and Kadmon (1998).
2.2 Classification Problems of High Resolution Panchromatic Imagery
The historical aerial imagery analyzed in this project is limited in spectral information
and has high spatial detail. These two variables can present some difficulties with the use
of common digital classification and image processing techniques. The first challenge is
the spectral resolution, which is only one band. This band lacks detailed spectral
information. Most panchromatic aerial films are sensitive to the visible spectrum but also
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require filtering to take into account haze and atmospheric conditions. The film is
generally filter exposed to green and red visible wavelengths and not the blue
wavelengths to cut down on atmospheric haze. The resulting image records in black and
white the tonal variations of the landscape in the scene (U.S. Army Corp of Engineers
1995). Common classification methods are limited in accuracy and usability when there
is only one band to work with (Short and Short 1987, Anderson and Cobb 2004, Caridade
et al. 2008).
Research from Carmel and Harari-Kremer (1999) and Carmel and Kadmon (1998)
have approached the limitations of having only one band of information to analyze in
several ways. Carmel and Kadmon (1998) used a combination of illumination
adjustment and a modified maximum likelihood classifier that used neighborhood
statistics to achieve classification accuracies of over 80% for study of long-term
vegetation patterns using gray level aerial imagery. This research showed that the
relationship between neighborhood pixels was an important factor in achieving improved
classification accuracy. Carmel and Harari-Kremer (1999) concentrated on training data
and ancillary data to produce vegetation maps from black and white aerial photos from
1962 and 1992. The accuracy of using a maximum likelihood classifier was about 80%.
Their study stresses the importance of carefully considered training data and the utility of
digital image processing of historical aerial photography in vegetation change detection
studies. Mast et al. (1997) researched long-term change detection of forest ecotones
using gray level aerial imagery from 1937 – 1990. Density slicing was used after
determining the range of brightness values for tree cover across all imagery to get a
classification of tree cover and no such. Results were satisfactory although no accuracy
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assessment was mentioned, but again the significance of object brightness values for gray
level imagery was established.
The second challenge when analyzing this imagery is that higher spatial resolution
does not generalize features to the degree coarse or medium scale imagery does, which
allows much more detail to be considered in an image. Individual trees, buildings and
sidewalks become visible when image detail is more perceptible in these 1-meter
resolution images. This factor makes visual interpretation easier but can cause problems
with automated classification, especially when spectral information is limited or non-
existent. High spatial resolution can increase within-class variances, which can cause
uncertainty between classes. Browning et al. (2009) in their study of historical aerial
imagery as a data source emphasized the importance of object scale when analyzing
imagery. Some objects may be larger than a pixel, referred to as H-resolution, and some
objects may be smaller than a pixel, which is referred to as L-resolution. This factor can
make imagery with multiple scale objects more difficult to get consistent classification
results across a scene. Spatial autocorrelation is also an important factor when
considering this concept, as all natural scenes in remote sensing will have some type of
spatial autocorrelation to create a scene, so that the image organization is something other
than random noise (Strahler et al. 1986).
The challenges of limited spectral information and high spatial detail can lead to a
number of features in an image having similar gray level signatures and a great deal of
confusion between class types (Fauvel and Chanussot 2007). In turn a per pixel classifier
such as the maximum likelihood classifier has difficulty distinguishing between a
medium gray field and water in a panchromatic image. Panchromatic image
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classification can be improved by considering the relationship between neighborhood
pixels as in texture analysis and object-based image analysis (Alhaddad et al. 2009,
Myint and Lam 2005, and Caridade et al., 2008).
2.3 Statistical Texture Indicators Image texture is one of the most important visual indicators in distinguishing between
homogenous and heterogeneous regions in a scene. The human interpreter uses shape,
texture, size, pattern, shadow, arrangement and context of elements in an aerial photo to
distinguish between objects in the image (Campbell 2008). According to Tuceryan and
Jain (1998) texture is easy to discern in an image but it can be a difficult concept to
define and there is not one generally accepted definition. One way to define texture is to
consider it as the spatial variation of the intensity values in a region of an image
(Tuceryan and Jain 1998). This regional variation in intensity values implies that the
evaluation of texture is a neighborhood process and that a single pixel does not create
texture on its own.
Texture is also a quality of an image scene that corresponds to a pattern that is part of
the structure of the image. In a natural scene an area of farmland and a forested area
comprise two separate visual patterns in separable regions. These regions may also
contain secondary patterns having characteristics such as brightness, shape, size, etc. A
field may also have a planting pattern and a forest may be comprised of deciduous and
coniferous trees giving the area a distinctive sub pattern that has its own brightness,
shape, size, etc (Srinivasan and Shobha 2008). Texture as a property of an object or
regional feature in an image can be described as fine, smooth, coarse, etc. Tone is the
range of shades of gray in an image. According to Haralick (1979), tone and texture are
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interdependent concepts in that both are always present in an image to varying degrees.
This interrelationship between tone and texture is explained by Haralick (1979) as
patches in an image that either have little variation in tonal primitives (tone) or a patch
that has a great variation of tonal primitives (texture).
The work of Haralick et al. (1973) was the foundation for most of the later research
relating to image texture analysis. Their work provided a computational method to
determine textural characteristics in an image scene and discussed several widely used
textural statistics used in image texture recognition. These statistics included: contrast,
correlation, angular second moment, inverse difference moment and entropy. Contrast
measures the amount of local variation in an image. Correlation measures the linear
dependency of gray levels in the image. Angular second moment measures local
homogeneity. Inverse difference moment also measures local homogeneity but relates
inversely to contrast. Entropy measures randomness of values. Image analysis may be
performed using these measures either alone or in combination.
There are three main approaches to texture analysis. These approaches include
statistical, spectral and structural. Statistical methods are based on local statistical
parameters such as the co-occurrence matrix and variability within moving windows.
Spectral methods include analysis using the Fourier transform and structural methods
emphasize the shape of image primitives (Srinivasan and Shobha 2008). This study
utilized statistical methods to include the co-occurrence matrix, the occurrence measures
and moving windows. By evaluating the spatial distribution of gray values using
statistical methods, a set of statistics can be derived from the distributions of neighboring
features throughout the image. There are first order and second order texture statistics.
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First order statistics such as mean, standard deviation, and variance analyze pixel
brightness values without analyzing the relationships between the pixels. Second order
statistics on the other hand analyze the relationships between two pixels and these
measures include contrast, dissimilarity, homogeneity, entropy, and angular second
moment (Srinivasan and Shobha 2008). First order and second order statistics are used in
this study as a method to improve the classification accuracy of panchromatic aerial
photos.
The analysis of texture is a technique that has been used to aid and increase
classification accuracy in both gray level image analysis and multispectral analysis.
Haralick et al. (1973) conducted the first major study of texture as an imagery analysis
tool. They demonstrated the utility of the Gray Level Co-occurrence Matrix (GLCM) as
an analysis tool for panchromatic aerial photographs and multispectral imagery even
though computer processing constraints of the time hindered their study. The
classification accuracy in their study was 82% for the panchromatic aerial imagery.
Caridade et al. (2008) used the GLCM and a variety of moving window sizes to achieve
an overall classification accuracy of black and white aerial photos of 83.4% using four
land cover classes. The GLCM uses statistics such as dissimilarity, angular second
moment, homogeneity, contrast, entropy etc. to statistically determine the frequency of
pixel pairs of gray levels in the image. Caridade et al. (2008) also discusses the variation
of land cover type accuracies throughout an image. Their study shows that certain land
cover types such as water may achieve accuracy levels of 100% while others such as bare
ground are much lower at 76.5%. Cots-Folch et al. (2007) used the GLCM to train a
neural network classifier but the highest accuracy obtained was only 74%. Their study
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stated that better training data and ancillary data sources could be used to improve the
results. Maillard (2003) compared the GLCM to semi-variogram and Fourier spectra
methods and found that the GLCM works better in areas where textures are easily
distinguished and the semi-variogram is better in areas where texture is more similar.
The Fourier method was less successful than either of the other two methods. Alhaddad
et al. (2009) found that the GLCM and mathematical morphology produced results which
were closer to visual interpretation than other texture analysis methods.
One of the main utilities of texture analysis as it applies to improving the classification
of panchromatic imagery in particular is that it increases the dimensionality of the
imagery from one band to multiple bands. A new band is created for each texture
function. This increased dimensionality can help alleviate some of the problems of class
separability that arise when trying to classify historical aerial photos (Halounova 2009).
Halounova used a combination of texture, filtering and object oriented classification to
achieve overall accuracy levels between 89% and 92%. Their methodology of increasing
the dimensionality of panchromatic imagery to try to achieve more separability between
land use/land cover classes was an important influence on this thesis research.
In areas of heterogeneous objects, the texture information in neighborhood pixels is a
consideration. Common classification algorithms that rely on spectral information at the
pixel level do not consider spatial information. This spatial information can become very
important when trying to discern land cover types such as urban areas (Myint and Lam
2005). Two types of analysis can assist the classification process: region-based analysis
and window based analysis. Region-based analysis involves using image segmentation
and window based analysis can be used in pre- or post-classification to filter noise from
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the results (Gong et al. 1992). The importance of the spatial aspect of texture analysis is
illustrated in many studies involving texture analysis (Haralick 1973, Gong et al. 1992,
Hudak and Wessman 1998, Myint and Lam 2005, Erener and Duzgun 2009, Pacifici et al
2009). This study used region-based analysis during object-based image analysis and
window based analysis through the GLCM.
2.4 Image Enhancements and Filtering
Texture analysis in combination with image pre-processing such as principal
component analysis has been explored by Awwad (2003). His study, which utilized a
1941 gray level photo, used texture analysis windows of different sizes and then
combined the results to create an image with sixteen layers. Principle components
analysis (PCA) was used to reduce the dimensionality of the resulting image. He
combined several digital processing techniques but overall accuracy was only 58%.
Much of the literature on using digital image processing techniques for classifying gray
level aerial photos does not make use of multiple texture window sizes in combination to
return a result. Even though examples are rare in the literature and accuracy was low as
reported by Awwad (2003), the technique has promise. Halounova (2009, 2005) also
combined several texture window sizes but used filtering and object oriented
classification rather than PCA to achieve classification accuracies over 90%. Image
enhancements such as filtering and texture add multiple channels to the one band
panchromatic image and allow the image to be processed in a similar fashion to a
multiple band image. There is room for more research using this type of methodology
with different parameters and different pre- or post-processing results such as
convolution filtering, edge detection and smoothing windows.
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Edge detection is another important consideration when trying to separate a scene into
distinct objects. A natural scene such as an aerial photo does not necessarily have a clear
relationship between an object and a background. Anderson and Cobb (2004) provided a
new unsupervised hybrid classification algorithm based on edge detection and
thresholding for pixel classification. Nearest edge thresholding outperformed both the
maximum likelihood and ISODATA clustering classification schemes. Their study
illustrated the importance of edge detection between features in gray level aerial photos.
Li et al. (2008) also conducted research, which concentrated on the importance of edge
detection and shape characteristics. The process used was automated using ArcGIS
Model Builder and results were compared to manual digitizing with the model correctly
identifying 70% of the manual classifications. Hu et al. (2008) used grayscale
thresholding in regards to image segmentation and emphasized the importance of
transition regions between objects in a scene and the ability to segment objects in an
image. Transition regions between objects can be problematic when classifying complex
scenes, as there can be multiple areas in the image with different gray scales between
objects causing classification errors and a salt and pepper effect.
Texture filters in combination with neural network classifiers are another methodology
that has shown some success in land use/land cover classification of gray level aerial
photos. Ashish (2002) used several artificial neural network (ANN) classifiers based on
histograms, texture and spatial parameters with some success on 1993 gray level aerial
photos. Textural parameters yielded the highest overall accuracy at 92%. His study
further showed the importance of texture parameters for classification of gray level aerial
photos. Another study conducted by Pacifici et al. (2009) used a neural network
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classifier and a simplification procedure with some success on the panchromatic bands of
WorldView-1 satellite imagery. After the simplification procedure called “network
pruning” was used on the imagery, texture was optimized and input features were
reduced producing classification accuracy above 90% in relation to the Kappa coefficient.
Their study provided another example of how texture parameters can improve the
classification accuracy of different types of classifiers using high resolution panchromatic
imagery.
2.5 Image Segmentation and Object-based Image Analysis
Considering the high spatial resolution of gray level aerial photos and the lack of
spectral information, object-based image analysis is another technique that has been
successful in classifying high spatial resolution imagery. Object-based image analysis
(OBIA) is a method of image analysis that uses objects in a scene rather than individual
pixels to derive information from the imagery. OBIA is a two-part process consisting of
image segmentation and then image classification. The image is first divided into
homogenous and adjacent regions, which take into account texture, region context, shape
and spectral information during the segmentation phase. Image segmentation reduces the
complexity of the image, and produces regions in the image, which can in turn be
considered meaningful to the image interpreter.
OBIA was compared to pixel based classification in a study by Pillai and Wesberg
(2005) using gray level aerial imagery from 1965 and 1995. Their study illustrated how
scale dependency can affect classification results depending on the objects studied. Scale
dependency of individual landscape elements can also affect the usefulness of texture
parameters as illustrated in Resler et al. (2004). Change at the scale of individual trees
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was not statistically significant between pixel based classification and object-based
classification. Object-based classification was more accurate when comparing patches of
trees in high spatial-resolution panchromatic imagery. Their study illustrates the
importance of determining land use categories and object scale when classifying imagery.
Elmqvist et al. (2008) performed OBIA on the panchromatic band of an Ikonos image
and found that spectral information provided the best segmentation results. Classification
accuracies were fairly low for their study but outperformed pixel based classification.
Laliberte et al. (2004) used a combination of low-pass filtering and object-based image
analysis on gray level aerial photos successfully integrating gray level aerial photos and
satellite imagery in a change detection study. Middleton et al. (2008) successfully used
feature extraction and a support vector machine (SVM) supervised classifier to extract
features on a 1947 aerial image in a change detection study. One of the main conclusions
of their study was that classification accuracy of the panchromatic image was based on
image quality. Historic panchromatic imagery is not always of good quality due to age or
deterioration of the film. A successful methodology for classifying this type of imagery
needs to be successful for various levels of image quality.
The literature regarding classification of gray level aerial photos concentrates for the
most part on replacing manual digitizing with digital image processing techniques. There
is a gap in the literature in regard to using digital image processing to help facilitate
digitizing. By combining digital image analysis techniques such as texture and object-
based image analysis with GIS vector capabilities, digitizing land cover classification
zones can be enhanced and in some cases possibly eliminated.
17
CHAPTER 3: CONCEPTUAL FRAMEWORK AND METHODOLOGY
3.1 Description of the Study Area
The study area for this project is near Ogden, Utah (Figure 1). The area is in north
central Utah (Figure 2) and consists of a variety of land cover types including agricultural
land, impervious surfaces, grassland, forest and water. The Ogden study area does not
provide an example of dense urban land cover so a secondary area of interest was chosen
in Salt Lake City, Utah (Figure 3). The Salt Lake City study area includes a park and a
variety of residential and commercial land cover. By using two study areas with a variety
of textures and objects in the scene, this research can show the usefulness of digital image
processing across two completely different areas and images.
The classification results concentrate on the Ogden imagery as this imagery has better
defined and larger areas of land class types. The Salt Lake City image is used mainly to
see how the same techniques can be used in an urban area. Urban areas have their own
unique classification challenges that are increased when trying to classify panchromatic
imagery. Another reason the Ogden image was the main focus of this research is that this
imagery was originally flown for FSA for agricultural purposes. It is also likely that
much of the historical imagery in the vault at APFO will be used to further study
historical agricultural change processes.
3.2 Description of Data
The image of Ogden, Utah from 1958 was obtained from the Aerial Photography Field
Office’s internal imagery storage network. The Ogden study area was clipped from a
digital orthophoto quarter quadrangle (DOQQ) 4111256ne from 1958 (Figure 4) and
covers approximately 0.5 square miles. The image was scanned from black and white
18
Figure 1 – Aerial photo of Ogden study area
Figure 2 - Overview of study areas in relationship to the state of Utah
19
Figure 3 – Aerial photo of Salt Lake City study area
film at APFO using a standard 25 microns which produces about 1016 Dots Per Inch
(DPI). The imagery was originally flown at 40,000 feet producing a pixel resolution of 1
meter and the bit depth of the image is 8 bits. This imagery was also ortho rectified at
APFO using the Socet Set 4x software suite and was rectified to the Universal Transverse
Mercator (UTM) coordinate system zone 12, North American Datum of 1983 (NAD 83).
The imagery is in GeoTIFF format, which can be used in a variety of imagery analysis
and GIS programs.
The image of Salt Lake City, Utah from 1977 (Figure 5) was obtained from the Utah
State Automated Geographic Reference Center interactive imagery website:
http://gis.utah.gov/images/sgidraster/SLCo_1977_DOQ.html. The Salt Lake City study
area was clipped from a Mosaiked Digital Orthophoto Quadrangle (MDOQ) q1219_1977
20
and was scanned and ortho rectified at APFO using the same parameters and methods as
the 1958 Ogden imagery. Q1219_1977 is a mosaic that was created from original
DOQQs using Socet Set 4x and interactive seaming. The image resolution is 1 meter and
the bit depth is 8 bits.
Figure 4 - Ogden DOQQ Study Area
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Figure 5 - Salt Lake City MDOQ Study Area
3.3 Methodology 3.3.1 Conceptual Overview
The research for this study involved a number of steps. The preliminary image
processing included creating a subset of the study area from both the 1958 imagery and
the 1977 imagery. A subset was used to cut down on processing and digitizing time.
Once the study areas were created, the classification scheme was determined and finally
heads up digitizing was performed on both images in order to obtain the digitized
baseline information for comparison to automated classification and to use as ground
truth data to test the accuracy of the digital imaging techniques. After the preliminary
22
processing was completed, a number of digital image processing techniques were
performed on the imagery (see Figure 6). The original imagery was classified using
supervised and unsupervised classifiers to form the classification baseline information.
Then four main digital image processing techniques were used to try to improve the
classification. These four processes were: convolution filtering, texture analysis,
principle components analysis, and object-based image classification. Texture analysis
was used to create layer-stacked images which increased the dimensionality of the
original one band image to improve classification results. Principle components analysis
was used to decrease the dimensionality of the multiple layer texture images and in one
case the first principle component image derived from the multi-layer texture image was
layer stacked with the original one band image. The final digital image processing
component in the research was image post-processing to refine the most promising results
for GIS analysis. After image post-processing an accuracy assessment was completed to
compare the results of each classification with the digitized baseline information obtained
by visual interpretation (heads up digitizing).
3.3.2 Software Utilized
There were three software programs used in this project as no single software suite
available to me provided all the tools needed for this research. The imagery analysis
programs used were ERDAS Imagine version 11.0, ENVI 4.8 and ENVI EX 4.8. The
GIS software used is ArcMap 10.0. ERDAS Imagine has a good set of texture analysis
and filtering tools. ENVI EX and ENVI have the benefit of integration with the GIS
software and ENVI EX provided a wizard based feature extraction toolset for object-
based image classification. The main interface used to provide the baseline land use/land
23
Figure 6 – Main Workflow Processes
24
cover zones to aid or facilitate the manual digitizing process is ArcMap 10 as this
software has good vector tools, and the ability to integrate ENVI image analysis tools
into ArcMap Model Builder.
3.3.3 Preliminary Processes
The study area was clipped from the original DOQQs using the ERDAS Imagine
subset tool. The area covers approximately 0.5 miles in both project areas to facilitate
digitizing and image processing. Much of the image processing including the use of
convolution filters; texture analysis and classification methods required trial and error to
find the best settings and analysis methods for the imagery. The best results were
analyzed further using post processing, vector conversion and editing.
Heads up digitizing was performed on the Ogden and Salt Lake City imagery. This
provided the digitized baseline information as ground truth to be used later in the
classification accuracy assessment. Heads up digitizing was performed using ESRI’s
ArcMap 10.0 software. A geodatabase was created for both the Ogden imagery and the
Salt Lake City imagery.
One person performed the visual interpretation of the imagery for the sake of
consistency. The interpreter has had eight years of work experience using photo
interpretation to create a variety of map types for the Defense Mapping Agency (now the
National Geospatial Intelligence Agency). The times were recorded so that a comparison
can be made between manual digitizing and digital image processing to determine the
efficiency of digital image processing.
The determination of land use classes was an important consideration as it had a great
deal of impact in the final results of image classification especially for panchromatic
25
imagery since so many land use/land cover types have similar digital number (DN)
values. Classification schemes in previous studies using black and white aerial imagery
have used relatively limited categories (Kadmon and Harari-Kremer 1999, Laliberte et al.
2004, Okeke and Karnieli 2006, and Pringle et al. 2009). This study includes three levels
of classification detail for the study areas. The approach looked at the classification of
the imagery in a bottom up manner going from a high level of detail in representing the
land cover types existing in the imagery to grouping these types into larger categories.
This strategy was used to determine how useful detailed digital analysis of the imagery
was compared to visual interpretation. The first level of classification of the Ogden
imagery was based on eight land use/land cover classes including water, forest, grassland,
dark fields, medium fields, light fields, bare earth and impervious surface (Table 1). At
this level it was too difficult to represent the cropland as one class as there is too much
variation between fallow fields and fields that are growing or wet. There was also
confusion between the most representative digital number values between dark, medium,
and light fields as there are pattern variations in the respective fields.
Table 1 – First level classification Ogden land use/land cover classes
Class Name Description
Water Lakes, Reservoirs, Rivers
Forest Areas of trees with a canopy cover greater than 50%
Grassland Areas dominated by grasses and herbaceous plants with little or no tree or shrub cover
Dark Fields Agricultural cropland area characterized by dark gray tone DN ~ 0-122
Medium Fields Agricultural cropland area characterized by medium gray tone DN ~ 100-188
Light Fields Agricultural cropland area characterized by light gray tone DN ~ 151-200
Bare Earth Areas of earth, sand, and rock with little to no vegetation
Impervious Surface Buildings, roads, parking lots
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The second level of classification took the eight classes and combined them into three
larger groups: cropland, vegetation, and other. Finally, the third level of classification
consisted of cropland and non-cropland. The results of these classifications and their
impact on classification accuracy were obtained by combining the results of the initial
classifications rather than running new supervised and unsupervised classifications to
reflect these combined groupings.
The classification system used on the Salt Lake City image also used a bottom up
approach starting out with a more detailed classification and then moving to more general
groupings. The first level of classification consisted of five land types including
commercial, transportation, trees, grass, and residential (Table 2). The second level
classification was reduced to built up areas, vegetation, and transportation. The third level
of classification consisted of built up areas and non-built up areas. The Salt Lake City
image has entirely different characteristics from the Ogden image, as the Salt Lake City
image is comprised of a mixed type urban area without any agriculture, bare earth, forest,
or large bodies of water. The added classification difficulty in the Salt Lake City image
was that the commercial and residential areas are made up of a mixture of manmade and
natural materials. These areas consisted of thousands of small buildings and may be
surrounded by either grass or concrete, all of which provide a very complex pattern of
shapes and surfaces which were tonally very similar. There were many tonal similarities
existing in the Ogden imagery as well but the land cover types such as dark fields, light
fields, water, etc. are fairly homogenous blocks unlike the patchwork of the urban areas.
27
Table 2 – First level classification Salt Lake City land use/land cover classes Class Name Description
Commercial Built up area consisting of industrial, commercial complexes
Transportation Transportation network including major streets and highways
Residential Mixed area that includes single family homes, apartments, trees, and grass
Grass Areas dominated by grasses and herbaceous plants (yards, fields)
Trees Woody vegetation < 20ft tall
3.3.4 Unsupervised Classification
Unsupervised classification was performed on the original subset of the Ogden and
Salt Lake City images to provide the unsupervised classification baseline information for
comparison to digital classifications with image enhancements. This initial classification
was completed using ENVI 4.8 tools for ArcGIS and the ISODATA clustering algorithm.
This clustering algorithm essentially divides the image into naturally occurring groups of
pixels. Similar pixels are grouped together. Three classification sets were used to
process the imagery: 10, 25, and 100 spectral classes. After the imagery was classified,
these groups were interactively assigned an information class by visually comparing the
classified image and/or reference data. Since many of the spectral classes have similar
tonal values and statistics, it was necessary to assign some of these mixed classes to
either the most numerous type or the type with the most concentrated areas of pixels.
There was room for interpretation, and there is a certain amount of subjectivity involved
in assigning these classes. The interpreter needs to be familiar with the study area, and
when some classes are divided between seemingly equal areas, it was difficult to
determine which was the best class to assign the pixels to. In some cases a spectral class
was divided between 3 or 4 information classes. At this stage there was not a method to
split these classes into their respective groups using the ENVI or ArcGIS software. It is
28
possible to use masking and a technique called cluster busting, but this methodology was
not used in this research, as it requires a significant amount of extra processing.
The unsupervised classification process did provide some useful general information
about the imagery. It was very difficult to assign classes to the detail level land
classification system used for both the Ogden and the Salt Lake City images. After
aggregating classes and assigning them a land use/land cover type from the classification
scheme, there were about five classes that could be distinguished in the Ogden image and
three in the Salt Lake City image. A useful tool to visualize how the clusters in an image
are derived is a dendrogram. Dendrograms were created using the ArcGIS software for
the same number of classes and iterations as the unsupervised classifications (Figures 7,
8, 9). A dendrogram is a graphic diagram in the form of a tree that is used to analyze
clusters in a signature file (ESRI 2011). The dendrograms are used to show the clustering
process from individual classes to one large cluster. The dendrogram tool takes an input
signature file created in ArcMap and creates the diagram based on a hierarchical
clustering diagram. The classes are clusters of pixels and the graph illustrates the
distances between merged classes. The dendrogram helps to illustrate how the 10, 25,
and 100 classes are distributed using the ISODATA classifier. Many of the classes
overlap and are very close together numerically, which is why unsupervised classification
on panchromatic imagery often gives the user unsatisfactory results. The dendrograms
also illustrate the relatively small changes in class distances between having 10, 25, and
100 classes. Dendrograms of the Salt Lake City imagery were very similar except for
slight differences of distances between pairs of combined classes (Figure 10). The
29
ISODATA classifier only returned 67 classes instead of 100 for the Salt Lake City image
and 93 out of 100 for the Ogden image.
Figure 7 - Ogden dendrogram of ISODATA clustering 10 classes
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Figure 8 - Ogden dendrogram of ISODATA clustering 25 classes
31
Figure 9 - Ogden dendrogram of ISODATA clustering 100 classes
32
10 Classes 25 Classes
100 Classes
Figure 10 – Distances between classes from Salt Lake City dendrograms
A K-Means unsupervised classifier was also used to classify an Ogden texture image
incorporating the mean, variance and homogeneity bands. This classifier provided a
more satisfactory result on the texture images than the ISODATA classifier did. The K-
Means classifier in the ENVI software uses a set number of classes provided by the
analyst, and classes are determined after the classifier iterates through the image and the
optimal separability is reached based on the distance to mean (ENVI 2011). The
ISODATA classifier had difficulties with the texture image and returned a completely
33
gray image unless the classes were increased to well over 25. Considering how time
consuming it was to assign classes to the result the K-Means classifier was used. Ten
classes and 25 classes were used on the texture image.
3.3.5 Supervised Classification
Supervised classification was performed on the original image subsets to create the
supervised classification baseline information. Later on, another supervised classification
was performed on images which had been digitally processed or enhanced (filtering or
texture analysis). Results of the latter supervised classification were compared to the
supervised classification baseline information to determine if these digital image process
enhancements improved classification. Supervised classification was performed using
ENVI and ArcGIS 10 software.
Supervised classification unlike unsupervised classification involves the user creating
training samples from land use/land cover classes that are determined to be present in the
imagery. The training sets called region of interest (ROI) were created using ENVI
software. This training data was used throughout the supervised classifications performed
on the original imagery, texture images, PCA images, and the filtered images. The final
training sets for both study areas were determined by trial and error. A training set was
developed which had about twice as many samples, but this set did not significantly
improve classification results for either image. These larger sets did however increase
processing time, so in the interest of efficiency smaller training sets were used throughout
(Figure 11 and 12). Training sets are inherently subjective and do require the analyst to
be able to distinguish land use/land cover types.
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Figure 11 – Training sample distribution for the Ogden image
Figure 12 – Training sample distribution for Salt Lake City Image
35
Several supervised classifiers were used to evaluate the imagery using ENVI software.
The minimum distance classifier, the maximum likelihood classifier, neural net, and
SVM classifiers were examined. Each classifier provides distinct advantages and
disadvantages. The minimum distance to means classifier determines the mean of each
pre-defined class and then classifies pixels into the appropriate class by using the
Euclidean distance of the closest mean. One of the advantages to this algorithm is that it
classifies all pixels and processes very quickly. The maximum likelihood classifier
assumes that each class is normally distributed and is based on the highest probability
that a pixel will be assigned to a particular class. When classes have a multimodal
distribution this classifier will not provide optimum results. An advantage of this method
is that the classifier considers the mean and covariance of the samples. The neural net
classifier provided by ENVI software uses back propagation to determine class
assignment of pixels. An advantage of the neural net classifier is that it does not make
assumptions about the distribution of the data. The Support Vector Machine (SVM)
classifier available in the ENVI software works with any number of bands and has good
accuracy when automatically separating pixels into classes. This classifier also
maximizes the boundary between classes, which may be useful for distinguishing land
use/land cover types with similar characteristics. Another advantage of this classifier is
that it works well on imagery that has a lot of noise (ENVI 2011, Jensen 2005).
3.3.6 Image Enhancement and Texture Analysis
Digital image processing techniques were explored to determine if classification
results could be improved. Texture analysis, convolution filtering, and contrast stretching
enhance some of the spatial characteristics of the imagery. For example, contrast
36
stretching brings out more differences between light and dark areas of the imagery, and
convolution filters can enhance edges. Low pass filters can smooth out areas of noise in
an image such as the variations found throughout the field areas in the Ogden imagery,
while high pass filters make the image appear more crisp or sharp (Jensen 2005).
Convolution filtering, contrast stretching and texture filtering were used in a variety of
combinations to enhance the study areas and try to improve classification.
A two standard deviation contrast stretch was applied to both study areas to enhance
the contrast and sharpness of the imagery. Both original images lacked definition in the
light and dark areas of the image (Figure 13). The Ogden study area had a DN range of
0-235 and the Salt Lake City study area had a DN range of 0-187. All subsequent
filtering and texture analysis was performed on the stretched images.
Unstretched Stretched
Figure 13 – Unstretched images compared to contrast stretched images
37
Convolution filtering was performed on the study areas using ENVI software. High
pass filtering was used to help sharpen the imagery using a variety of kernel sizes: 3x3,
5x5, 7x7, and 11x11. Low pass filtering was applied to the imagery to smooth out noise
in the field areas. Again 3x3, 5x5, 7x7, and 11x11 kernels were examined. As the kernel
gets larger with low pass filtering, the detail becomes more generalized or blurred as this
type of filtering preserves the low frequency parts of the image. A median filter was also
examined using the previously mentioned kernel sizes. This filter has a smoothing effect
on the image but the edges remain somewhat crisper than the low pass filter. ENVI also
provides several edge enhancing filters that were used to process the original study
images. The filters used in this study were Laplacian, Roberts and Sobel. The Laplacian
filter has an editable window size whereas the Roberts and Sobel filters do not have
editable kernels or window sizes. Edge filtered images were created using the Laplacian
filter using window sizes of 3x3, 5x5, 7x7 and 11x11. The Laplacian filter was also used
in combination with the Gaussian low pass filter to try and reduce some of the noise that
results when creating the Laplacian filtered images.
Texture images were created using ENVI software and are based on the GLCM which
includes the following texture characteristics: mean, variance, homogeneity, contrast,
dissimilarity, entropy, second moment and correlation. Another set of texture images
were created using the Occurrence measures which consist of data range, mean, variance,
entropy, and skewness. Each set of texture images was created using a 3x3, 5x5, 7x7 and
11x11 processing window. The processing window measures the number of times each
gray level occurs in that particular part of the image (ENVI 2011). As the processing
window becomes larger, image detail is lost. The texture images created using the
38
GLCM are eight band images, and the texture occurrence images are five band images;
thus the dimensionality of the imagery is significantly increased by the use of texture.
These two texture images were also layer stacked with the original imagery to create
nine-band and six-band images. Additional nine-band and six-band images were also
created from these two texture images layer stacked with a filtered original image. The
resulting images were then classified using unsupervised and supervised classifiers. The
accuracy of these classifications was then compared to the classification baseline
information using an error matrix.
Principle components analysis was used to reduce the number of bands on several
composite images. In this way the dimensionality of the imagery is reduced but most of
the information in the imagery is maintained. PCA was performed on a multi-layer
image consisting of images created from variance, mean, and homogeneity texture
operators, plus the original unprocessed image. The result was a two-layer image which
incorporates information from the original image and the texture layers.
ENVI software also provides tools to perform mathematical morphology filtering
which is a non-linear process based on shape. Morphology filtering was performed on
both the original imagery and 5x5 occurrence texture images. Supervised and
unsupervised classification was then performed to determine the accuracy as compared to
the classification baseline information.
3.3.7. Object-based Image Analysis
Another digital image processing technique which was explored in this research was
object-based image analysis. Object-based image analysis is based on regions or groups
of pixels in an image rather than single pixels. Feature extraction was performed using
39
ENVI EX which provides object-based tools that utilize spatial, spectral, and textural
features. The object-based analysis provided by the ENVI software uses an edge-based
segmentation algorithm and requires only the scale level as an input parameter. The scale
levels range from 0-100 where a high scale level reduces the number of segments that are
defined, and a low scale level increases the number of segments that are defined. There
should be a balance in determining the scale level by trying to choose a scale that
delineates the image object boundaries as well as possible. This level is likely to be
different depending on the characteristics of the imagery being analyzed. ENVI provides
an interactive preview window to help determine an appropriate scale level for an image.
The preview window allows you to see what kind of effect changing the scale level of the
segmentation has on the objects of interest in the image scene before the segmentation
runs. This helps to avoid creating numerous unsuccessful segmentation images. After
the initial segmentation has been performed, image segment merging can be done. ENVI
uses the Lambda-Schedule algorithm that iteratively merges segments by using a
combination of spectral and spatial information. This step is especially helpful when an
image has been over segmented as it enables the aggregation of small segments that may
occur from image object variation (ENVI 2011). After segmentation the next step is to
find objects and classify the imagery. Objects were chosen interactively from the
segmented image and the image was then classified. ENVI EX offers either a K-means
classifier or a SVM classifier. Classification and post processing was performed using
both available OBIA classifiers. The final step before classification in the ENVI EX
feature extraction workflow is the refine results window. In this window there are
options to export vectors and smooth the results similar to using a majority filter on a
40
classified image. The process for using the feature extraction tools in ENVI EX is
designed to make the process of OBIA user friendly.
ENVI 4.8 also offers an OBIA classification method called size-constrained region
merging (SCRM). This tool is an extension that can be added to ENVI. The tool
partitions an image into reasonably homogenous polygons based on a minimum size
threshold. The output of the tool is a vector file and an image file. The vector file can be
used directly as an initial source to assist visual interpretation, and the image can be
further classified using either unsupervised or supervised classification. One of the
limitations of this extension is that there is a size limitation of 2MB for the image
(Castilla and Hay 2007). All of the layer stacked imagery exceeded the size limitation for
using this tool. SCRM was used on the original imagery, the one band dissimilarity,
mean, homogeneity, and variance texture images. The second moment, entropy, and
contrast bands were not used, as there appears to be a lot of correlation between them and
the bands that were selected. The correlation band does not have enough usable
information in it to segment it into objects. The output image was then classified using
the SVM classifier.
3.3.8. Post Processing and Automation
The classified images created from the previously mentioned digital processing
techniques and classifiers contained varying quantities of island pixels and salt and
pepper noise. There are numerous methodologies to reduce these types of areas in a
classified image. Majority and minority filtering, clump, sieve, and combine classes are
some of the commonly available tools provided in GIS and image analysis software.
These processes reduce the complexity of the classification and allow a more cohesive
41
result for further analysis. Post classification processing may also produce error in the
final imagery by smoothing and combining the wrong classes together. It is also not
practical to remove noise pixel by pixel, as there may be thousands of areas to examine.
The next step in this research was to produce a vector polygon layer that can assist in
visual interpretation of the imagery. In order to simplify the procedure of processing the
classified rasters and converting them to a vector layer that facilitates visual
interpretation, a model was developed using ArcGIS Model Builder (Figure 14). This
model allows the user to input a classified image, apply a smoothing kernel, aggregate
island pixels to a specified tolerance, convert the raster to a vector layer, and smooth and
simplify the resulting polygons. For consistency a majority filter using a 3x3 window
and aggregation using a minimum threshold of 25 was used on all the classified images
examined. The model parameters for smoothing and simplifying polygons were left open
so that adjustments can be made for different images.
Figure 14 – Post Processing ArcGIS Model
42
One of the challenges of using vector files that have been converted from raster files is
that polygons have a stepped appearance that follows pixel boundaries. This
characteristic appearance is much different from a vector file created through heads up
digitizing. A human digitizer classifies an image into recognizable objects using shape,
context, texture, shadows, etc. to help determine the boundaries of objects. This would
be very difficult if not impossible for a human digitizer to create land use/land cover
boundaries at the pixel level. This is one of the main differences between automated
classification and classification performed by visual interpretation.
The polygon smoothing and aggregation steps used in the model help to reduce some
of the stepped appearance created by the raster to vector conversion process (Figure 15).
After polygons underwent smoothing and simplification, the result appeared much closer
to results obtained through visual interpretation. This process was also an advantage if
polygons needed to be reshaped. There are fewer vertices for each polygon after
completing these operations.
Once the vector layer had been processed through the model, it was edited using a
custom toolbar in ArcGIS 10 software. The custom toolbar includes a combination of
Figure 15 – Polygon raster to vector, smoothing, and smooth and simplify
43
out of the box tools (Selection Tool and Cut Polygon Tool) and several custom tools
created using C#.net and ArcObjects. The purpose of the custom toolbar is to provide
functions to remove small islands by merging them to other neighboring pixels. It was
implemented as an “Add-in” which was easily added to the ArcGIS 10 user interface.
The toolbar consists of four custom tools: select by area, merge with smallest neighbor,
merge with largest neighbor, and merge with selected polygon. These tools are very
similar to raster majority and minority filtering except that the user has more control over
them. The tools were then used to further refine the classification using visual
interpretation. The automated classifications in essence become the starting point for the
manual digitizing effort for the study areas.
3.3.9. Accuracy Assessment
One of the most serious limitations of historical imagery is ground-truthing. The
imagery is between 33 and 52 years old, and it is likely that many of the objects in the
imagery have changed or no longer exist today. Ground-truthing was limited to visual
interpretation and image accuracy. The baseline information derived from heads up
digitizing was used as ground truth to evaluate the accuracy of the classification of both
the original images and the images where digital image processing has been used (i.e.
filtering, texture, PCA and segmentation).
An evaluation tool called a confusion matrix (or error matrix) was used between
classification baseline information and classifications after image processing
enhancement so that there is a comparison of accuracy results. To save time and labor,
only the classifications deemed best were evaluated. The confusion matrix can help to
44
visually represent classification error by use of a table, and it is used to help validate the
results of the image classification compared to the ground truth.
A stratified random sample of points was used as a sampling strategy for the accuracy
assessment. The samples for each image were created using Hawth’s sampling tools for
ArcMap. The values for the points were derived from the digitized baseline information.
Each class consisted of forty sample points except for extremely sparse areas such as
impervious surface on the Ogden image and grass on the Salt Lake City image. These
sparse areas were underrepresented by a simple random sampling strategy and as such
did not give an accurate assessment. Using the stratified sampling strategy allows each
land cover type to have a statistically significant number of points. The Ogden image
high level classification scheme used eight classes. Forty sample points were chosen for
the seven most predominant classes and thirty for the sparse class totaling 310 sample
points. The Salt Lake City high level classification scheme used five classes. Again forty
sample points were chosen for the four most predominant classes and thirty for the sparse
class totaling 190 sample points. The sample point numbers for each class in the study
areas are statistically significant and for the sake of time and effort a relatively small
number of points was chosen for each area based on the number of land use/land cover
classes determined for each area.
The Extract Values to Points tool was used to get values from the classified image and
the ground truth. These values were then combined in one column (e.g. 1-1, 1-3, etc.) to
obtain unique value pairs and then the summarize tool in ArcMap was used to obtain the
count. The values were then entered into an Excel spreadsheet which was set up to
calculate percentages of overall accuracy, producer’s accuracy, errors of omission, user’s
45
accuracy, errors of commission, single class accuracy, and the Kappa coefficient. Refer to
Appendix 1 for a sample of the error matrixes used in this research.
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CHAPTER 4: ANALYSIS RESULTS AND DISCUSSION
4.1. Manual Digitizing
Heads up digitizing of land cover classes on any type of imagery whether it is multi
spectral or panchromatic allows the user more control over the results of the
classification. The results of this method of classification in general do not require
further editing or post processing. On the other hand subjectivity of the digitizer has an
effect on the results of the classification. It is unlikely that a digitizer would be able to
classify an image exactly the same every time.
Digitizing took place in two sessions with the Ogden imagery taking approximately
five hours to complete and the Salt Lake City image took approximately three hours to
complete. The Salt Lake City image has 331 polygons compared to the Ogden image
which has 172 polygons. The Ogden image took much longer to digitize even though
there are approximately half the amount of polygons. The polygons and land cover
configurations were more complicated when considering the integration of grassland,
forest and water areas on the image. The features on the Salt Lake City image are laid
out in a grid pattern separated by wide streets so even though there were almost twice as
many polygons to digitize the process went more quickly. An important aspect of this
research was to show that digital image processing of historical panchromatic imagery
could enhance and facilitate visual interpretation of the imagery on a variety of terrains
and features.
The visual interpretation of the imagery required a zoom level of between 1:1,500 and
1:3,000 on the Salt Lake City image and 1:1,000 and 1:4,000 on the Ogden image. These
zoom levels were determined by the digitizer by how well they could see the details in
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the imagery while still being able to have some reference to the context of objects being
examined. In the experience of the digitizer a more consistent result is also achieved if
there is not a large variance in the viewing scale of the objects in the scene. If an area is
digitized at 1:3,000 and another area at 1:24,000 then the details being observed will not
be consistent throughout the study area. Digital image processing on the other hand
classifies by pixel without involving scale issues. This is a major difference in the
methodology of classification. Digitizing at varying scales is both an advantage and
disadvantage compared to digital classification. If the scale is zoomed in at the pixel
level, it was impossible to discern what the objects in the imagery were. A large variance
in scale can lead to inconsistency, but a small variance in digitizing scale can help the
digitizer to consider a feature’s relationship to surrounding objects when determining
what the object is, unlike most per pixel digital classifications. By using a small variance
in digitizing scale for land use/land cover classification of panchromatic imagery, both
detail and consistency can be maintained while the expert knowledge of relationships and
contexts of features can be utilized.
This project used relatively small areas of interest. After examining the land use/land
cover classes from the beginning of the project to its conclusion, there were areas of the
initial digitizing which on further analysis could have been refined or changed, especially
in diverse areas containing many intricate changes in the landscape. There was a
tendency to generalize areas where the land use/land cover types are fragmented. This
tendency is most notable in the Ogden image in the southern half of the image where the
forested areas are broken up by water and grasslands. The initial digitizing was not
changed to reflect new perceptions of the land class areas on the imagery. Some of these
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inconsistencies have an effect on the final accuracy of the digital classifications, as it was
apparent that at some points the digital classification was more correct than the visual
interpretation. This is a limitation of the research.
One of the major differences found in this research between the manual digitizing
classification and the digital image processing classification was the level of detail
achieved in the classifications. In the Ogden image the total number of polygons
digitized was 172 (Figure 16) and the total number of polygons digitized for the Salt
Lake City image was 331 (Figure 17). The digital classifications in comparison before
post processing yielded several thousand polygons. After post processing most digital
image classifications still exceeded the digitized baseline information but results
averaged about 500-1000 polygons. It was a difficult task to digitize very detailed areas
on the imagery. This study has shown that by utilizing digital image processing
techniques to help facilitate visual interpretation of land use/land cover classes, the
analyst can take advantage of the detail and repeatability that digital processes provide
while improving the classification accuracy using a GIS in post processing the results.
Results using visual interpretation and heads up digitizing may provide more initial
accuracy, but digital image processing lends some added consistency to the process.
4.2. Unsupervised Classification
Supervised and unsupervised classification results varied depending on the image, the
classification method, pre-processing, and post-processing. Panchromatic imagery
presents many challenges as previously mentioned in this study. The heterogeneity of
the study area also has an effect on how successful classification is. This study has
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Figure 16 – Classification using visual interpretation of the Ogden image
Figure 17 - Classification using visual interpretation of the Salt Lake City image
50
concentrated on supervised classification as this methodology gave better classification
results and was far less time consuming once the training classes were obtained.
Overall classification accuracy for unsupervised classification was low on both images
ranging from 25-40% on both study areas for the detailed classification. The more
generalized classification schemes improved the results by 8-50% (Table 3). The largest
improvement was from level 1 to level 3 using 10 spectral classes, ISODATA classifier
and the Halounova image. Running the unsupervised classification with more classes did
not generally improve accuracy except in the Salt Lake City image with the level 2 land
use/land cover classification scheme. Unsupervised classification with ten spectral
classes provided the best overall accuracy on both the Salt Lake City and Ogden images.
One of the major problems in assigning information classes to spectral classes was that
there was so much overlap between classes such as forest and dark fields, and water and
medium fields. There was no easy way to separate these areas on the raster image.
These unsupervised classifications appear very similar to each other visually (Figure 18).
The 100 class ISODATA was more difficult to assign classes to as many of the areas
were very small and appeared to be evenly divided at times between two or three
opposing classes such as water, grassland and medium fields.
Table 3 – ISODATA overall accuracy results for Ogden and Salt Lake City study areas
Land use/land cover Classification Scheme
10 Classes 25 Classes 100 Classes
Ogden – level 1 39% 40% 39% Ogden – level 2 48% 50% 47% Ogden – level 3 55% 56% 49% Salt Lake City – level 1 39% 38% 38% Salt Lake City – level 2 51% 51% 53% Salt Lake City – level 3 67% 64% 65%
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Although unsupervised classification showed low accuracy in both study areas, the
results showed some important trends in the data. In the Ogden image it was very
difficult to extract more than five classes which was an indication that land cover types
such as water, medium fields and grassland are very similar. Panchromatic imagery
would require more pre- and post-processing to achieve a more accurate classification
using eight land cover types. As the classes are aggregated into larger parent classes the
classification accuracy increased accordingly. Unsupervised classification even on a
small study area such as this was more time consuming than supervised classification and
provided somewhat unsatisfactory results.
The Salt Lake City image proved difficult in a different way in that the mixed urban
area consisted of commercial, residential and transportation areas which appear very
distinct using visual interpretation but present difficulties for digital classifiers. Urban
areas are uniquely difficult to classify on multispectral imagery, as there is such a mixture
of impervious surfaces. Black and white high spatial resolution imagery complicates this
10 spectral classes 25 spectral classes 100 spectral classes
Figure 18 – Ogden image ISODATA classifications
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situation, as there was an extreme overlap between classes, because features such as
buildings and mixed surfaces like parking lots and vegetation exist in both residential and
commercial areas making it difficult to distinguish these areas. None of the ISODATA
classifications of the Salt Lake City imagery were able to distinguish between all five,
detail level land cover types. Trees, transportation, and commercial land cover types
were the only three land cover types that could be classified from the 10, 25, and 100
spectral class ISODATA classifications (Figure 19). Many areas of overlap exist
between the commercial and transportation classes in all three unsupervised
classifications. The transportation network in this image is a very distinct linear feature
when classifying the imagery through visual interpretation, but there are many tonal
variations in the pavement which causes a great deal of confusion for most traditional
unsupervised classifiers. Grass and residential land cover types were unable to be
distinguished from commercial, transportation and trees as there was considerable tonal
overlap between these areas.
A 10 and 25 spectral class K-Means unsupervised classification was performed on the
Ogden imagery using a layer stacked image consisting of the original image and the
following texture characteristics: mean, variance, and homogeneity. Surprisingly the use
of texture did not improve the unsupervised classification using the level 1 land use/land
cover types. Overall accuracy was 25% for 10 classes and 34% for 25 classes. This is
most likely due to the fact that there was little to no distinction between the field areas as
most of them exhibit a smooth surface. Also the field areas and the water areas were
confused as well. Aggregating the classification into the more generalized classes
increased accuracy significantly in the unsupervised classification. This was particularly
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10 spectral classes 25 spectral classes 100 spectral classes
Figure 19 – Salt Lake City image ISODATA classifications
apparent in the texture image. Accuracy increased to 54% for the level 2 classification (3
land use/land cover types classification scheme) and increased to 71% for the level 3
classification (2 land use/land cover types classification scheme). The Halounova image
which consisted of texture and filtered layers did not provide improvement for the Ogden
image level 1 classification scheme using unsupervised classification, but did slightly
improve the Salt Lake City level 1 overall accuracy. Due to the poor accuracy results
using texture and unsupervised classification no further analysis was performed in either
study area.
4.3 Supervised Classification
Supervised classification of panchromatic imagery again presents many challenges.
The SVM classifier was used to perform the supervised classification as it has the ability
to process single band imagery and it provided better results. The supervised classifiers
available in ENVI are limited when using single band data as many options such as
maximum likelihood, spectral angle divergence, and neural net all require more than one
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band of data to classify the image. The classification baseline for the original Ogden
image had a poor overall accuracy of 39%.
The training data statistics showed how challenging it is to distinguish a detailed
classification on an unprocessed panchromatic image. The training areas displayed
either bimodal or multimodal histograms which in itself is a challenge for classifiers such
as maximum likelihood where the premise is that the data should have a normal
distribution (Jensen 2005, Campbell 2008). Another challenge in classifying the Ogden
image was that certain land cover types such as forest and impervious surface have a
large standard deviation. If visual interpretation is used to classify the imagery, we see
that forested areas display a lot of texture and that there is a lot of variation in the tonal
properties of this land cover. Impervious surface has the same problem in that some of
the roads are very light and others are a medium gray. The min and max values across
the training set for the individual classes also overlap. Several different training sets
were examined but this problem occurred in all sets examined. The land cover types that
had the most overlap with other classes were forest with a min of 0 and a max of 162 and
impervious surface with a min of 74 and a max of 223 (Table 4). There are no other
spectral characteristics to go from to help distinguish these types of subtleties in gray
level imagery.
The Salt Lake City image presented even more challenges in part due to the
characteristics of the image and the detailed land cover classification scheme. The land
cover types were very detailed and textured. Overlap between classes is impossible to
avoid in this type of area using a gray level image. The training data statistics again help
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Table 4 – Training sample statistics from original Ogden image
Land Cover Type Min Max Mean StDev Points
Water 129 177 151.6 13.1 1611
Forest 0 162 70.3 33.7 3246
Grassland 81 197 129.9 13.8 1944
Dark Field 53 102 78.2 12 2706
Medium Field 94 150 124.1 13.5 5056
Light Field 148 194 179 6.5 1918
Impervious Surface 74 223 170.7 28.3 732
Bare Earth 180 227 197.8 7.5 1393
to illustrate the overlap which occurs between commercial, residential and transportation
classes throughout this image (Table 5). The histograms were either bimodal or
multimodal. Although the histogram for transportation approached a normal distribution,
there were still many peaks and valleys indicating variations in gray levels in the image
for this land cover type.
Supervised classification results showed that it was very difficult to extract more than
8 classes on the Ogden image and 5 classes on the Salt Lake City image. One of the
limitations of using panchromatic imagery for land use/land cover classification is that
the DN values which make up the signature for many land use/land cover types contain a
significant amount of confusion. Real world features may be difficult to identify without
taking into account their spatial context (Hung and Wu 2005). Land use/land cover types
may need to be generalized. For example, detail like corn or wheat fields may not be
characterized using panchromatic imagery, but dark fields and light fields or cropland
may be possible. The increased accuracy achieved when aggregating land use/land cover
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types into the level 2 and level 3 classification schemes support this conclusion. Training
samples tested with a greater number of pixels increased the confusion between classes
such as water and medium fields and forest and dark and medium fields. These larger
samples had a broader min and max range for all classes.
This research showed that although it is possible to use supervised classification on
gray level imagery, it does require a significant amount of post processing of both the
classification result and the vector file. The initial supervised classification is very noisy
(Figure 20) compared to some of the results obtained using texture and object-based
analysis. All classifiers had difficulty distinguishing between transportation areas and
grass and residential areas. The most confusion occurred between residential and
commercial areas with almost no distinguishable residential areas correctly classified.
Both Minimum Distance and SVM classifiers were able to distinguish trees throughout
the image better than other land cover types. One of reasons this occurred was because
the trees training sample had a mean which was much farther away from other land cover
types.
Table 5 – Training sample statistics from original Salt Lake City image
Land Cover Type Min Max Mean StDev Points
Trees 0 133 38.8 18.4 3246
Grass 41 156 92.9 26.3 907
Transportation 60 169 108.5 14.8 9286
Residential 6 169 112 31.6 10738
Commercial 35 179 130.4 21.6 14221
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Minimum Distance Support Vector Machine
Figure 20 – Minimum distance and support vector machine classification of the Salt Lake City image
4.4. Image Enhancement and Texture Analysis
Many combinations of image filtering, texture operators, principle components
analysis and convolution filters were used to try and improve classification baseline
information. Convolution filtering alone was only moderately successful. Low pass
filtering was somewhat more successful than high pass filtering as the high pass filter
produced an excessive amount of noise and reduced the number of distinguishable land
use/land cover types in the resulting classifications. Window sizes used for filtering
ranged from 3x3 to 11x11 incremented in odd numbers for both the high pass and the low
pass filters. It appears from examining the images produced from the high pass filtering
that as the window size increases, the contrast between edges of objects become more
prominent. The low pass filtering causes the image to become smoother as the window
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size increases although the blurring along edges reduced classification accuracy and
caused most of the cropland classes to become confused when using an ISODATA
classifier. Results were better using supervised classification. A minimum distance
classifier was used on the one band filtered images. The high pass filter results were too
noisy to be useful especially with the 3x3 filter where individual features such as forest
and grassland are almost indistinguishable (Figure 21). The low pass filter caused
confusion between classes such as medium fields, grassland and water and dark fields
and forest (Figure 22). A combination median filter and Gaussian high pass filter was
used to create a two layer image using 3x3 and 11x11 window size, respectively. The
result was similar to the low pass filter but was noisier in the field and forest areas.
These combination images did not appear to improve classification results so no further
analysis was conducted on these images.
3x3 Window 11x11 Window
Figure 21 – Minimum distance classification of the Ogden image with high pass filter
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3x3 Window 11x11 Window
Figure 22 – Minimum distance classification of the Ogden image with low pass filter
Halounova (2009) combined filtering, texture, and object oriented classification to
classify panchromatic aerial photos. Similar combinations were examined in this study
using both pixel based and object-based classification for the Ogden study area. A multi
band image was created using a median filter, Gaussian high pass filter, mean texture
measure with 11x11 and 21x21 filter sizes, variance texture measure with11x11 and
21x21 filter sizes, and dissimilarity texture measure with 11x11 and 21x21 filter sizes.
This image is similar to the most successful classification used in the Halounova (2009)
study with the only differences being the variance texture measure as opposed to the
standard deviation, and the Gaussian filter did not have an option for 9 standard
deviations in the ENVI software. The 11x11 texture window was used to preserve the
smallest objects in the image. In the Ogden image most of the individual trees fit this
window although there were a couple of buildings which were smaller, but it was
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determined that the majority of small objects were better represented by the 11x11
window. The larger window sizes are used to mainly filter noise from the image. The
pixel-based supervised classifications using the level 1 classification scheme were poor
for the minimum distance and neural net classifiers at 35%. Level 2 and level 3
classification schemes increase substantially ranging from 50%-55% for level 2 and 79%
for level 3. An ISODATA unsupervised classification was also run on this image using
10 spectral classes. Class names were assigned based on the majority level 1 land
use/land cover type contained in each spectral class. Due to significant confusion
between level 1 land use/land cover types, the level 1 classification scheme was unable to
be represented by the 10 spectral class ISODATA classification. Class 1 was labeled as
medium field because this was the majority field type contained in this spectral class.
The three field types (medium, light, and dark) used in the level 1 classification were
clustered together in class 1 due to the effects of filtering and texture on this image. The
other spectral classes were assigned as follows: classes 2-3 were labeled as grassland,
and classes 4-10 were labeled as forest (figure 23). This helps to explain why the level 1
overall accuracy was so low at 32% and increases substantially to 82% for the level 3
classification scheme. The level 3 classification scheme using ISODATA unsupervised
classification was also comparable to supervised classification overall accuracy which
was 79% for both the neural net and minimum distance classifiers for this image.
A Halounova based image was also created for the Salt Lake City study area. The
resulting classifications were slightly more successful for ISODATA using 10 spectral
classes at 42%. The Halounova image classified with the SVM classifier had the best
overall accuracy for supervised classification of the Salt Lake City study area at 43%.
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Figure 23 – ISODATA 10 Spectral Classes Halounova Image
One of the shortcomings of combining the texture and filter images was that it created
edge artifacts where the size of the imagery changes with the larger processing windows.
The neural net and minimum distance classifiers handled these areas better than the
maximum likelihood and ISODATA classifiers. Object-based classification was slightly
less accurate than the pixel based classification. It was difficult to segment the image and
obtain a good representation of classes. Overall accuracy of object-based classification
for level 1 was 35%, level 2 was 52%, and level 3 was 77%. One of the biggest problems
with the object-based classification was that there was significant confusion between
water, medium fields, and dark fields. The results may be due to the study area
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characteristics or software as Halounova (2009) used E-Cognition software that appears
to have a more robust segmentation algorithm than ENVI.
The most promising image enhancements were texture operators and principle
components analysis. The occurrence texture images using moving window sizes of 3x3,
5x5, 7x7 and 11x11 were the most successful texture images. The supervised
classification baseline information on the Ogden image provided an overall accuracy
improvement of between 11-13%. The 3x3 window had the highest accuracy at 52% for
the detailed classification. Overall accuracy improved considerably as the land cover
scheme was generalized. The 3x3 occurrence measure image increased in overall
accuracy at level 2 to 65% and level 3 to 77%.
The GLCM images with eight layers were somewhat difficult to work with in that
several of the resulting layers such as entropy, second moment, dissimilarity, and
correlation use floating point values that range from 0 –1. The 3x3 window produced an
image with too much noise and presented problems for some classifiers including neural
net and maximum likelihood where the result was a few speckled areas or a completely
gray image. The GLCM images were easier to work with after the individual layers were
split and saved as 8 bit unsigned integer TIFF images. In this case the DN ranges were
all within 0-255. As previously mentioned there is still a good deal of overlap between
texture characteristics such as homogeneity and second moment. Through trial and error
combinations that included the mean, variance, and contrast bands achieved some of the
more successful classification results ranging from 50% overall accuracy to 98% overall
accuracy.
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Other image enhancements using the detailed level classification system scheme were
edge enhancement 1 band image (39% overall accuracy), 3x3 co-occurrence layers
stacked with original 9 band image (50% overall accuracy), laplacian filter 1 band image
(34% overall accuracy), closed morphology filter 1 band image (43% overall accuracy),
and a PCA 2 band image, original image plus the first principle component of the mean,
contrast and variance 3-band image (50% overall accuracy). Generally, processes that
smoothed the image rather than sharpened the image were more successful. Texture
analysis was successful in separating highly textured areas such as forest from cropland,
but had difficulty in separating medium and light fields from water areas as both are
similar in tonal range and have a relatively smooth texture.
4.5 Object-based Image Analysis
Object-based image analysis offered some very promising results using the SRCM
region based approach and the ENVI EX object-based tools. Unfortunately, the SRCM
based classification could not be verified in the same manner as the other classifications.
The software produced an image which was unable to be projected in ArcMap so visually
the images looked very good for the Salt Lake City and Ogden imagery (Figure 24), but
the extract values to points tool was unable to be used. The Ogden image produced a
shapefile output that could be lined up with the original imagery, but the shapefile option
would not work for the Salt Lake City image. In order to determine the accuracy of the
SRCM it would be necessary to assign a land use/land cover classification to each
polygon. It did not seem practical to do this since there were over 1000 polygons for the
Ogden study area. Converting the polygons to a raster and then classifying the regions
proved unsuccessful as the SRCM output was based on the number of regions and did not
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contain any spectral data. This tool produced visually very good results, but it has
limitations and it does not have consistent performance. If these limiting factors could be
improved, this tool may be more successful than the ENVI EX object-based tools.
Object-based classification using ENVI EX feature extraction proved to be the most
successful classification of the original unprocessed Salt Lake City image with an overall
accuracy of 53%. This is still relatively low, but for the detailed classification this is a
14% improvement over the best pixel based classification results. As the land cover
classification scheme was generalized, the overall accuracy improved to 64% for level 2
and 74% for level 3. The object-based classification had the advantage of using spatial
and textural relationships to break the imagery up into their respective classes.
One of the difficulties of OBIA is that there are generally no set rules for determining
the segmentation of the image. The segmenting scale and merging scale were determined
from the preview function and seeing if the objects in the scene were well represented.
Ogden image Salt Lake City image
Figure 24 – SCRM object-based segmentation images
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A low segmentation scale seemed more successful if the image was slightly over
segmented to keep the field boundaries, although this caused some over segmentation in
the forest areas. The merging step after the initial segmentation helped to eliminate some
of the extra segments caused by keeping the field boundaries. For the Ogden project area
a scale of 45 and a merging scale of 75 were used. Two types of classification are
available in the ENVI software to classify the imagery, rule based and example based.
This study uses example based classifications, as it is the simplest and most
straightforward approach.
The Ogden image produced a fairly successful object-based classification on a PCA
texture image with an overall accuracy of 50%. The generalized classifications yielded
an overall accuracy of 61% for level 2 and 77% for level 3. The object-based
classification on the Ogden image did a good job of distinguishing large areas in the
imagery, but there was difficulty distinguishing between the medium and light field
classes. This had the effect of lowering the overall accuracy of the level 1 classification
scheme. The object-based classification for both images had less noise than other
classification methods. Since the object-based classifications were the most successful
and produced results with less noise than the per pixel supervised and unsupervised
classifications, they were used to test the post-processing system explained in the next
section. A per pixel classification based on the methods used by Halounova (2009) was
used for comparison.
4.6 Post Processing and Automation
Post processing the image classification rasters was an important step in order to use
these results to facilitate visual interpretation. Supervised classification produced better
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results in general than unsupervised classification, but there is also a practicality aspect to
the usefulness of digital image processing to aid an image analyst. It became apparent
after much trial and error using different classifiers and processing methods that there
was not one single method or process that was ideal for classifying panchromatic
imagery. A good base classification makes the job of visual interpretation easier, but it is
not entirely necessary if a good system is put into place to utilize the digital image
classification. The post processing model and the polygon editing toolbar provide a way
to give the user a base to start from even if it is less than ideal. This flexibility in the
system was an important consideration due to the variability that the user may encounter
between land use/land cover types for different projects and variability in general image
quality.
A post-processing system was designed to improve and clean up final land use/land
cover results obtained from the classified images. The system consists of 3 steps. Step 1
is to run the post-processing ArcGIS Model (Figure 15), which converts the image into a
polygon layer. Step 2 is to interactively edit the resulting polygon layer using the custom
vector editing toolbar. Finally step 3 is to perform an accuracy assessment to determine
the success of the results.
Heads up digitizing is often performed using GIS software with the output being a
land cover classification vector layer. A vector layer provides a more flexible medium
for visual interpretation as each member of a class is represented as a feature rather than a
non-contiguous area. Vertices and attributes are easily edited in vector format. The
custom polygon editing tool was used to delete any remaining noise and to reduce small
island pixels throughout the classification. Refining the object-based classification
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through the post processing system mentioned above took between 3 and 5 additional
hours for each image which is similar in time to digitizing. Depending on the project an
overall quick cleanup could be performed in about an hour and would likely add
substantial accuracy to the classification. Most of the system involved using the merge
with largest neighbor tool and the merge with selected polygon tool. The merge with
selected polygon tool was used most often, as occasionally what appears to be the largest
neighbor was difficult to discern.
The object-based classification of the study areas provided a good base to work with
as there were fewer areas of noise and island pixels. The post processing system was
very successful at improving the overall accuracy of the classification for both images
(Figures 25 and 26). For the Ogden image using the level 1 scheme, overall accuracy
was increased from 56% to 85% with a Kappa index of 83% indicating a substantial
agreement between the classification and the reference data. The level 2 scheme’s
overall accuracy was increased from 80% to 93% with a Kappa Index of 89%, and the
level 3 scheme produced an overall accuracy of 98% improved from 94% with a Kappa
index of 97%. The Salt Lake City image also yielded substantial accuracy improvements
from 53% to 72% (65% Kappa index), from 63% to 76% (65% Kappa index), and from
71% to 82% accuracy (63% Kappa index) respectively in decreasing level of land cover
classification scheme.
A per pixel classification based on Halounova (2009) methodology using texture and
filtering was also used to determine whether the post processing system could improve a
classification with an initially lower overall accuracy. Before using the post processing
system overall accuracy of this image was 35% (Figure 27). The post processing system
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analysis of the Halounova (2009) based image took five hours compared to three hours
for the object-based analysis. The object of this study was not to try to perfect the
classification but to effectively and efficiently improve the results in terms of time
consumption. After post processing the overall accuracy for the level 1 classification
scheme was improved from 35% to 75% (72% Kappa index). Level 2 was improved
from 50% to 87% and level 3 from 79% to 98%. The improvements in level 2 and level
3 are similar to the gains seen for object-based classification. This is likely due to the
good separation between cropland and all other classes on this image. This study has
shown that the post processing system can improve a poor initial classification
substantially.
Figure 25 – Ogden object-based classification image and post processing system vectors
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Figure 26 – Salt Lake City object-based classification image and post processing system vectors
Figure 27 – Ogden pixel based classification and post processing system vectors
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4.7 Classification Accuracy and Results
This research showed that classification accuracy for panchromatic imagery could be
variable depending on the image and the classification scheme. One of the biggest
factors that affected classification accuracy was the number of land cover categories.
Some projects require a high level of classification accuracy. This research has also
shown that by using digital image processing techniques and a system of post processing
on the results, it is possible to achieve a high level of accuracy (over 80%) for a project
requiring detailed land use/land cover classification. The Kappa index used to determine
how much of the accuracy may be due to chance was quite variable throughout this study
ranging from 25% to upwards of 97%. The higher the number the more agreement there
was between the reference data (digitized baseline information) and the classification
image. It is notable that the Kappa index was extremely high after the post-processing
system was applied. This high Kappa value shows that the agreement between the
ground truth classification and the post-processing results increased, and positive results
had less to do with chance alone. Overall accuracy does provide a benchmark to
determine in general how the classifications compared to each other. Please refer to
Appendix 1 for a sample of error matrixes used for this research.
Classification results between supervised and unsupervised classification were very
similar on the original panchromatic imagery for the level 1 classification scheme.
Overall accuracy for unsupervised classification ranged from 39%-40% on the Ogden
image while the supervised classification baseline information overall accuracy was 39%.
The overall accuracy of the Salt Lake City image unsupervised classification ranged from
38% to 39% and the supervised classification baseline was 38%. Both study areas had
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very low overall accuracy. There was not one classifier either supervised or unsupervised
which stood out as being significantly better than another for the classification of the
original panchromatic image in either study area. Please refer to tables 6 and 7 for a
comparison of classification results for the Ogden and Salt Lake City study areas.
From a user’s perspective supervised classification provides more usable and easier to
work with results given that identified classes are returned. This in turn offers a better
base that can be run through the post processing system proposed in this research.
Supervised classification takes into account the classes that the user has determined
beforehand and does not require further identification of classes. Identifying classes and
labeling them is much more time consuming in the unsupervised classifications (Tables 6
and 7). Unsupervised classification results were useful for seeing how classes were
grouped in the imagery and were a good illustration of why the more generalized
classifications had higher overall accuracy. After assigning labels to classes from the
unsupervised classification due to confusion and combining classes, there were only 4-7
level 1 land use/land cover classes that could be categorized in the Ogden study area
depending on the number of spectral classes chosen and whether texture/filtering was
used. The object-based classifications on the other hand had the most promising results
in this study for use in the post processing system, although the system works to improve
even an unsatisfactory classification base.
When specific land use/land cover types were examined, the unsupervised
classification baseline was very good at classifying the dark, medium, and light fields but
very poor at classifying water, forest, grassland, impervious surface and bare earth.
Supervised classification worked well on dark fields, water, and light fields. In both
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unsupervised and supervised classifications the dark fields’ user’s accuracy was
consistently the highest, ranging from 60%-92.5%. User’s accuracy for impervious
surface and bare earth was consistently low for both methodologies ranging from 0%-
35%. See table 8 for a comparison of the user’s accuracy for individual land use/land
cover results for the Ogden study area.
Table 6 Ogden image overall accuracy and level 1 completion time
Classification method Time Classification scheme Level 1 Level 1 Level 2 Level 3 ISODATA 10 spectral classes - original imagery 17 m 39% 48% 55% ISODATA 25 spectral classes - original imagery 22 m 40% 50% 56% ISODATA 100 spectral classes - original imagery 57 m 39% 47% 49% ISODATA 10 spectral classes – Halounova image 15 m 32% 51% 82% Unsupervised K-Means 10 spectral classes– original, texture (mean, variance, homogeneity) 12 m 25% 54% 71%
SVM - original imagery 6 m 39% 53% 60%
Minimum Distance – Halounova 9 layer 1 m 35% 50% 79% Neural Net – Halounova 9 layer 30 m 35% 55% 79% SVM - 3x3 texture occurrence measures 8 m 52% 65% 77% SVM – 5x5 texture occurrence measures 8 m 51% 65% 78% SVM – 7x7 texture occurrence measures 8 m 50% 63% 77% SVM – 11 x 11 texture occurrence measures 8 m 46% 61% 80% SVM – 3x3 Co-occurrence measures and original 8 m 50% 62% 76% SVM – PCA, original, 3x3 texture (mean, homogeneity) 7 m 35% 53% 62% SVM – PCA, original, 3x3 texture (mean, contrast, variance) 7 m 50% 61% 75%
SVM – 5x5 edge enhance 6 m 39% 50% 60% SVM – Laplacian filter add back 80% original 7 m 34% 46% 54% SVM – closed morphology filter 7 m 43% 53% 62%
SVM Object-based – PCA, original, 3x3 texture (mean, contrast, and variance) 15 m 56% 80% 94%
Neural Net – Halounova 9 layer post processing system 5 h 75% 87% 98% SVM – 11x11 texture occurrence measures post processing system 1h 27m 61% 72% 92%
SVM Object-based post processing system – PCA, original, texture (mean, contrast, and variance) 3 h 85% 93% 98%
*Please note times were only recorded for level 1 classification results as level 2 and level 3 results were obtained by aggregating the land use/land cover types
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Adding texture layers offered fairly significant improvement on the overall accuracy
using the level 1 classification scheme for the Ogden study area. Only slight
improvement was seen in the Salt Lake City image. Overall accuracy for the Ogden
image using the occurrence texture measures ranged from 46%-52% and the Salt Lake
City image ranged from 38%-42%. The size of the texture window for the Ogden image
did not significantly impact the overall accuracy of the image until the window size
reached 11x11. At this point overall accuracy started to drop more quickly than between
the smaller windows (52%-46%). The overall range of accuracy is still fairly small at
6%. The texture window size for level 2 and level 3 classification schemes had even less
impact on overall accuracy with a range of only 3%-4% difference. The texture window
size had the opposite effect on the Salt Lake City image. Overall accuracy increased very
gradually as the texture window got larger (38%-42%). This helps to show the
differences in the two study areas. The Ogden study area is characterized by more
homogenous features such as forest and cropland whereas the Salt Lake City study area
has diverse land use/land cover classes such as commercial and residential which are
more heterogeneous in content consisting of a variety of natural and manmade materials
in widely varying shapes and sizes.
The use of texture also had an effect on the various land use/land cover classes in both
study areas. Texture increased the user’s accuracy of features such as forest, grassland,
dark fields, and impervious surface while water, bare earth, and medium fields decreased.
Light fields had mixed results either decreasing or increasing depending on the texture
window size. The Salt Lake City land use/land cover classes were also affected by
texture. Texture measures increased the user’s accuracy of grass, and residential classes
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Table 7 Salt Lake City image overall accuracy and level 1 completion time Classification method Time Classification scheme Level 1 Level 1 Level 2 Level 3 ISODATA 10 spectral classes - original imagery 10m 39% 51% 67% ISODATA 25 spectral classes - original imagery 22m 38% 51% 64% ISODATA 100 spectral classes - original imagery 46m 38% 53% 65% ISODATA 10 spectral classes – Halounova image 8m 42% 60% 67% Minimum Distance Classifier – original imagery 1m 38% 54% 63% SVM – original imagery 5m 38% 45% 58% Maximum Likelihood – 5x5 texture occurrence, saturation stretch
1m 42% 66% 63%
Minimum Distance Classifier – Halounova image 1m 42% 60% 65% Neural Net – Halounova image 40m 39% 48% 54% Neural Net – 5x5 texture occurrence 14m 37% 46% 57% SVM – 3x3 texture occurrence measures 15m 38% 45% 55% SVM – 5x5 texture occurrence measures 15m 41% 48% 56% SVM – 11x11 occurrence measures 16m 42% 52% 58% SVM – 5x5 texture occurrence measures, saturation Stretch
16m 41% 66% 75%
SVM – Halounova Image 30m 43% 52% 58% SVM Object-based – original imagery 10m 53% 64% 71% SVM Object-based Post Processing System – original imagery
3 h 72% 76% 82%
*Please note times were only recorded for level 1 classification results as level 2 and level 3 results were obtained by aggregating the land use/land cover types and decreased the user’s accuracy of trees and commercial classes. The transportation
class user’s accuracy increased or decreased depending on the texture window size. The
individual land use/land cover classes in both study areas were affected by texture
window size depending on how homogenous or heterogeneous the features were. Classes
that were more homogenous like water, medium fields, and bare earth decreased as
window sizes increased. Classes that were more heterogeneous like forest, grassland, and
impervious surface increased in accuracy as window sizes increased up to the 11x11
window size where accuracy then started to decline again. This demonstrated that there
was not a single texture processing window that was able to effectively characterize all
the textures for either study area (Tables 8 and 9).
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Object-based classification had the highest overall accuracy for both study areas. For
the level 1 classification scheme the Ogden study area had an overall accuracy of 56%,
which was an improvement over pixel based classifications of 4%-31%. The level 2 and
level 3 classification schemes showed an even greater improvement of 15%-33% and
12%-45% respectively. The level 3 classification scheme was the only classification
which attained over 90% accuracy without post-processing. This indicated that the
object-based classification was good at distinguishing cropland from other land use/land
cover classes. This is due to the spectral homogeneity of dark and medium fields and the
shape homogeneity of the fields. User’s accuracy for individual land use/land cover
classes in the level 1 classification scheme varied for object-based classification. Object-
based classification showed significant improvement over pixel based classification for
water, dark fields, impervious surface, and bare earth from 10%-72.5%. Forest,
grassland, and medium fields varied depending on the classifier and texture properties.
Light field was the only feature that had a decrease in accuracy ranging from 27.5%-75%
depending on whether texture or supervised, unsupervised classification was initially
used. This decrease in accuracy was most likely due to confusion between light fields
and medium fields that could be caused by some spectral similarities, and the close
proximity of medium and light fields in the Ogden study area.
Object-based classification was also generally more successful than pixel based
classification for the Salt Lake City study area. The overall accuracy for the level 1
classification scheme was 53% which was a 10%-16% improvement. Level 2 and level 3
classification schemes also showed improvement from 4%-17% over most of the pixel
based classifiers except the 5x5 texture processing window using the occurrence
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Table 8 – User’s accuracies for individual land use/land cover types Ogden study area
Classification method Land use/land cover
Water Forest Grassland Dark Fields
Medium Fields
Light Fields
Impervious Surface
Bare Earth
ISODATA 10 spectral classes – original imagery 0% 27.5% 17.5% 92.5% 62.5% 67.5% 0% 35%
ISODATA 25 spectral classes – original imagery 15% 30% 7.5% 87.5% 62.5% 90% 0% 17.5%
ISODATA 100 spectral classes – original imagery 0% 30% 5% 85% 77.5% 92.5% 0% 15%
ISODATA 10 spectral classes – Halounova image 0% 90% 30% 72.5% 52.5% 0% 0% 0%
Unsupervised K-Means 10 spectral classes– original, texture (mean, variance, homogeneity)
0% 27.5% 17.5% 92.5% 62.5% 67.5% 0% 35%
SVM – original imagery 62.5% 32.5% 0% 77.5% 45% 65% 0% 22.5%
Minimum distance – Halounova image 13% 38% 38% 60% 25% 63% 47% 0%
Neural Net – Halounova image 7.5% 62.5% 17.5% 60% 37.5% 42.5% 53.3% 7.5%
SVM – 3x3 Texture occurrence measures 52.5% 75% 47.5% 82.5% 35% 62.5% 36.7% 17.5%
SVM – 5x5 Texture occurrence measure 45% 77.5% 55% 80% 22.5% 72.5% 46.7% 7.5%
SVM – 7x7 Texture occurrence measures 35% 77.5% 52.5% 82.5% 17.5% 67.5% 56.7% 10%
SVM – 11x11 occurrence measures 32.5% 67.5% 35% 80% 20% 65% 63.3% 7.5%
SVM – 3x3 Co-occurrence measures and original 47.5% 72.5% 32.5% 80% 32.5% 70% 40% 20%
SVM – PCA, original, 3x3 texture (mean, homogeneity) 2.5% 67.5% 12.5% 65% 57.5% 52.5% 0% 17.5%
SVM – PCA, original, 3x3 texture (mean, contrast, variance) 62.5% 77.5% 32.5% 82.5% 30% 47.5% 43.3% 20%
SVM – 5x5 edge enhance 80% 37.5% 0% 67.5% 52.5% 47.5% 0% 17.5%
SVM – Laplacian filter add back 80% original 22.5% 22.5% 0% 67.5% 55% 67.5% 6.7% 25%
SVM – closed morphology filter 70% 37.5% 20% 82.5% 47.5% 62.5% 0% 15%
SVM Object-based – PCA, original, 3x3 texture (mean, contrast, and variance) 80% 70% 12.5% 92.5% 57.5% 15% 73.3% 50%
Neural Net – Halounova image post processing system 72.5% 72.5% 45% 97.5% 97.5% 85% 90% 47.5%
SVM – 11x11 texture occurrence measures post processing system 45% 70% 57.5% 85% 67.5% 92.5% 43.3% 20%
SVM Object-based post processing system – PCA, original, 3x3 texture (mean, contrast, variance)
90% 85% 55% 95% 95% 85% 96.7% 80%
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Table 9 – User’s accuracies for individual land use/land cover types Salt Lake City study area
Classification method Land use/land cover
Trees Grass Transportation Residential Commercial
ISODATA 10 spectral classes – original imagery 55% 0% 60% 0% 70%
ISODATA 25 spectral classes - original imagery 55% 0% 50% 0% 75%
ISODATA 100 spectral classes – original imagery 57.5% 0% 45% 0% 80%
ISODATA 10 spectral classes – Halounova image 80% 0% 65% 10% 45%
Minimum Distance Classifier – original imagery 42.5% 46.7% 20% 17.5% 67.5%
SVM – original imagery 37.5% 0% 50% 20% 72.5%
Maximum Likelihood – 5x5 texture occurrence, saturation
stretch 30% 53.3% 42.5% 37.5% 50%
Minimum Distance Classifier – Halounova image 22.5% 50% 60% 25% 52.5%
Neural Net – Halounova image 20% 0% 55% 80% 32.5%
Neural Net – 5x5 texture occurrence 27.5% 0% 50% 47.5% 52.5%
SVM – 3x3 Texture occurrence measures 27.5% 0% 47.5% 40% 65%
SVM – 5x5 Texture occurrence measure 27.5% 3.3% 42.5% 72.7% 60%
SVM – 11x11 occurrence measures 20% 10% 57.5% 60% 52.5%
SVM – 5x5 texture occurrence measures, saturation stretch 22.5% 36.7% 57.5% 32.5% 52.5%
SVM – Halounova Image 25% 0% 52.5% 70% 57.5%
SVM Object-based – 5x5 occurrence measures 37.5% 60% 50% 50% 70%
SVM Object-based Post Processing System – original imagery 40% 66.7% 82.5% 72.5% 97.5%
measures which was more accurate by 2%-4%. The Salt Lake City study area showed
slightly less improvement than the Ogden study area which was likely due to the
variation of objects on the ground in the commercial and residential land use/land cover
classes. Please see table 10 for a comparison of overall accuracy ranges for the level 1
classification scheme and the range of improvement gained for levels 2 and 3 for each
classification group.
User’s accuracies for individual land use/land cover types using object-based
classification in the Salt Lake City study area were mixed and in general did not show
asmuch improvement as the Ogden study area. Grass was the only feature which showed
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significant improvement between 14%-60% for object-based classification. All other
land use/land cover classes had mixed results depending on which classification strategy
it was compared to. For example, the commercial class showed an increase in accuracy
when compared to texture and filtering but showed a decrease in accuracy when
compared to the unsupervised classification group of images. Transportation, residential,
and commercial land use/land cover classes showed a similar trend. The commercial,
residential, and transportation areas are very heterogeneous spectrally and even though
spatially they are fairly homogenous, the spectral variations seemed to affect the accuracy
of these classes. Object-based classification appeared to handle the more homogenous
classes like trees and grass better than the other land use/land cover types. This study has
shown that object-based classification is a very promising technique for high resolution
panchromatic aerial imagery. This methodology was more successful in the Ogden study
area that is characterized by more distinct regions of land use/land cover types than the
urban area that characterized the Salt Lake City study area.
The post processing system provided the most accurate results for both study areas.
Overall accuracy for level 1 classification ranged from 61%-85% with improvement
Table 10 – Overall accuracy ranges for classification groups
Classification group Level 1 accuracy
range Level 2 accuracy range
Level 3 accuracy range
Range of overall improvement
Unsupervised classifications 25-42% 47-60% 49-82% 5-57% Baseline classifications 38-39% 45-54% 58-63% 6-25% Texture/Filtering/PCA classifications
34-52% 45-66% 54-80% 2-46%
Object-based classifications 53-56% 64-80% 71-94% 8-41% Post processing system classifications
61-85% 72-93% 82-98% 8-37%
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between 8%-37% for levels 2 and 3. The object-based classification for the Ogden study
area has the highest overall accuracy for the level 1 classification scheme at 85%. All
land use/land cover categories for both study areas showed significant improvement.
Notable areas of improvement included water, bare earth, impervious surface,
transportation, and grass.
Effectiveness and efficiency of the post processing system were two of the primary
goals of this research. This research has shown that the system effectively improves
classification accuracy. The post processing system is an efficient method based on user
interaction, but it did add significant time to the image classification. Image-processing
software can automatically classify, post-process, and produce a usable land use/land
cover layer in minutes. In comparison the post processing system in this study added
several hours onto this process in order to gain a more accurate land use/land cover layer
for use in a GIS. Since the system is based on user interaction, the time can be variable
depending on how much detail or what accuracy level may be required for a project. If a
generalized classification using three or less classes and accuracy between 70% and 80%
would be appropriate for a project, then the post processing system can be completed in
two to three hours. This is a modest time-savings compared to manual digitizing,
although the resulting classifications contained far more detail than the manually
digitized results. The Ogden image took approximately five hours to digitize and the Salt
Lake City image about three hours. Performing automated classification using image
processing software is the most efficient method to produce a land use/land cover
classification layer in this study. It took about 1 minute using a minimum distance
classifier and about 30 minutes using a neural net classifier, but the accuracy of the
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resulting classification was often low. The post processing system added both
consistency and accuracy to the classifications but did add several hours to the process.
The object-based classifications provided a better starting base but added about three to
four hours to get a fairly accurate final land use/land cover classification. In contrast a
poor starting base with an overall accuracy of 35% took about five hours to achieve an
improved overall accuracy of 75%.
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CHAPTER 5: CONCLUSION
5.1 Limitations of the Research
The point of this research was to facilitate visual interpretation of panchromatic
historical imagery by using digital image processing. Imagery is variable in both quality
and scene characteristics so that what is successful on one dataset may not be successful
on other datasets. Another limit to the methods in this research was the sheer number of
digital image processing techniques which could be used on the imagery. Thousands of
combinations and settings were available to process the imagery, but due to time
limitations relatively few were feasible to study. Availability of algorithms and software
capabilities are also a limitation as commercial software provides relatively few choices
compared to open source programs available from a variety of sources. Another
limitation was knowledge of advanced algorithms and programming to determine new
classification algorithms that may benefit single band image classification. It was
determined that using readily available software and the capabilities they offer was more
useful if the research is to be applied in a practical manner for the study of the Farm
Service Agency’s historical aerial photos. The overall research goals were achieved in
that it was shown that by combining digital image processing with a system of post-
processing that allows user interaction, image classification accuracy can be improved by
at least 20%.
5.2 Potential Future Developments
The current vector tools created for this project could be expanded to include more
editing and reporting capabilities. It would be beneficial to include an undo button, a
field editing option and real time statistical reporting. Also including more of the
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advanced ENVI functionality through the use of IDL and Python programming into the
ArcGIS interface could expand the options for supervised classification including the
SVM, SCRM Object-based, neural net, and the self organizing map. The process of
classification is more streamlined for the user if only one software package is required to
perform all the steps.
Through trial and error many filters and combinations of texture images were assessed
for their usefulness in classification of panchromatic imagery. There are almost endless
combinations using texture measures, filtering, PCA, and contrast stretching. A
relatively small number of these combinations were assessed for this research. This
aspect of the study lends itself to further research in the future as there may be other
combinations which could be more successful.
Further research is also possible in the area of classification algorithms designed
specifically for panchromatic imagery. Most classifiers are optimally designed to take
advantage of multi-spectral bands. As computer-processing power continues to advance,
it may be possible to develop new algorithms that have the capability of better
distinguishing distinct land cover classes with limited spectral information. More
integration between texture measures and object-based segmentation and feature
extraction needs to be explored further. These digital techniques show obvious
advantages and improvements in classifying panchromatic imagery, but results could still
be improved especially in urban areas.
One of the findings in this research was that certain classification techniques are more
successful than others on particular land use/land cover types. This suggests that it may
be possible to achieve high classification accuracy using a hybrid approach where each
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land use/land cover type is classified separately using a technique which yields a high
accuracy for that category. For example, in the Ogden study area a 10 spectral class
ISODATA unsupervised classification yielded 92.5% accuracy for dark fields but low
accuracy for water (0%) and impervious surface (0%). Through masking and a process
of elimination water and impervious surface could be classified using a more successful
technique such as object-based classification where accuracy was higher 80% and 50%
respectively. Each land use/land cover type in the image could then be successively
classified using a high accuracy technique. This would likely be a multi-process
classification but the possibility of less post processing and higher accuracy has a lot of
potential for future study.
As historical imagery becomes more readily available to the public through
technology such as web-based image services, classification tools for use in these
services would have wide appeal to government and the remote sensing community. The
ability to make use of historical data to study long-term land use trends is one of the most
important aspects of this research. Black and white aerial photography is an
underutilized resource at present, but developing more tools to access the information
contained in the imagery will broaden its appeal to the remote sensing community.
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APPENDIX 1: ERROR MATRIX TABLES Ogden unsupervised classification of original unprocessed image
Pixels Classification Data Unsupervised Ogden Data 10 Spectral Classes Reference Data
Water Forest Grassland Dark Fields
Medium Fields
Light Fields
Impervious Surface
Bare Earth
Row Total
Producer’s Accuracy
Errors of Omission
Water 0 0 0 0 0 0 0 0 0 0% 100% Forest 3 11 1 0 0 0 1 1 17 64.7% 35.3% Grassland 3 7 7 2 3 0 10 2 34 20.6% 79.4%
Dark Fields 0 12 8 37 9 0 2 6 74 50% 50% Medium Fields 20 8 20 0 25 2 8 8 91 27.5% 72.5%
Light Fields 12 2 2 1 3 27 6 9 62 43.5% 56.5% Impervious Surface 0 0 0 0 0 0 0 0 0 0 100%
Bare Earth 2 0 2 0 0 11 3 14 32 43.8% 56.3% Column Total 40 40 40 40 40 40 30 40 121
User’s Accuracy 0% 27.5% 17.5% 92.5% 62.5% 67.5% 0% 35%
Errors of Commission 100% 72.5% 82.5% 7.5% 37.5% 32.5% 100% 65%
Overall Accuracy 39% Kappa Index 30%
Pixels Classification Data Unsupervised Ogden Data 25 Spectral Classes Reference Data
Water Forest Grassland Dark Fields
Medium Fields
Light Fields
Impervious Surface
Bare Earth
Row Total
Producer’s Accuracy
Errors of Omission
Water 6 0 3 0 2 0 2 3 16 37.5% 62.5% Forest 3 12 1 0 0 0 1 1 18 66.6% 33.4% Grassland 1 5 3 2 2 0 8 1 22 13.6% 86.4% Dark Fields 0 9 8 35 6 0 0 5 63 55.5% 44.4% Medium Fields 18 11 21 1 25 4 10 9 99 25.2% 74.7%
Light Fields 11 2 3 1 3 36 6 14 76 47.3% 52.6% Impervious Surface 0 0 0 0 0 0 0 0 0 0% 100%
Bare Earth 1 1 1 1 2 0 3 7 16 43.7% 56.3% Column Total 40 40 40 40 40 40 30 40 124
User’s Accuracy 15% 30% 7.5% 87.5% 62.5% 90% 0% 17.5%
Errors of Commission 85% 70% 92.5% 12.5% 37.5% 10% 100% 82.5%
Overall Accuracy 40% Kappa Index 31%
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Pixels Classification Data Unsupervised Ogden Data 100 Spectral Classes Reference Data
Water Forest Grassland Dark Fields
Medium Fields
Light Fields
Impervious Surface
Bare Earth
Row Total
Producer’s Accuracy
Errors of Omission
Water 0 0 0 0 0 0 0 1 1 0% 100% Forest 3 12 1 1 0 0 1 1 19 63.2% 36.8% Grassland 0 1 2 1 0 0 1 0 5 40% 60% Dark Fields 0 10 8 34 6 0 0 5 63 54% 46% Medium Fields 24 15 25 3 31 3 19 10 130 23.8% 76.2%
Light Fields 12 2 3 1 3 37 6 17 81 45.7% 54.3% Impervious Surface 0 0 0 0 0 0 0 0 0 0% 100%
Bare Earth 1 0 1 0 0 0 3 6 11 54.5% 45.5% Column Total 40 40 40 40 40 40 30 40 122
User’s Accuracy 0% 30% 5% 85% 77.5% 92.5% 0% 15%
Errors of Commission 100% 70% 95% 15% 22.5% 7.5% 100% 85%
Overall Accuracy 39% Kappa Index 30%
Pixels Classification Data Unsupervised Ogden Data 10 Spectral Classes Halounova image Reference Data
Water Forest Grassland Dark Fields
Medium Fields
Light Fields
Impervious Surface
Bare Earth
Row Total
Producer’s Accuracy
Errors of Omission
Water 0 0 0 0 0 0 0 0 0 0% 100% Forest 15 36 24 4 10 6 20 26 141 25.5% 74.5% Grassland 12 4 12 7 9 3 10 13 70 17.1% 82.9% Dark Fields 0 0 0 29 0 0 0 0 29 100% 0% Medium Fields 13 0 4 0 21 31 0 1 70 30% 70%
Light Fields 0 0 0 0 0 0 0 0 0 0% 100% Impervious Surface 0 0 0 0 0 0 0 0 0 0% 100%
Bare Earth 0 0 0 0 0 0 0 0 0 0% 100% Column Total 40 40 40 40 40 40 30 40 98
User’s Accuracy 0% 90% 30% 72.5% 52.5% 0% 0% 0%
Errors of Commission 100% 10% 70% 27.5% 47.5% 100% 100% 100%
Overall Accuracy 32% Kappa Index 21%
Ogden level 1 classification scheme
Pixels Classification Data Supervised Ogden Original Unprocessed SVM Reference Data
Water Forest Grassland Dark Fields
Medium Fields
Light Fields
Impervious Surface
Bare Earth
Row Total
Producer’s Accuracy
Errors of Omission
Water 25 5 10 0 16 14 11 12 93 26.9% 73.1% Forest 3 13 3 1 1 0 1 1 23 56.5% 43.5% Grassland 0 0 0 0 0 0 0 0 0 0% 100% Dark Fields 0 8 4 31 2 0 0 5 50 62% 38% Medium Fields 5 13 19 7 18 0 13 4 79 22.8% 77.2%
Light Fields 6 1 2 1 3 26 2 9 50 52% 48% Impervious Surface 0 0 0 0 0 0 0 0 0 0% 100%
Bare Earth 1 0 2 0 0 0 3 9 15 60% 40% Column Total 40 40 40 40 40 40 30 40 122
User’s Accuracy 62.5% 32.5% 0% 77.5% 45% 65% 0% 22.5%
Errors of Commission 37.5% 67.5% 100% 22.5% 55% 35% 100% 77.5%
Overall Accuracy 39% Kappa Index 30%
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Pixels Classification Data Supervised 3x3 Occurrence SVM Reference Data
Water Forest Grassland Dark Fields
Medium Fields
Light Fields
Impervious Surface
Bare Earth
Row Total
Producer’s Accuracy
Errors of Omission
Water 21 0 1 0 11 11 1 0 45 46.7% 53.3% Forest 4 30 9 3 2 0 2 6 56 53.6% 46.4% Grassland 3 5 19 1 8 2 10 3 51 37.3% 62.7% Dark Fields 0 0 1 33 1 0 0 1 36 91.7% 8.3% Medium Fields 3 1 4 2 14 0 4 0 28 50% 50%
Light Fields 6 0 2 0 3 25 0 8 44 56.8% 43.2% Impervious Surface 3 4 4 1 1 2 11 15 41 26.8% 73.2%
Bare Earth 0 0 0 0 0 0 2 7 9 77.8% 22.2% Column Total 40 40 40 40 40 40 30 40 160
User’s Accuracy 52.5% 75% 47.5% 82.5% 35% 62..5% 36.7% 17.5%
Errors of Commission 47.5% 25% 52.5% 17.5% 65% 37.5% 63.3% 82.5%
Overall Accuracy 52% Kappa Index 45%
Pixels Classification Data Supervised 5x5 Occurrence SVM Reference Data
Water Forest Grassland Dark Fields
Medium Fields
Light Fields
Impervious Surface
Bare Earth
Row Total
Producer’s Accuracy
Errors of Omission
Water 18 0 0 0 10 8 1 0 37 48.6% 51.4% Forest 4 31 6 3 2 0 2 4 52 59.6% 40.4% Grassland 7 4 22 1 15 1 9 4 63 34.9% 65.1% Dark Fields 1 0 2 32 0 0 0 1 36 88.9% 11.1% Medium Fields 1 0 1 2 9 0 4 0 17 52.9% 47.1%
Light Fields 5 0 3 0 2 29 0 5 44 65.9% 34.1% Impervious Surface 4 5 6 2 2 2 14 23 58 24.1% 75.9%
Bare Earth 0 0 0 0 0 0 0 3 3 100% 0% Column Total 40 40 40 40 40 40 30 40 158
User’s Accuracy 45% 77.5% 55% 80% 22.5% 72.5% 46.7% 7.5%
Errors of Commission 55% 22.5% 45% 20% 77.5% 27.5% 53.3% 92.5%
Overall Accuracy 51% Kappa Index 44%
Pixels Classification Data Supervised Minimum Distance Classifier Halounova 9 layer - original, texture, filters Reference Data
Water Forest Grassland Dark Fields
Medium Fields
Light Fields
Impervious Surface
Bare Earth
Row Total
Producer’s Accuracy
Errors of Omission
Water 5 0 0 0 10 5 0 0 20 25% 75% Forest 7 15 12 2 5 4 6 9 60 25% 75% Grassland 12 4 15 10 9 2 10 14 76 19.7% 80.3% Dark Fields 0 0 1 24 0 0 0 0 25 96% 4% Medium Fields 1 0 1 2 10 0 0 0 14 71.4% 28.6%
Light Fields 3 0 0 0 0 25 0 1 29 86.2% 13.8% Impervious Surface 8 21 11 2 5 2 14 16 79 17.7% 82.3%
Bare Earth 4 0 0 0 1 2 0 0 7 0% 100% Column Total 40 40 40 40 40 40 30 40 108
User’s Accuracy 13% 38% 38% 60% 25% 63% 47% 0%
Errors of Commission 87% 62% 62% 40% 75% 37% 53% 100%
Overall Accuracy 35% Kappa Index 26%
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Pixels Classification Data Supervised Neural Net Classifier Halounova 9 layer - original, texture, filters Reference Data
Water Forest Grassland Dark Fields
Medium Fields
Light Fields
Impervious Surface
Bare Earth
Row Total
Producer’s Accuracy
Errors of Omission
Water 3 0 0 0 4 13 0 1 21 14.3% 85.7% Forest 3 25 12 4 3 0 5 5 57 43.9% 56.1% Grassland 14 2 7 6 7 2 9 6 53 13.2% 86.8% Dark Fields 0 0 1 24 0 0 0 0 25 96% 4% Medium Fields 4 0 1 5 15 1 0 0 26 57.7% 42.3%
Light Fields 2 0 0 0 1 17 0 0 20 85% 15% Impervious Surface 13 13 17 1 9 4 16 25 98 16.3% 83.7%
Bare Earth 1 0 2 0 1 3 0 3 10 30% 70% Column Total 40 40 40 40 40 40 30 40 110
User’s Accuracy 7.5% 62.5% 17.5% 60% 37.5% 42.5% 53.3% 7.5%
Errors of Commission 92.5% 37.5% 82.5% 40% 62.5% 57.5% 46.7% 92.5%
Overall Accuracy 35% Kappa Index 27%
Pixels Classification Data Supervised Neural Net Classifier Halounova 9 layer - original, texture, filters Post Processing System Reference Data
Water Forest Grassland Dark Fields
Medium Fields
Light Fields
Impervious Surface
Bare Earth
Row Total
Producer’s Accuracy
Errors of Omission
Water 29 0 0 0 0 0 0 0 29 100% 0% Forest 3 29 9 1 0 0 2 3 47 61.7% 38.3% Grassland 1 2 18 0 0 0 1 2 24 75% 25% Dark Fields 0 0 0 39 0 0 0 0 39 100% 0% Medium Fields 0 0 0 0 39 2 0 0 41 95.1% 4.9%
Light Fields 0 0 0 0 0 34 0 0 34 100% 0% Impervious Surface 7 9 12 0 1 4 27 16 76 35.5% 64.5%
Bare Earth 0 0 1 0 0 0 0 19 20 95% 5% Column Total 40 40 40 40 40 40 30 40 234
User’s Accuracy 72.5% 72.5% 45% 97.5% 97.5% 85% 90% 47.5%
Errors of Commission 27.5% 27.5% 55% 2.5% 2.5% 15% 10% 52.5%
Overall Accuracy 75% Kappa Index 72%
Pixels Classification Data Supervised Ogden PCA 3x3tex orig, mean, contrast, variance SVM Object-based classification Reference Data
Water Forest Grassland Dark Fields
Medium Fields
Light Fields
Impervious Surface
Bare Earth
Row Total
Producer’s Accuracy
Errors of Omission
Water 32 0 0 0 0 0 0 4 36 88.9% 11.1% Forest 3 28 15 2 2 0 1 6 57 49.1% 50.9% Grassland 0 1 5 0 1 0 4 0 11 45.5% 54.5% Dark Fields 0 0 0 37 1 0 1 0 39 94.9% 5.1% Medium Fields 0 1 0 0 23 34 0 0 58 39.7% 60.3%
Light Fields 0 0 1 1 2 6 0 0 10 60% 40% Impervious Surface 1 8 11 0 4 0 22 10 56 39.3% 60.7%
Bare Earth 4 2 8 0 7 0 2 20 43 46.5% 53.5% Column Total 40 40 40 40 40 40 30 40 173
User’s Accuracy 80% 70% 12.5% 92.5% 57.5% 15% 73.3% 50
Errors of Commission 20% 30% 87.5% 7.5% 42.5% 85% 26.7% 50
Overall Accuracy 56% Kappa Index 50%
88
Pixels Classification Data Supervised Ogden PCA 3x3tex orig, mean, contrast, variance SVM Object-based Post Processing System
Reference Data
Water Forest Grassland Dark Fields
Medium Fields
Light Fields
Impervious Surface
Bare Earth
Row Total
Producer’s Accuracy
Errors of Omission
Water 36 0 0 0 0 0 0 4 40 90% 10% Forest 3 34 11 1 1 0 0 4 54 63% 37% Grassland 1 2 22 0 0 0 1 0 26 84.6% 15.4% Dark Fields 0 0 0 38 1 0 0 0 39 97.4% 2.6% Medium Fields 0 2 0 0 38 6 0 0 46 82.6% 17.4%
Light Fields 0 0 0 1 0 34 0 0 35 97.1% 2.9% Impervious Surface 0 0 0 0 0 0 29 0 29 100% 0%
Bare Earth 0 2 7 0 0 0 0 32 41 78% 22% Column Total 40 40 40 40 40 40 30 40 263
User’s Accuracy 90% 85% 55% 95% 95% 85% 96.7% 80%
Errors of Commission 10% 15% 45% 5% 5% 15% 3.3% 20%
Overall Accuracy 85% Kappa Index 83%
Ogden level 2 classification scheme
Pixels Classification Data Unsupervised Ogden Data 10 Spectral Classes Reference Data Vegetation Croplan
d Other Row Total Producer’s
Accuracy Errors of Omission
Vegetation 26 5 20 51 51% 49% Cropland 52 104 71 227 45.8% 54.2% Other 2 11 19 32 59.4% 40.6% Column Total 80 120 110 149 User’s Accuracy 32.5% 86.7% 17.3% Errors of Commission 67.5% 13.3% 82.7% Overall Accuracy 48% Kappa Index 19%
Pixels Classification Data Unsupervised Ogden Data 25 Spectral Classes Reference Data Vegetation Croplan
d Other Row Total Producer’s
Accuracy Errors of Omission
Vegetation 21 4 15 40 52.5% 47.5% Cropland 54 111 73 238 46.6% 53.4% Other 5 5 22 32 68.8% 31.3% Column Total 80 120 110 154 User’s Accuracy 26.3% 92.5% 20% Errors of Commission 73.7% 7.5% 80% Overall Accuracy 50% Kappa Index 20%
Pixels Classification Data Unsupervised Ogden Data 100 Spectral Classes Reference Data Vegetation Croplan
d Other Row Total Producer’s
Accuracy Errors of Omission
Vegetation 16 2 6 24 66.7% 33.3% Cropland 63 118 93 274 43.1% 56.9% Other 1 0 11 12 91.7% 8.3% Column Total 80 120 110 145 User’s Accuracy 20% 98.3% 10% Errors of Commission 80% 1.7% 90% Overall Accuracy 47% Kappa Index 15%
89
Pixels Classification Data Unsupervised Ogden Data 10 Spectral Classes Halounova image Reference Data Vegetation Croplan
d Other Row Total Producer’s
Accuracy Errors of Omission
Vegetation 76 39 96 211 36% 64% Cropland 4 81 14 99 81.8% 18.2% Other 0 0 0 0 0% 100% Column Total 80 120 110 157 User’s Accuracy 95% 67.5% 0% Errors of Commission 5% 32.5% 100% Overall Accuracy 51% Kappa Index 30%
Pixels Classification Data Supervised 3x3 Occurrence SVM Reference Data Vegetation Croplan
d Other Row Total Producer’s
Accuracy Errors of Omission
Vegetation 63 16 28 107 58.9% 41.1% Cropland 8 78 22 108 72.2% 27.8% Bare Earth 9 26 60 95 63.2% 36.8% Column Total 80 120 110 201 User’s Accuracy 78.8% 65% 54.5% Errors of Commission 21.2% 35% 45.5%
Overall Accuracy 65% Kappa Index 47%
Pixels Classification Data Supervised Ogden PCA 3x3tex orig, mean, contrast, variance SVM Object-
based post processing system Reference Data Vegetatio
n Cropland
Other Row Total Producer’s Accuracy
Errors of Omission
Vegetation 69 2 8 79 87.3% 12.7% Cropland 2 118 1 121 97.5% 2.5% Other 9 0 101 110 91.8% 8.2% Column Total 80 120 110 288 User’s Accuracy 86.3% 98.3% 91.8% Errors of Commission 13.8% 1.7% 8.2% Overall Accuracy 93% Kappa Index 89%
Pixels Classification Data Supervised Ogden PCA 3x3tex orig, mean, contrast, variance SVM Object-
based classification Reference Data Vegetatio
n Cropland
Other Row Total Producer’s Accuracy
Errors of Omission
Vegetation 49 5 14 68 72.1% 27.9% Cropland 2 104 1 107 97.2% 2.8% Other 29 11 95 135 70.4% 29.6% Column Total 80 120 110 248 User’s Accuracy 61.3% 86.7% 86.4% Errors of Commission 38.8% 13.3% 13.6%
Overall Accuracy 80% Kappa Index 69%
Pixels Classification Data Supervised Ogden Original Unprocessed SVM Reference Data Vegetation Croplan
d Other Row Total Producer’s
Accuracy Errors of Omission
Vegetation 16 2 5 23 69.6% 30.4% Cropland 47 88 44 179 49.2% 50.8% Other 17 30 61 108 56.5% 43.5% Column Total 80 120 110 165 User’s Accuracy 20% 73.3% 55.5% Errors of Commission 80% 26.7% 44.5%
Overall Accuracy 53% Kappa Index 26%
90
Pixel Classification Data Supervised Halounova 9 layer - original, texture, filters Reference Data Vegetation Croplan
d Other Row Total Producer’s
Accuracy Errors of Omission
Vegetation 46 32 58 136 33.8% 66.2% Cropland 2 61 5 68 89.7% 10.3% Other 32 27 47 106 44.3% 55.7% Column Total 80 120 110 154 User’s Accuracy 57.5% 50.8% 42.7% Errors of Commission 42.5% 49.2% 57.3%
Overall Accuracy 50% Kappa Index 26%
Pixels Classification Data Supervised Neural Net Classifier Halounova 9 layer - original, texture, filters Reference Data Vegetation Croplan
d Other Row Total Producer’s
Accuracy Errors of Omission
Vegetation 46 22 42 110 41.8% 58.2% Cropland 2 63 6 71 88.7% 11.3% Other 32 35 62 129 48.1% 51.9% Column Total 80 120 110 171 User’s Accuracy 57.5% 52.5% 56.4% Errors of Commission 42.5% 47.5% 43.6%
Overall Accuracy 55% Kappa Index 33%
Pixels Classification Data Unsupervised Isodata Classifier Halounova 9 layer - original, texture, filters Reference Data Vegetation Croplan
d Other Row Total Producer’s
Accuracy Errors of Omission
Vegetation 60 20 61 141 42.6% 57.4% Cropland 11 93 28 132 70.5% 29.5% Other 9 7 21 37 56.8% 43.2% Column Total 80 120 110 174 User’s Accuracy 75% 77.5% 19.1% Errors of Commission 25% 22% 80.9%
Overall Accuracy 56% Kappa Index 35%
Pixels Classification Data Supervised Neural Net Classifier Halounova 9 layer - original, texture, filters
Post Processing System Reference Data Vegetation Croplan
d Other Row Total Producer’s
Accuracy Errors of Omission
Vegetation 58 1 12 71 81.7% 18.3% Cropland 0 114 0 114 100% 0% Other 22 5 98 125 78.4% 21.6% Column Total 80 120 110 270 User’s Accuracy 72.5% 95% 89.1% Errors of Commission 27.5% 5% 10.9%
Overall Accuracy 87% Kappa Index 80%
91
Ogden level 3 classification scheme
Pixels Classification Data Unsupervised Ogden Data 10 Spectral Classes Reference Data Cropland Non-Cropland Row Total Producer’s Accuracy Errors of Omission Cropland 104 123 227 45.8% 54.2% Non-Cropland 16 67 83 80.7% 19.3% Column Total 120 190 171 User’s Accuracy 86.7% 35.3% Errors of Commission 13.3% 64.7% Overall Accuracy 55% Kappa Index 19%
Pixels Classification Data Unsupervised Ogden Data 25 Spectral Classes Reference Data Cropland Non-Cropland Row Total Producer’s Accuracy Errors of Omission Cropland 111 127 238 46.6% 53.4% Non-Cropland 9 63 72 87.5% 12.5% Column Total 120 190 174 User’s Accuracy 92.5% 33.2% Errors of Commission 7.5% 66.8% Overall Accuracy 56% Kappa Index 22%
Pixels Classification Data Unsupervised Ogden Data 100 Spectral Classes Reference Data Cropland Non-Cropland Row Total Producer’s Accuracy Errors of Omission Cropland 118 156 274 43.1% 56.9% Non-Cropland 2 34 36 94.4% 5.6% Column Total 120 190 152 User’s Accuracy 98.3% 17.9% Errors of Commission 1.7% 82.1% Overall Accuracy 49% Kappa Index 13%
Pixels Classification Data Unsupervised Ogden Data 10 Spectral Classes Halounova image Reference Data Cropland Non-Cropland Row Total Producer’s Accuracy Errors of Omission Cropland 81 18 99 81.8% 18.2% Non-Cropland 39 172 211 81.5% 18.5% Column Total 120 190 253 User’s Accuracy 67.5% 90.5% Errors of Commission 32.5% 9.5% Overall Accuracy 82% Kappa Index 60%
Pixels Classification Data Supervised Ogden PCA 3x3tex original, mean, contrast, variance SVM
Object-based post processing system Reference Data Cropland Non-Cropland Row Total Producer’s Accuracy Errors of Omission Cropland 118 3 121 97.5% 2.5% Non-Cropland 2 187 189 98.9% 1.1% Column Total 120 190 305 User’s Accuracy 98.3% 98.4% Errors of Commission 1.7% 1.6% Overall Accuracy 98% Kappa Index 97%
Pixels Classification Data Supervised Ogden PCA 3x3tex orig, mean, contrast, variance SVM Object-
based classification Reference Data Cropland Non-Cropland Row Total Producer’s Accuracy Errors of Omission Cropland 104 3 107 97.2% 2.8% Non-Cropland 16 187 203 92.1% 7.9% Column Total 120 190 291 User’s Accuracy 86.7% 98.4% Errors of Commission 13.3% 1.6% Overall Accuracy 94% Kappa Index 87%
92
Pixels Classification Data Supervised Ogden Original Unprocessed SVM Reference Data Cropland Non-Cropland Row Total Producer’s Accuracy Errors of Omission Cropland 88 91 179 49.2% 50.8% Non-Cropland 32 99 131 75.6% 24.4% Column Total 120 190 187 User’s Accuracy 73.3% 52.1% Errors of Commission 26.7% 47.9% Overall Accuracy 60% Kappa Index 23%
Pixels Classification Data Supervised 3x3 Occurrence SVM Reference Data Cropland Non-Cropland Row Total Producer’s Accuracy Errors of Omission Cropland 78 30 108 72.2% 27.8% Non-Cropland 42 160 202 79.2% 20.8% Column Total 120 190 238 User’s Accuracy 65% 84.2% Errors of Commission 35% 15.8% Overall Accuracy 77% Kappa Index 50%
Pixels Classification Data Supervised Halounova 9 layer - original, texture, filters Reference Data Cropland Non-Cropland Row Total Producer’s Accuracy Errors of Omission Cropland 61 7 68 79.7% 10.3% Non-Cropland 59 183 242 75.6% 24.4% Column Total 120 190 244 User’s Accuracy 50.8% 96.3% Errors of Commission 49.2% 3.7% Overall Accuracy 79% Kappa Index 51%
Pixels Classification Data Supervised Neural Net Classifier Halounova 9 layer - original, texture,
filters Reference Data Cropland Non-Cropland Row Total Producer’s Accuracy Errors of Omission Cropland 63 8 71 88.7% 11.3% Non-Cropland 57 182 239 76.2% 23.8% Column Total 120 190 245 User’s Accuracy 52.5% 95.8% Errors of Commission 47.5% 4.2% Overall Accuracy 79% Kappa Index 52%
Pixels Classification Data Unsupervised Isodata Classifier Halounova 9 layer - original, texture, filters Reference Data Cropland Non-Cropland Row Total Producer’s Accuracy Errors of Omission Cropland 93 39 132 70.5% 29.5% Non-Cropland 27 151 178 84.8% 15.2% Column Total 120 190 244 User’s Accuracy 77% 79.5% Errors of Commission 22% 20.5 Overall Accuracy 79% Kappa Index 56%
Pixels Classification Data Supervised Neural Net Classifier Halounova 9 layer - original, texture,
filters Post Processing System Reference Data Cropland Non-Cropland Row Total Producer’s Accuracy Errors of Omission Cropland 114 0 114 100% 0% Non-Cropland 6 190 196 96.9% 3.1% Column Total 120 190 304 User’s Accuracy 95% 100% Errors of Commission 5% 0% Overall Accuracy 98% Kappa Index 96%
93
Salt Lake City unsupervised classification
Pixels Classification Data Unsupervised Salt Lake City Original ISODATA 10 Spectral Classes Reference Data
Trees Grass Transportation Residential Commercial Row Total
Producer’s Accuracy
Errors of Omission
Trees 22 12 4 9 2 49 44.9% 55.1% Grass 0 0 0 0 0 0 0% 0% Transportation 12 14 24 20 10 80 30% 70% Residential 0 0 0 0 0 0 0% 0% Commercial 6 4 12 11 28 61 45.9% 54.1% Column Total 40 30 40 40 40 74 User’s Accuracy 55% 0% 60% 0% 70%
Errors of Commission 45% 100% 40% 100% 30%
Overall Accuracy 39%
Kappa Index 23%
Pixels Classification Data Unsupervised Salt Lake City Original ISODATA 25 Spectral Classes Reference Data
Trees Grass Transportation Residential Commercial Row Total
Producer’s Accuracy
Errors of Omission
Trees 22 12 4 9 2 49 44.9% 55.1% Grass 0 0 0 0 0 0 0% 100% Transportation 9 11 20 18 8 66 30.3% 69.7% Residential 0 0 0 0 0 0 0% 100% Commercial 9 7 16 13 30 75 48.4% 51.6% Column Total 40 30 40 40 40 72 User’s Accuracy 55% 0% 50% 0% 75%
Errors of Commission 45% 100% 50% 100% 25%
Overall Accuracy 38%
Kappa Index 30%
Pixels Classification Data Unsupervised Salt Lake City Original ISODATA 100 Spectral Classes Reference Data
Trees Grass Transportation Residential Commercial Row Total
Producer’s Accuracy
Errors of Omission
Trees 23 13 5 11 2 54 42.6% 57.4% Grass 0 0 0 0 0 0 0% 0% Transportation 8 9 18 14 6 55 32.7% 67.3% Residential 0 0 0 0 0 0 0% 0% Commercial 9 8 17 15 32 81 39.5% 60.5% Column Total 40 30 40 40 40 73 User’s Accuracy 57.5% 0% 45% 0% 80%
Errors of Commission 42.5% 100% 55% 100% 20%
Overall Accuracy 38%
Kappa Index 22%
94
Salt Lake City level 1 classification scheme
Pixels Classification Data Salt Lake City Original Supervised Minimum Distance Classifier Reference Data
Trees Grass Transportation Residential Commercial Row Total
Producer’s Accuracy
Errors of Omission
Trees 17 3 2 4 0 26 65.4% 34.6% Grass 12 14 9 12 6 53 26.4% 73.6% Transportation 1 4 8 7 2 22 36.4% 63.6% Residential 4 6 11 7 5 33 21.2% 78.8% Commercial 6 3 10 10 27 56 48.2% 51.8% Column Total 40 30 40 40 40 73 User’s Accuracy 42.5% 46.7% 20% 17.5% 67.5%
Errors of Commission 57.5% 53.3% 80% 82.5% 32.5%
Overall Accuracy 38%
Kappa Index 23%
Pixels Classification Data Salt Lake City Original Supervised Support Vector Machine Classifier Reference Data
Trees Grass Transportation Residential Commercial Row Total
Producer’s Accuracy
Errors of Omission
Trees 15 0 1 2 0 18 83.3% 16.7% Grass 0 0 0 0 0 0 0% 0% Transportation 11 13 20 19 9 72 27.8% 72.2% Residential 8 13 5 8 2 36 22.2% 77.8% Commercial 6 4 14 11 29 64 45.3% 54.7% Column Total 40 30 40 40 40 72 User’s Accuracy 37.5% 0% 50% 20% 72.5%
Errors of Commission 62.5% 100% 50% 80% 27.5%
Overall Accuracy 38%
Kappa Index 21%
Pixels Classification Data Salt Lake City 5x5 Texture Occurrence Supervised Support Vector Machine Classifier
Reference Data
Trees Grass Transportation Residential Commercial Row Total
Producer’s Accuracy
Errors of Omission
Trees 11 0 2 1 0 14 78.6% 21.4% Grass 0 1 0 0 0 1 100% 0% Transportation 4 8 17 8 8 45 37.8% 62.2% Residential 24 19 9 24 8 84 28.6% 71.4% Commercial 1 2 12 0 24 39 61.5% 38.5% Column Total 40 30 40 33 40 77 User’s Accuracy 27.5% 3.3% 42.5% 72.7% 60%
Errors of Commission 72.5% 96.7% 57.5% 27.3% 40%
Overall Accuracy 41%
Kappa Index 27%
95
Pixels Classification Data Salt Lake City 5x5 Texture Occurrence Saturation Stretch Supervised Support
Maximum Likelihood Reference Data
Trees Grass Transportation Residential Commercial Row Total
Producer’s Accuracy
Errors of Omission
Trees 12 0 2 1 0 15 80% 20% Grass 7 16 9 8 3 43 37.2% 62.8% Transportation 4 7 17 9 13 50 34% 66% Residential 16 6 1 15 4 42 35.7% 64.3% Commercial 1 1 11 7 20 40 50% 50% Column Total 40 30 40 40 40 80 User’s Accuracy 30% 53.3% 42.5% 37.5% 50%
Errors of Commission 70% 46.7% 57.5% 62.5% 50%
Overall Accuracy 42%
Kappa Index 28%
Pixels Classification Data Salt Lake City 11x11 Texture Occurrence Support Vector Machine Classifier Reference Data
Trees Grass Transportation Residential Commercial Row Total
Producer’s Accuracy
Errors of Omission
Trees 8 1 3 1 0 13 61.5% 38.5% Grass 2 3 1 1 0 7 42.9% 57.1% Transportation 2 5 23 11 5 46 50% 50% Residential 27 18 7 24 14 90 26.7% 73.3% Commercial 1 3 6 3 21 34 61.8% 38.2% Column Total 40 30 40 40 40 79 User’s Accuracy 20% 10% 57.5% 60% 52.5%
Errors of Commission 80% 90% 42.5% 40% 47.5%
Overall Accuracy 42%
Kappa Index 26%
Pixels Classification Data Salt Lake City Neural Net Classifier Halounova Image Reference Data
Trees Grass Transportation Residential Commercial Row Total
Producer’s Accuracy
Errors of Omission
Trees 8 2 3 6 1 20 40% 60% Grass 0 0 0 0 0 0 0% 100% Transportation 1 6 22 2 11 42 52.4% 47.6% Residential 31 22 8 32 15 108 29.6% 70.4% Commercial 0 0 7 0 13 20 65% 35% Column Total 40 30 40 40 40 75 User’s Accuracy 20% 0% 55% 80% 32.5%
Errors of Commission 80% 100% 45% 20% 67.5%
Overall Accuracy 39%
Kappa Index 23%
96
Pixels Classification Data Supervised Salt Lake City Object-based Support Vector Machine Classifier Reference Data
Trees Grass Transportation Residential Commercial Row Total
Producer’s Accuracy
Errors of Omission
Trees 15 2 1 1 0 19 78.9% 21.1% Grass 11 18 6 15 2 52 34.6% 65.4% Transportation 2 3 20 3 3 31 64.5% 35.5% Residential 11 7 4 20 7 49 40.8% 59.2% Commercial 1 0 9 1 28 39 71.8% 28.2% Column Total 40 30 40 40 40 101 User’s Accuracy 37.5% 60% 50% 50% 70%
Errors of Commission 62.5% 40% 50% 50% 30%
Overall Accuracy 53%
Kappa Index 42%
Pixels Classification Data Supervised Salt Lake City Object-based Support Vector Machine Post Processing
System Reference Data
Trees Grass Transportation Residential Commercial Row Total
Producer’s Accuracy
Errors of Omission
Trees 16 2 1 1 0 20 80% 20% Grass 5 20 3 6 0 34 58.8% 41.2% Transportation 2 4 33 3 1 43 76.7% 23.3% Residential 6 0 0 29 0 35 82.9% 17.1% Commercial 11 4 3 1 39 58 67.2% 32.8% Column Total 40 30 40 40 40 137 User’s Accuracy 40% 66.7% 82.5% 72.5% 97.5%
Errors of Commission 60% 33.3% 17.5% 27.5% 2.5%
Overall Accuracy 72%
Kappa Index 65%
Salt Lake City level 2 classification scheme
Pixels Classification Data Unsupervised Salt Lake City Original ISODATA 10 Spectral Classes Reference Data Built up
Area Vegetation Transportation Row
Total Producer’s Accuracy
Errors of Omission
Built up Area 39 10 12 61 63.9% 36.1% Vegetation 11 34 4 49 69.4% 30.6% Transportation 30 26 24 80 30% 70% Column Total 80 70 40 97 User’s Accuracy 48.7% 48.6% 60% Errors of Commission 51.3% 51.4% 40%
Overall Accuracy 51% Kappa Index 20%
Pixels Classification Data Unsupervised Salt Lake City Original ISODATA 25 Spectral Classes Reference Data Built up
Area Vegetation Transportation Row
Total Producer’s Accuracy
Errors of Omission
Built up Area 43 16 16 75 57.3% 42.7% Vegetation 11 34 4 49 69.4% 30.6% Transportation 26 20 20 66 30.3% 69.7% Column Total 80 70 40 97 User’s Accuracy 53.8% 48.6% 50% Errors of Commission 46.2% 51.4% 50%
Overall Accuracy 51% Kappa Index 20%
97
Pixels Classification Data Salt Lake City Original Supervised Minimum Distance Classifier Reference Data Built up
Area Vegetation Transportation Row
Total Producer’s Accuracy
Errors of Omission
Built up Area 49 19 21 89 55.1% 44.9% Vegetation 22 46 11 79 58.2% 41.8% Transportation 9 5 8 22 36.4% 63.6% Column Total 80 70 40 103 User’s Accuracy 61.3% 65.7% 20% Errors of Commission 38.7% 34.3% 80%
Overall Accuracy 54% Kappa Index 25%
Pixels Classification Data Salt Lake City Original Supervised Support Vector Machine Classifier Reference Data Built up
Area Vegetation Transportation Row
Total Producer’s Accuracy
Errors of Omission
Built up Area 50 31 19 100 50% 50% Vegetation 2 15 1 18 83.3% 16.7% Transportation 28 24 20 72 27.8% 72.2% Column Total 80 70 40 85 User’s Accuracy 62.5% 21.4% 50% Errors of Commission 37.5% 78.6% 50%
Overall Accuracy 45% Kappa Index 8%
Pixels Classification Data Supervised Salt Lake City Object-based Support Vector Machine Classifier Reference Data Built up
Area Vegetation Transportation Row
Total Producer’s Accuracy
Errors of Omission
Built up Area 56 19 13 88 63.6% 36.4% Vegetation 18 46 7 71 64.8% 35.2% Transportation 6 5 20 31 64.5% 35.5% Column Total 80 70 40 122 User’s Accuracy 70% 65.7% 50% Errors of Commission 30% 34.3% 50%
Overall Accuracy 64% Kappa Index 41%
Pixels Classification Data Supervised Salt Lake City Object-based 5x5 Occurrence Texture Saturation Stretch Support Vector Machine Classifier
Reference Data Built up Area
Vegetation Transportation Row Total
Producer’s Accuracy
Errors of Omission
Built up Area 56 12 12 80 70% 30% Vegetation 22 46 5 73 63% 37% Transportation 2 12 23 37 62.2% 37.8% Column Total 80 71 40 125 User’s Accuracy 70% 65.7% 57.5% Errors of Commission 30% 34.3% 42.5%
Overall Accuracy 66% Kappa Index 44%
98
Pixels Classification Data Supervised Salt Lake City Neural Net Classifier Reference Data Built up
Area Vegetation Transportation Row
Total Producer’s Accuracy
Errors of Omission
Built up Area 60 53 15 128 46.9% 53.1% Vegetation 7 10 3 20 50% 50% Transportation 13 7 22 42 52.4% 47.6% Column Total 80 70 40 92 User’s Accuracy 75% 14.3% 55% Errors of Commission 25% 85.7% 45%
Overall Accuracy 39% Kappa Index 23%
Pixels Classification Data Supervised Salt Lake City Object-based 11x11 Occurrence Texture Support
Vector Machine Classifier Reference Data Built up
Area Vegetation Transportation Row
Total Producer’s Accuracy
Errors of Omission
Built up Area 62 49 13 124 50% 50% Vegetation 2 14 4 20 70% 30% Transportation 16 7 23 46 50% 50% Column Total 80 70 40 99 User’s Accuracy 77.5% 20% 57.5% Errors of Commission 22.5% 80% 42.5%
Overall Accuracy 52% Kappa Index 20%
Pixels Classification Data Supervised Salt Lake City Object-based Support Vector Machine Post
Processing System Reference Data Built up
Area Vegetation Transportation Row
Total Producer’s Accuracy
Errors of Omission
Built up Area 69 21 3 93 74.2% 25.8% Vegetation 1 43 4 48 89.6% 10.4% Transportation 4 6 33 43 76.7% 23.3% Column Total 74 70 40 145 User’s Accuracy 93.2% 61.4% 82.5% Errors of Commission 6.8% 38.6% 17.5%
Overall Accuracy 76% Kappa Index 65%
Salt Lake City level 3 classification scheme
Pixels Classification Data Unsupervised Salt Lake City Original ISODATA 10 Spectral Classes Reference Data Built up
Area Non Built up Area
Row Total Producer’s Accuracy Errors of Omission
Built up Area 39 22 61 63.9% 36.1% Non Built up Area 41 88 129 68.2% 31.8% Column Total 80 110 127 User’s Accuracy 48.8% 80% Errors of Commission 51.2% 20%
Overall Accuracy 67% Kappa Index 30%
99
Pixels Classification Data Unsupervised Salt Lake City Original ISODATA 25 Spectral Classes Reference Data Built up
Area Non Built up Area Row Total Producer’s
Accuracy Errors of Omission
Built up Area 43 32 75 57.3% 42.7% Non Built up Area 37 78 115 67.8% 32.2% Column Total 80 110 121 User’s Accuracy 53.8% 70.9% Errors of Commission 46.2% 29.1%
Overall Accuracy 64% Kappa Index 25%
Pixels Classification Data Salt Lake City 5x5 Texture Occurrence Supervised Support Vector Machine
Classifier Reference Data Built up
Area Non Built up Area Row Total Producer’s
Accuracy Errors of Omission
Built up Area 63 67 130 48.5% 51.5% Non Built up Area 17 43 60 71.7% 28.3% Column Total 80 110 106 User’s Accuracy 78.8% 39.1% Errors of Commission 21.2% 60.9%
Overall Accuracy 56% Kappa Index 16%
Pixels Classification Data Salt Lake City 5x5 Texture Occurrence Saturation Stretch Supervised Support Maximum Likelihood
Reference Data Built up Area
Non Built up Area Row Total Producer’s Accuracy
Errors of Omission
Built up Area 46 36 82 56.1% 43.9% Non Built up Area 34 74 108 68.5% 31.5% Column Total 80 110 120 User’s Accuracy 57.5% 67.3% Errors of Commission 42.5% 32.7%
Overall Accuracy 63% Kappa Index 25%
Pixels Classification Data Salt Lake City 11x11 Texture Occurrence Supervised Support Vector Machine
Classifier Reference Data Built up
Area Non Built up Area Row Total Producer’s
Accuracy Errors of Omission
Built up Area 62 62 124 50% 50% Non Built up Area 18 48 66 72.7% 27.3% Column Total 80 110 110 User’s Accuracy 77.5% 43.6% Errors of Commission 22.5% 56.4%
Overall Accuracy 58% Kappa Index 20%
Pixels Classification Data Salt Lake City Neural Net Classifier Reference Data Built up
Area Non Built up Area Row Total Producer’s
Accuracy Errors of Omission
Built up Area 60 68 128 46.9% 53.1% Non Built up Area 20 42 62 67.7% 32.3% Column Total 80 110 102 User’s Accuracy 75% 38.2% Errors of Commission 25% 61.8%
Overall Accuracy 54% Kappa Index 12%
100
Pixels Classification Data Supervised Salt Lake City Object-based Support Vector Machine Classifier Reference Data Built up
Area Non Built up Area Row Total Producer’s
Accuracy Errors of Omission
Built up Area 56 32 88 63.6% 36.4% Non Built up Area 24 78 102 76.5% 23.5% Column Total 80 110 134 User’s Accuracy 70% 70.9% Errors of Commission 30% 29.1%
Overall Accuracy 71% Kappa Index 40%
Pixel Classification Data Supervised Salt Lake City Object-based Support Vector Machine Post
Processing System Reference Data Built up
Area Non Built up Area Row Total Producer’s
Accuracy Errors of Omission
Built up Area 69 24 93 74.2% 25.8% Non Built up Area 11 86 97 88.7% 11.3% Column Total 80 110 155 User’s Accuracy 86.3% 78.2% Errors of Commission 13.2% 21.8%
Overall Accuracy 82% Kappa Index 63%
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APPENDIX 2: VECTOR EDITING TOOLBAR C#.NET CODE
Merge Polygon Button using ESRI.ArcGIS.ArcMapUI; using System; using System.Collections.Generic; using System.Linq; using System.Text; using System.Windows.Forms; using ESRI.ArcGIS.Carto; using ESRI.ArcGIS.Editor; using ESRI.ArcGIS.Geometry; using ESRI.ArcGIS.Geodatabase; namespace MergePolys { class MergePolysMethods { //neighborType is either "smallest" or "largest" depending on the button clicked. static internal void Merge(string neighborType) { try { IActiveView activeView = ArcMap.Document.ActiveView; IEditor3 theEditor; IEditLayers editLayers; IFeatureSelection featSel; string message; //Check that edit session is set up correctly for merging and set up featSel if (SetupOK(out theEditor, out editLayers, out featSel, out message) != true) { MessageBox.Show(message); return; } ICursor cursor; IFeatureCursor featCursor; IFeature currentFeat; IFeature featToBeMergedWith; ITopologicalOperator topoOp; featSel.SelectionSet.Search(null, false, out cursor); featCursor = cursor as IFeatureCursor; currentFeat = featCursor.NextFeature(); while (currentFeat != null) { featToBeMergedWith = GetAdjacentFeature(currentFeat, neighborType); if (featToBeMergedWith != null) { topoOp = featToBeMergedWith.ShapeCopy as ITopologicalOperator; featToBeMergedWith.Shape = topoOp.Union(currentFeat.ShapeCopy); featToBeMergedWith.Store(); currentFeat.Delete(); } else { MessageBox.Show("No polygons are adjacent to: " + currentFeat.OID); } currentFeat = featCursor.NextFeature(); } ArcMap.Document.ActiveView.Refresh(); } catch (Exception ex)
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{ MessageBox.Show("Error: " + ex.Message + Environment.NewLine + "Merge aborted!"); } } static internal void Merge(IPoint clickPoint) { try { IActiveView activeView = ArcMap.Document.ActiveView; IEditor3 theEditor; IEditLayers editLayers; IFeatureSelection featSel; string message; //Check that edit session is set up correctly for merging and set up featSel if (SetupOK(out theEditor, out editLayers, out featSel, out message) != true) { MessageBox.Show(message); return; } if (featSel.SelectionSet.Count > Properties.Settings.Default.MaxSelBeforeWarn) { string dialogMessage = "There are " + Properties.Settings.Default.MaxSelBeforeWarn.ToString() + " features selected to be merged. Are you sure you want to merge all those that touch the clicked polygon?"; const string caption = "Warning"; var result = MessageBox.Show(dialogMessage, caption, MessageBoxButtons.YesNo, MessageBoxIcon.Exclamation); if (result == DialogResult.No) { return; } } ICursor cursor; IFeatureCursor featCursor; IFeature currentFeat; IFeature featToBeMergedWith; ITopologicalOperator topoOp; featSel.SelectionSet.Search(null, false, out cursor); featCursor = cursor as IFeatureCursor; currentFeat = featCursor.NextFeature(); //get feature selected by mouse click clickPoint.SpatialReference = currentFeat.Shape.SpatialReference; ISpatialFilter clickSpatFilter = new SpatialFilter(); clickSpatFilter.Geometry = clickPoint as IGeometry; clickSpatFilter.SpatialRel = ESRI.ArcGIS.Geodatabase.esriSpatialRelEnum.esriSpatialRelIntersects; IQueryFilter queryFilter = (IQueryFilter)clickSpatFilter; IFeatureCursor toBeMergedWithFeatCursor = editLayers.CurrentLayer.FeatureClass.Search(queryFilter, false); int numMerged = 0; featToBeMergedWith = toBeMergedWithFeatCursor.NextFeature(); while (currentFeat != null) { if (FeaturesTouch(featToBeMergedWith, currentFeat)) { topoOp = featToBeMergedWith.ShapeCopy as ITopologicalOperator; featToBeMergedWith.Shape = topoOp.Union(currentFeat.ShapeCopy); featToBeMergedWith.Store(); currentFeat.Delete(); numMerged++; } currentFeat = featCursor.NextFeature(); } if(numMerged==0)
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{ MessageBox.Show("Merge polygon did not touch selected features"); } else{ ArcMap.Document.ActiveView.Refresh(); } } catch (Exception ex) { MessageBox.Show("Error: " + ex.Message + Environment.NewLine + "Merge aborted!"); } } static private IFeature GetAdjacentFeature(IFeature featToBeMerged, string neighborType) { ISpatialFilter spatFilter = new SpatialFilter(); IFeatureClass theFeatClass = featToBeMerged.Class as IFeatureClass; spatFilter.Geometry = featToBeMerged.Shape; spatFilter.SpatialRel = esriSpatialRelEnum.esriSpatialRelIntersects; IFeatureCursor featCursor = theFeatClass.Search(spatFilter, false); IFeature tempFeat; IFeature featToBeMergedWith = null; Double threshold = 0; Double tempFeatArea = 0; tempFeat = featCursor.NextFeature(); while (tempFeat != null) { if (tempFeat.OID != featToBeMerged.OID) { tempFeatArea = GetArea(tempFeat.Shape as IArea); if (featToBeMergedWith == null) { featToBeMergedWith = tempFeat; threshold = tempFeatArea; } else { switch (neighborType) { case "smallest": if (tempFeatArea < threshold) { featToBeMergedWith = tempFeat; threshold = tempFeatArea; } break; case "largest": if (tempFeatArea > threshold) { featToBeMergedWith = tempFeat; threshold = tempFeatArea; } break; } } } tempFeat = featCursor.NextFeature(); } return featToBeMergedWith; } static private bool FeaturesTouch(IFeature featA, IFeature featB) { IRelationalOperator relationalOperator = (IRelationalOperator)featA.Shape; return relationalOperator.Touches(featB.Shape); }
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static private Double GetArea(IArea thePolygon) { return thePolygon.Area; } static private bool SetupOK(out IEditor3 theEditor, out IEditLayers editLayers, out IFeatureSelection featSel, out string message) { try { theEditor = null; editLayers = null; featSel = null; theEditor = ArcMap.Application.FindExtensionByName("ESRI Object Editor") as IEditor3; if (theEditor.EditState != esriEditState.esriStateEditing) { message = "Please start an editing session first!"; return false; } editLayers = theEditor as IEditLayers; if (editLayers.CurrentLayer.FeatureClass.ShapeType != esriGeometryType.esriGeometryPolygon) { message = "Current edit layer must be a polygon layer."; return false; } featSel = editLayers.CurrentLayer as IFeatureSelection; if (featSel.SelectionSet.Count == 0) { message = "No features to be merged have been selected."; return false; } message = "OK"; return true; } catch { throw new Exception("Error checking edit session."); } } } }
Select by Area Button
using ESRI.ArcGIS.esriSystem; using System; using System.Collections.Generic; using System.ComponentModel; using System.Data; using System.Drawing; using System.Linq; using System.Text; using System.Windows.Forms; using ESRI.ArcGIS.ArcMapUI; using ESRI.ArcGIS.Carto; using ESRI.ArcGIS.Editor; using ESRI.ArcGIS.Geometry; using ESRI.ArcGIS.Geodatabase; namespace MergePolys { public partial class SelectByArea : Form
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{ public SelectByArea() { InitializeComponent(); } private void btn_Cancel_Click(object sender, EventArgs e) { this.Dispose(); } private void btn_Select_Click(object sender, EventArgs e) { IActiveView activeView = ArcMap.Document.ActiveView; IEditor3 theEditor; IEditLayers editLayers; IFeatureClass featCLS; try { theEditor = ArcMap.Application.FindExtensionByName("ESRI Object Editor") as IEditor3; if (theEditor.EditState != esriEditState.esriStateEditing) { MessageBox.Show("Please start an editing session first!"); this.Dispose(); } else { editLayers = theEditor as IEditLayers; featCLS = editLayers.CurrentLayer.FeatureClass; if (featCLS.ShapeType != esriGeometryType.esriGeometryPolygon) { MessageBox.Show("Current edit layer must be a polygon layer."); } else { IFeatureSelection featureSelection = editLayers.CurrentLayer as IFeatureSelection; IQueryFilter qf = new QueryFilterClass(); qf.WhereClause = "Shape_Area < " + this.txt_Area.Text; activeView.PartialRefresh(esriViewDrawPhase.esriViewGeoSelection, null, null); featureSelection.SelectFeatures(qf, esriSelectionResultEnum.esriSelectionResultNew, false); activeView.PartialRefresh(esriViewDrawPhase.esriViewGeoSelection, null, null); this.Close(); } } } catch (Exception ex) { MessageBox.Show("Error selecting by area - " + ex.Message); this.Dispose(); } } } }
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