extraction of linear features by edge detection...

CHAPTER 5

EXTRACTION OF LINEAR FEATURES BY EDGE

DETECTION TECHNIQUES

5.1 Introduction

The term ―feature‖ in Remote Sensing Image (RSI) takes its specific definition

from the objective and scope of the study by the analyst. In spatial data mining using

remote sensing satellite data, features mainly indicate objects constituting natural

resources such as land, water and sea. This may broadly encompass vegetation, land

condition, water quality, extent and types of vegetation and combinations these features.

Extracting features in spectral domain is a tedious task requiring combination of image

processing techniques such as Principal Component Analysis (PCA), statistical operators

and clustering methods. The PCA as discussed in the chapter 4 has helped to understand

the necessity of adapting certain mathematical functions not only to reduce the multi-

dimensional image but enhancing the image to identify and extract relevant features.

Similarly, boundary detection and detecting linear features from RSI involves

enhancement techniques using statistical operators.

Linear features are identified either by their sharp continuities or abrupt

discontinuities of objects in an image. In other words, it may be stated as the process of

identifying and locating sharp discontinuities detecting edges in an image. Detection of

edges in an image is a very important feature-extraction method and has been widely

used in many computer vision and image processing applications. This is based on the

idea to locate object boundary information by thresholding pixels using some statistical

edge detectors and mapping the pixel-intensity variation in the selected RSI. Edge

detection in an image may be studied under edge structure and orientation and noise in

the image.

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Many previous studies on edge detection are subjective as well objective studies

such as computation approach to machine perception of solids [RO65], object

enhancement [PR70], edge detection [CA86], boundary detection algorithms for edge

detection [PDK96], and parametric selection for edge detection [YP03]. Studies have

shown that appropriate filter is an ordeal while undertaking edge detection that leads to

identify many apparent as well as subtle linear features. The other puzzling matter in

edge detection is that it may sometimes applied during pre-processing stage to extract

some boundary features which may again processed further for specific application

[SFT05]. At the same time, in some specific application such as studying structural

aspects of earth for mineral investigation and structural deformation on the earth‘s

surface, edge detection algorithms play a significant role as feature extraction tool

[AM05]. Edge detection and linear feature extraction techniques have a major role in

identifying linear features for geological application to identify water and mineral

resources of an area and applied for disaster mitigation studies like earth quake, landslide

floods and so on.

5.2 Theoretical Background of Edge Detection Algorithms

Extraction of spatial components features based on spectral values of pixels (DN

values) in RSI involve spatial transformation. Transforms may be applied to extract local

information of a subset or area of interest of an image such as convolution transformation

or global to extract full image information using Fourier transformation beside scale

space filters such as Gaussian, Laplacian and wavelet transform to extract spatial

information in feature space. These filters may be generally categorized under three

broad types: linear, statistical and gradient filters.

Linear filters use moving window on the image as a 3 x 3 matrix form. Classical

method of liner filter to detect edges or linear features is convolution filter. It is

calculated in the spatial domain as the weighted sum of pixels within the moving window

(kernel). For example, input image ‗I‘ of Nx x Ny matrix where Nx is the number of bands

in x column and Ny is the number of y column and window response function ‗w‘, the


output pixel Pij may be mathematically written as 𝑃𝑖𝑗 = 𝑁𝑥−1𝑚=0 𝐼𝑚𝑛 𝑤𝑖−𝑚 ,𝑗−𝑛

𝑁𝑦−1

𝑛=0 and

size of ‗w‘ may be 𝑤𝑥 × 𝑤𝑦 . This may again be simply expressed as P = I * w.

The significant characteristic of convolution filter is that the output sum of two or

more inputs is equal to the sum of individual outputs that would be produced by each

input separately using the principle of superposition. The value at the center of the

designed window of the filter is multiplied with the pixel and the output is stored as the

new value for that pixel. The process is repeated till all the pixels are weighted and their

values are stored in the output. All these filters move linearly row after row until it

computes the weighted sum for all the pixels in the image. Apart from convolution filter

some other relatively important filters are Low-Pass Filter (LPF), High-Pass Filter (HPF),

high boot filter, box filters and cascaded filters.

Statistical filters on the other hand, measure statistical property of an image such

as mean, median, standard deviation, mode minimum value and maximum value. This is

mostly used to obtain local information of an image but nevertheless very significant in

terms of noise reduction, Signal to Noise Ratio (SNR) and texture feature extraction. The

most significant statistical filter is morphological filter that applies a structuring element

to an input image, creating an output image of the same size. In this process, value of

each pixel in the output image is based on a comparison of the corresponding pixel in the

input image with its neighbors and the size and shape of the neighborhood could be

chosen to construct a morphological operation that is sensitive to specific shapes in the

input image.

The most basic morphological operations are dilation and erosion, where dilation

adds pixels to the boundaries of objects in an image, while erosion removes pixels on

object boundaries. The number of pixels added or removed from the objects in an image

depends on the size and shape of the structuring element used to process the image.

Morphological functions position the origin of the structuring element, its center element,

over the pixel of interest in the input image. For pixels at the border of an image, the

morphological functions assign a value to these undefined pixels, as if the functions had


padded the image with additional rows and columns. The value of these padding pixels

varies for dilation and erosion operations. Rule for dilation may be explained as the value

of the output pixel is the maximum value of all the pixels in the input pixel's

neighborhood. Similarly, rule for erosion may be given as the value of the output pixel is

the minimum value of all the pixels in the input pixel's neighborhood and in a binary

image, if any of the pixels is set to 0, then the output pixel is also set to 0. The shaped

window (structuring element) can be designed to perform pattern matching for

modification of shapes and the window shape could be square, rectangular, and in any

desired shape. This type of filter processing is mainly used for segmentation and noise

removal besides in earth resource applications [TGS00].

Similarly, median filter is an important tool in removing outliers and other

isolated noise in an image. In this operator, the output of the median filter is the DN of

the pixel at the middle of the list and excludes typical pixels in the sequence of the order

of pixels. Use of statistical operators is an application specific and may be used to extract

information on the general trend of pixels values and predominance of specific feature in

an image.

Special types of filters that play an important role in detecting significant change

in the DN values from one pixel to another are gradient filters. The directional filters

produce images of DN values that are proportional to the difference between neighboring

pixel value (DN value) in a given direction. An isotropic gradient is computed and

applied on the selected RSI to derive the edge enhanced image showing linear features.

Some of the important gradient filters are Sobel, Prewitt and Robert filters. These filters

are highly useful in extracting subtle linear features on the earth‘s surface and immensely

used in extracting structural information of earth‘s feature and in disaster mitigation

studies.

Apart from these filters, some special filters are also applied on RSI for image

compression, remove noise and extract features. For example, Fourier transform filter,

scale-space transform filter – Laplacian of Gaussian (LoG) and wavelet transforms


[VFA06]. Fourier transform is a general analysis of data in one or more dimensions as

linear combinations of sinusoidal functions. Fast Fourier Transformation (FFT) is the

most common algorithm used in analysis remote sensing satellite image for linear feature

extraction. Fourier transform expands manipulates an image into weighted sum of global

cosine and sine functions.

Laplacian of Gaussian operator is a second order derivative filter and it

overcomes certain limitations of the first order gradient filter in detecting edges and in

turn, linear features. First order gradient filters extract information from the local

neighborhood about each pixel and difficult to find edges explicitly over large scale. On

the other hand, pyramidal representation of Laplacian-Gaussian filters provides easier

access to analyze multiple image scale with a single-size filter. These are also called zero

crossing filters producing fully connected lines and could enhance both low contrast as

well as high contrast features [HA84]. Both horizontal and vertical edges could be

extracted using second order derivative in both the x and y directions, Laplacian of I,

which may expressed as ∇2𝐼 =𝜕2𝐼

𝜕𝑥 2 +𝜕2𝐼

𝜕𝑦 2 . The LoG filter is linear and rotationally

symmetrical with one mask searching for zero crossing of the image that has been

smoothed with a Gaussian mask or kernel, and computing the second derivative

convolving the image with LoG. It may be simply written as∇2 𝐺 ⊗ 𝐼 = ∇2𝐺 ⊗ 𝐼.

Wavelet transforms carryout decomposition of an image into components at different

scales and with different resolutions [ZHH07; HZL08]. This is used for extracting high

frequency features such as points, lines and edges for automated registration of two

images and for fusion of images from different sensors of remote sensing satellites.

In short, both the first and second order derivatives filters help in extracting

information on linear features, detecting edges and fusion of satellite image of different

sensors. All these operators involve certain mathematical functions which are described

in the following section.


5.3 Mathematical Background of the First Order Derivative Filter

In the present RSI analysis for linear feature identification and extraction, first

order derivative gradient filters such as Sobel and Prewitt filters are used to detect the

edges. The gradient filters operate both horizontally and vertically and combined to

generate an output showing gradient changes in pixel intensity of the image. Such

changes in intensity helped to ascertain physical boundary of features and from such

boundaries information on objects and their degree of interaction with other objects could

be extracted.

Detection of such linear boundaries and edges depends upon the nature of

inherent variables such as edge orientation, its structure and the degree of noise present in

the image. The operator should also be designed based on its geometry to determine the

characteristic direction in which the operator is most sensitive to edges and could bring

out such information. Based on this assumption, the filter must be optimized so that it

could enhance pixels showing continuity in horizontal, vertical and diagonal as well. For

such operation, presence of noise is a hindrance and appropriate data cleansing to be

applied before implementing such mathematical operators. The mathematical

components involved in the present analysis of extracting features using first order

derivatives may be explained as follows.

The directional high pass filters could produce an output image showing DN

values that are proportional to the difference between neighboring pixel values (DN

values) in any direction and thus computing the directional gradient. Implementing the

filter on the selected RSI in two orthogonal directions, both horizontal and vertical, may

result in an isotropic gradient combining the vector calculation of pixels in the image.

The magnitude of the local computation of gradient filter may be estimated by the length

of the composite vector. The components of the gradient may be computed using the

following approximation:

𝜕𝑓 (𝑥 ,𝑦)

𝜕𝑥= ∆𝑥 =

𝑓 𝑥+𝑑𝑥 ,𝑦 −𝑓(𝑥 ,𝑦)

𝑑𝑥


𝜕𝑓 (𝑥 ,𝑦)

𝜕𝑥= ∆𝑦 =

𝑓 𝑥 ,𝑦+𝑑𝑦 −𝑓(𝑥 ,𝑦)

𝑑𝑦

where 𝑑𝑥 and 𝑑𝑦 measure distance along the x and y directions respectively.

In discrete images, one can consider 𝑑𝑥 and 𝑑𝑦 in terms of numbers of pixel

between two points 𝑑𝑥 = 𝑑𝑦 = 1, which is the pixel spacing, and the point at which pixel

coordinates are (i, j) then,

∆𝑥 = 𝑓 𝑖 + 1, 𝑗 − 𝑓(𝑖, 𝑗)

∆𝑦 = 𝑓 𝑖, 𝑗 + 1 − 𝑓(𝑖, 𝑗)

This is illustrated in the Figure 5.1.

Figure 5.1 Geometry of Gradient Filter Measuring Gradient and Magnitude

In the gradient filter (Sobel and Prewitt), the presence of a gradient discontinuity

may be detected by calculating the change in the gradient at (i, j) by measuring the

magnitude (M) and the gradient direction (ɵ). 𝑀𝑎𝑔𝑛𝑖𝑡𝑢𝑑𝑒 𝑀 = ∇𝑥2 + ∇y2 , and the

gradient direction may be estimated using 𝜃 = tan−1(Δ𝑦

Δ𝑥). The gradient operators

perform a 2-D spatial gradient measurement on the image. Typically, it is used to find the

approximate absolute gradient magnitude at each point (i, j) of an input image. For

example, as explained above, Sobel edge detector uses a pair of 3 x 3 convolution masks,

one estimating gradient in the x-direction and the other estimating gradient in y- direction.

Since the operator is local, the convolution is usually much smaller at pixel levels than

the actual global image. As a result, the mask is slide over the image manipulating a

square of pixels at a time. The mask slides over an area where the input image changes

with that pixel‘s value and then shifts one pixel to the right and continues to the right

until it reaches the end of the row, then automatically starts again at the beginning of the


next row. The Gx mask highlights the edges in the horizontal direction while the Gy

mask highlights the edges in vertical direction. After taking the magnitude of both, the

resulting output detects edges in both directions.

It is important to note that pixels in the first row and last row, as well as the first

and last column cannot be manipulated by a 3 x 3 mask. For example, when placing the

centre of the mask over a pixel in the first row, the mask will be outside the image

boundaries. This may result in some loss of information, though minimal, in the output

image.

5.4 Design and Implementation of Edge Detection Algorithms

The design of gradient filters involves detection of edges by computing maximum

and minimum spectral value in the first order derivative of the selected RSI. In the

present analysis Sobel and Prewitt operators are implemented on RSI database showing

three different environment domains such as urban, coastal and landuse features to

understand their significance in terms of extraction of information in feature space from

RSI under different environment settings.

The Sobel and Prewitt operators are discrete differentiation operator, computing

an approximation of the gradient of the image intensity function. The mask of the

operators helps in reducing the error due to the effects of noise by local averaging within

the neighborhood of the mask. An advantage of using a mask of odd size is that the

operators are centered and can therefore provide an estimate that is based on a center

pixel (i, j).

As explained earlier, both the operators have common approach in handling the

image except their kernel values which would attempt to produce different results. They

calculate the gradient of the image intensity of each pixel of the selected RSI data

determining the direction of the maximum value (or brightness) to the minimum value (or

darkness). This may result in an abrupt change in the image intensity or smooth the image


intensity of that pixel and based on the orientation of the edges liner objects or features as

well as the edges are extracted. They also bring out the orientation of edges, which is

very significant in terms of disaster assessment and resources assessment apart from

urban studies. At each pixel of the image, the gradient vector points to the direction of

largest possible intensity increase, and the length of the gradient vector corresponds to the

rate of change in that direction, implying a region of image intensity growing linearly

from darker to brighter values. While designing the algorithm for Sobel and Prewitt

operators, they may further be explained as follows.

The mask or window of 3 x 3 pixels is placed on the selected pixel (i, j) of the

image data (RSI), it may appear as follows and the partial derivative may be computed as

𝐺𝑥 = 𝑎2 + 𝑐𝑎3 + 𝑎4 − (𝑎0 + 𝑐𝑎7 + 𝑎6) and

𝐺𝑦 = 𝑎6 + 𝑐𝑎5 + 𝑎4 − (𝑎0 + 𝑐𝑎1 + 𝑎2)

where ‗c‗is the constant given to pixels closer to the center of the mask.

If c = 1, then Prewitt operator could be derived and if c =2, Sobel‘s, operator

could be derived as shown in the table 5.1.

Table 5.1 Approximation of Prewitt and Sobel Operators

It could be noted that 𝐺𝑥and 𝐺𝑦 are approximation for both the operators at for pixel (i, j).

Algorithm for both Sobel edge detection operators:

𝑎0 𝑎1 𝑎2

𝑎7 i,j 𝑎3

𝑎6 𝑎5 𝑎4

Prewitt

Sobel

𝐺𝑥 𝐺𝑦 𝐺𝑥 𝐺𝑦

-1 0 1 -1 -1 -1 -1 0 1 -1 -2 -1

-1 0 1 0 0 0 -2 0 2 0 0 0

-1 0 1 1 1 1 -1 0 1 1 2 1


Input: Selected RSI showing predominantly specific environment setting – urban, coastal

and landuse

Output: Output image linear features and edges

Step 1: Read the input image

Step 2: Apply mask𝐺𝑥 ,𝐺𝑦 to the input image

Step 3: Apply Sobel edge detection algorithm and the gradient

for i = 1 to size(c,1) do

for j=1 to size(c,2) do

Gx=((2*C(i+2,j+1)+C(i+2,j)+C(i+2,j+2))-(2*C(i,j+1)+C(i,j)+C(i,j+2)));

Gy=((2*C(i+1,j+2)+C(i,j+2)+C(i+2,j+2))-(2*C(i+1,j)+C(i,j)+C(i+2,j)));

end for

end for

Step4: Masks manipulation of 𝐺𝑥 𝐺𝑦 separately on the input image

Step 5: Results combined to find the absolute magnitude of the gradient

G = abs (Gx2 + Gy

2)

Step 6: The absolute magnitude is the output edges and shown as image output

Algorithm for Prewitt edge detection operators:

Prewit mask for x-direction:

Input: Selected RSI showing predominantly specific environment setting – urban, coastal

and landuse

Output: Output image linear features and edges

Step 1: Read the input image

Step 2: Apply mask𝐺𝑥 ,𝐺𝑦 to the input image

Step 3: Apply Prewitt edge detection algorithm and the gradient

for i = 1 to size(c,1) do

for j=1 to size(c,2) do

Gx=((C(i+2,j+1)+C(i+2,j)+C(i+2,j+2))-(C(i,j+1)+C(i,j)+C(i,j+2)));

Gy=((C(i+1,j+2)+C(i,j+2)+C(i+2,j+2))-(C(i+1,j)+C(i,j)+C(i+2,j)));

end for

end for


Step4: Masks manipulation of 𝐺𝑥 𝐺𝑦 separately on the input image

Step 5: Results combined to find the absolute magnitude of the gradient

G = abs(Gx2 + Gy

2)

Step 6: The absolute magnitude is the output edges and shown as image output

The resultant output image is studied to understand the effectiveness of extracting

linear features of both operators under different environment setting, which is discussed

in the following sections.

5.5 Significance of Edge Detection on the Image

Linear features from RSI were identified by exploiting detection of edges among

features or boundaries. In other words, it is an identification of sharp discontinuities in an

image and while doing so some features or objects grow linearly and stand out from other

objects in the image. These objects by their linearity and continuity may be identified and

extracted as linear features for many applications in RSI domain. There are many

techniques that depend upon some form of preprocessing to enhance edges or lines, or

segment the imagery into homogeneous regions so that specific objective pertaining to

application domain may be carried out [SI87].

Remotely sensed data and the land cover/land use classification of urban areas

exploit edge detection to identify liner features of their own requirements for specific

applications [HM95; HLK01]. Semi-automatic linear feature extraction from digital

images could be utilized for GIS data capture for urban information by either a dynamic

programming approach or by Least Square B-spline (LSB) snakes [GL97]. Gradient

profile algorithms are used to extract linear features such as roads and railways in an

urban environment [WZ00]. Also, many of the edge detection methods from satellite

image are significant in terms of texture analysis, three dimensional surfacing,

segmentation, and image matching and image fusion. Similarly [RG01] explained the

importance of using edges rather than individual pixels for comparing pairs of RSI and

image fusion. Again, the finding may lead to change detection studies that significantly


utilize the multi-temporal characteristic of RSI, studying temporal images to observe and

monitor dynamic features and objects, which could be crucial in planning and

developmental activities.

Similarly, 1 m high resolution satellite image could be used to detect the road

center lines using semi-automatic edge detection algorithms [KPJ04]. Such studies using

RSI could be of immense significance in term of traffic management, vehicle tracking,

road maintenance and related infrastructure developmental activities in an urban

environment. Extraction of curvi-linear features and their related information from RSI is

another significant application that could be applied on urban centers to study road

networks, highways and canals [MSK08].

Many of the linear feature extraction studies from RSI using various edge

detection techniques have been focusing upon extraction of roads, railways in an urban

environment, rivers in agricultural landuse environment and coastline that separated land

and sea in coastal area. In the present study, gradient filters (Prewitt and Sobel)

implemented on the image bring out many linear features such as coastline, roads, canals,

apart from boundaries such as buildings and agricultural fields.

5.6 Implementation and Discussion of Edge Detection on the Image

In the present study implementation of gradient operators Prewitt and Sobel are

applied on RSI data base to study their significance in extracting linear features using

edge detection algorithms. Edges play an important role in many applications of image

processing, in particular for machine vision systems that analyze scenes of man-made

objects under controlled illumination conditions. Edges detected in the image aid

significantly in reducing the amount of data and filters out useless information, while

preserving the important structural properties in the image and play the role of knowledge

management. For a comparative assessment RSI showing three predominant features in

the environment application domain - urban features, coastal features and landuse

features – are selected and implemented with the operators for their significance in


extracting quantum of information on linear features. They are discussed in the following

sections.

5.6.1 Extraction of Linear Features on the Urban Image

Edge detectors are preliminarily important tool in identifying and extracting linear

features in RSI data that could be used as basis for specific investigation. They provide

general information of RSI in the feature space augmenting further site specific analysis

and provide direction to the applicability of RSI in environmental domain. In the present

analysis using RSI of urban area, gradient filter using Prewitt algorithm highlighted the

boundary of many of the urban objects and delineated them separately. Initially, the

Prewitt operator runs from top left to right lines after lines of the image in X – direction.

At the end of the image, it again the kernel of the operator again moves from top left in

Y-direction in similar fashion. A sample output of the filter showing Y-gradient is shown

in Figure 5.2A. Finally, both X and Y gradients are added and the resultant image is

shown in Figure 5.2B.

Figure 5.2 A. Prewitt Y-gradient Filter

Image of the Urban Features

Figure 5.2 B. Prewitt XY Gradient


Roads are the prominent feature depicted in the output image of the gradient filter

in the Y-direction. Constructed structures have also been brought out from the image.

Boundary lines of many structures such as buildings and fields are also extracted from the

image. At the center of the image, the business center of the urban feature is highlighted.


They are of constructed in nature and show high spectral reflectance showing higher DN

values in all the three bands (RGB) especially near red and near-infrared region. The DN

range values of some of the urban features and their resultant values of Prewitt filter

extracting linear features is given in Table 5.2.

Such uniform spectral values reflected by dense buildings in the urban center

provide uniformly light grey color and the filters have less scope in highlighting pixels

and they remain either dark or light. There is also a brighter tone observed in the resultant

output as shown in Figure 5.2A. In the Y gradient image, a relatively brighter tone is

observed showing more contrasting boundary lines in the form of circles. These brighter

tones invariably happened due to the filtering of pixels in the image by Prewitt kernel that

has both stronger spectral values as well as low spectral values. Evidently, when the

satellite image showing urban environment was examined, it showed presence of many

small waterbodies, tanks and ponds around the urban center as well as a canal on the

upper left hand side of the image. Such brighter contrast with the surroundings may be

due to the contrasting spectral signature, a high spectral value as shown by constructed

features such as buildings and roads and a lesser spectral value (DN value) of water. The

observation from the analysis of Prewitt gradient filter showed the distribution and

pattern of features as observed in the urban RSI. Also, the resultant final output image

combining both the X and Y gradients as shown in Figure 5.2B revealed that only

prominent linear features and similar objects could be extracted. This imposes certain

limitation in extracting features from the RSI database. Similarly, Figure 5.3A and 5.3B

is the resultant image derived from implementing Sobel operator on the urban RSI.


Figure 5.3 A. Sobel Y-Gradient Image of

the Urban Features

Figure 5.3 B. Sobel XY Gradient


The resultant image is almost similar to that of Prewitt operator and showed not

much variation in the pattern of objects. Upon examining the resultant image shown in

Figure 5.3B of the Sobel operator, it could be noticed certain minor variation compared to

Prewitt. Certain objects shown in Prewitt at the top right hand side corner of the image

are absent in the Sobel‘s resultant image. This could be well visualized in Figure 5.4.

Figure 5.4 Comparison of Prewitt and Sobel Operators of the Urban Image

Comparative DN values of some of the urban features that are detected

implementing the above filters are shown in Table 5.2.

The distinct pattern of roads and other linear and curvilinear objects including

tanks and ponds are seen more pronounced in the Prewitt operator rather than the Sobel.

While implementing both Prewitt and Sobel operators as edge detector for feature

extraction and boundary delineation of objects in an urban RSI, apparently not much

difference is observed in their overall capability.


Table 5.2 Digital Number Values of the Urban Image and their Extracted Values

S.No Features Blue Green Red

Resultant Value

Prewitt Sobel

1.

2.

3.

4.

5.

6.

7.

8.

9.

10.

Urban Center

Major Road

Minor Road

Canal with water

Industrial Building

Vacant Lot

Barren land

Canal without water

Agriculture

Fallow land

144

106

105

70

107

138

113

90

79

135

113

74

76

36

84

105

84

57

44

104

120

73

80

44

93

106

93

78

98

95

15

14

16

18

2

30

21

11

2

6

13

15

14

16

7

20

23

16

2

7

In short, features that show contrasting spectral values would be brought out

clearly rather than objects showing high density of uniform spectral values.

Linear features of distinct pattern such as roads, canals and tank and ponds are

clearly extracted using Prewitt and Sobel operators though more distinctly while

implementing Prewitt operator. To analyze further, these two operators may be

implemented on RSI showing coastal features and the emerging pattern is discussed in

the following section.

5.6.2 Extraction of Linear Features on the Coastal Image

RSI predominant with coastal features is implemented with the edge detectors –

Prewitt and Sobel operators – to extract linear features and to study the efficiency of such

filters in identifying and extracting features in a coastal environment. Some of the most

complex features or objects are seen along the coastline since it is the zone where tidal

interaction on land is dynamic giving rise to ecosystem and thriving landuse pattern.


Common features along the coastline are beach, coastal dune, waterbodies, saltpan,

scrub, marshy land, soil moisture, barren land, agriculture and plantation crops.

Figure 5.5A. Prewitt- Y Gradient Image of

the Coastal Features

Figure 5.5B. Prewitt XY gradient Image of

the Coastal Features

The Figure 5.5 A and B are derived as the resultant output of implementing

Prewitt gradient operator, depicted the delineation of boundary lines of various objects in

the RSI. In the Figure 5.5 A, not only the boundary lines of objects are extracted but also

the major land features present in the RSI, their quantity as well as the quality. Also, DN

values of some of the prominent coastal features and their extracted values are shown in

Table 5.3.

Extraction of such information is formidable in the process of feature extraction

and subsequent identification of land environment. While examining the Y-gradient

resultant image, the most significant linear feature that is observed is that land and sea

parts are divided and separated distinctly. Linear stretch of beach feature could be

distinctly extracted from this operator. The other linear pattern where moisture or

waterbody is involved could also be extracted. A long linear canal leading to a larger

waterbody at the center of the RSI is distinctly shown. The boundary edge of the canal

displays the banks of the canal and its extension into the larger waterbody. Also, some

curvilinear pattern seen inside the waterbody would indicate the depth of the standing

water as well as the turbidity of water (that is water laden with soil particles or other


weed infestation). Such sort of distinctly emerging pattern from the implementation of

the operator helped not only to extract linear features and other related objects but also

form as baseline information for many applications. For example, the extension or

submergence of banks of the canal and change in the extension of the boundary of larger

waterbody at the center during monsoon or floods could be assessed well in

implementing such edge detecting operators. Though they may not provide substantial

information but throw light on the extent of water spread before and after a storm. Some

field like pattern in the left hand part of the waterbody, with the aid of application domain

expert may be extracted as saltpans. Because of the distinct boundary lines of saltpan

fields, any variations or extension or shrinking in such feature or object may also be

assessed. Since many contrasting features are available within the RSI such as vegetation

whose reflectance is high in red spectral region and low in blue layer whereas the

reflectance of waterbody is high in blue spectral region and less in red providing

invaluable information regarding the qualitative condition of land such as soil moisture,

vegetation and barren land.

The resultant image of combination of X and Y gradient as shown in Figure 5.5 B

depicted the limitation of such operator in extracting certain discrete linear features. For

example, the continuity of shoreline feature is well preserved while the banks of the canal

present at the center of the canal are shown as discontinuous line segments. But, still the

extraction of such features is well appreciated when compared to the urban features as

discussed in the previous section. Moreover, the line segment progressing from sea to

land reflected different land features such as coastal dune and vegetation along the shore

land. And wherever dry up and is present, they are well delineated by the operator. But as

explained earlier, uniform tonal and textural pattern of various features suppress edges

since edges or boundary lines are enhanced where such variations (DN values) are high.

Sobel operator applied on the coastal RSI and its resultant image are shown in

Figure 5.6 A and B. It reflected a similar pattern as it is in the Prewitt operator with some

minor changes in the pattern of linear features.


Figure 5.6 A. Sobel - Y Gradient Image of

the Coastal Feature

Figure 5.6 B. Sobel- XY Gradient Image

of the Coastal Feature

A cursory appreciation of the image revealed more contrasting and brighter edges

of features than that of Prewitt. The linear pattern demarcating land and sea is much

brighter and stronger and discrete in nature. The boundary lines of almost all the features

along the shoreline are distinctly and vividly extracted by the operator. Many of the

vegetation features along the shoreline such as coastal dune and dune vegetation mostly

plantation crop could be extracted more distinctly than the Prewitt. The feature classes

are identified with the help of domain experts in the application field. Also, many isolated

clusters of linear and curvilinear objects are observed at the top part of the RSI, which are

later identified as vegetation, both agriculture and natural vegetation, scrub. These

features are discriminated and brought out relatively lesser in the Prewitt. The most

spectacular part of a linear feature canal is distinctly highlighted at the bottom part of the

waterbody and could be extracted in discrete fashion. Regarding the other features and

the pattern, Sobel does not show much variation with Prewitt. A comparative sketch of

the resultant output of Prewitt and Sobel operators are shown in Figure 5.7. The figure

evidently illustrates such similarities in the pattern of linear features as well some

distinctive presence of features in Sobel, especially vegetation feature extracted at the top

right hand part of the coastal RSI.


Figure 5.7 Comparison of Prewitt and Sobel Operators of the Coastal Image

Comparative DN values of some of the coastal features that are detected


In short, implementation and analysis of Prewitt and Sobel operators on a coastal

RSI signified that Sobel operator is relatively more significant in bringing out linear

features than the Prewitt. Analysis and implementation of edge detectors on coastal RSI

showed that the Sobel operator performed relatively better in extracting discrete linear

features than the Prewitt operator.

Table 5.3 Digital Number Values of the Coastal Features and their Extracted Values


Resultant Value

Prewitt Sobel

1.

2.

3.

4.

5.

6.

7.

8.

9.

10.

Sea water

Turbid water

Lagoon water

Saltpan with water

Beach

Dune sand

Natural Vegetation

Saltpan

Soil moisture

Plantation

59

107

100

70

255

255

115

255

140

63

21

59

71

52

255

255

107

255

160

45

12

22

22

28

220

227

246

188

147

166

5

14

1

7

82

75

16

10

8

24

6

15

2

4

91

22

17

10

6

28


With this observation, RSI showing a typical landuse and land cover environment

is analyzed to appreciate the significance of the Prewitt and Sobel operators, which is

discussed in the following section.

5.6.3 Extraction of Linear Features on the Landuse Image

Extraction of Land Use Land Cover (LULC) of an area from the RSI is interesting

in terms of understanding their spectral characters and their interaction among

themselves. Features on the earth‘s surface are heterogeneous and are always overlapping

in the spectral domain. Because of such complexity, extraction of features from RSI is an

arduous task. In the present analysis, Prewitt and Sobel operators are implemented on the

selected RSI to extract LULC features and their applicability in extracting them. Some of

the LULC features that are predominant in the selected RSI include agriculture, river,

hill and a waterbody at the center of the image. The implementation and analysis of these

two operators are discussed below.

From the Figure 5.8 A, it could be clearly seen that Y gradient shows brighter

lines around waterbodies. At the top left hand side, the boundary of a large waterbody is

clearly extracted. Moreover, the linear boundary in the downward side of the waterbody

Figure 5.8 A Prewitt- Y Gradient Image

of the Landuse Features

Figure 5.8 B. Prewitt-XY Gradient

Image of the Landuse Features


is clearly implies storage bank. On the left hand side and in the upward side of the

waterbody, a curvilinear boundary line is extracted implying the water catchment area or

the water spread. Such extracted boundary lines especially along the upward side in the

left hand side of the waterbody, helps in assessing the water spread of the tank or

reservoir which has a wide implication in terms of irrigation and drinking water

management. This also helps in measuring the areal extent of the waerbody. Such

information when studied periodically from RSI using change detection techniques would

help in assessing the water storage. Integrating extracted information on the feature from

the RSI with other form of field information could be used to develop analytical models

catering specific water management applications.

The Prewitt operator is successful in extracting the exact boundary of

waterbodies. Similarly, river course in curvilinear shape is also seen. It runs diagonally in

the right hand side of the RSI in a discrete fashion. Also, another small segment of river

course, in the top left hand part of the RSI, is extracted connecting to the water body.

Another noticeable pattern is complete absence of features in the top right hand part of

the image. When original RSI is examined presence of a hill is observed in that particular

part of the image. Because of the uniform tone and spectral values (DN values) of such

larger objects, the scope for enhancing and highlighting them is minimal. Hence, the

features in that particular part of the image are not properly extracted. In this fashion,

edge detectors show certain limitation in extracting features from the RSI. The extracted

features clearly depict the nature of LULC in the selected RSI. Many of the features

detected during the Y directional kernel movement are not extracted during the XY

combined final output image. This is especially true that the features detected in the right

hand corner of the image in Y-directional output image are not observed in the final

output image. This may be limitation of the Prewitt operator but still it acts as a

significant tool in extracting linear features for preliminary investigation purposes.

Sobel operator, similar to the Prewitt operator, showed similar trends and pattern

of features. But the Sobel operator apparently generated image showing high contrast

among features, especially discrete features. As explained earlier, objects of contrasting


Figure 5.9 A. Sobel- Y Gradient Image

of the Landuse Features

Figure 5.9 B. Sobel-XY Gradient

Image of the Landuse Features

spectral values (DN), for example sand (high reflectance) and river water (low

reflectance), are brought out significantly. When this pattern is continuous, discrete

objects of this nature are vividly highlighted. Same pattern is observed for the waterbody

at the top of the image. Its boundary is well extracted owing to the reflectance behavior

of objects along the bank and objects adjacent to the waterbody. Also, a small segment of

linear feature joining the waterbody is also extracted at the top left hand corner of the

image, which is later identified as a river feeding the waterbody. A similar fractured

diagonally oriented linear pattern below the waterbody is also observed, which may be an

extension of some canal from the waterbody. Because of some spectral mixture, only part

of the canal is extracted while implementing the Sobel operator on the RSI. On the right

hand side of the canal, a discontinuous linear pattern is extracted, which may be

indicating presence of agricultural vegetation along the bank. The extracted linear pattern

and features implementing Sobel kernel operator is almost similar to that of Prewitt

operator. Figure 5.10 illustrates the pattern of extracted features implementing Prewitt

and Sobel from the select original RSI.


Figure 5.10 Comparison of Prewitt and Sobel Operators of the Landuse Image

Comparative DN values of some of the LULC features that are detected


Table 5.4 Digital Number Values of the Landuse Features and their Extracted Values


Resultant Value

Prewitt Sobel

1.

2.

3.

4.

5.

6.

7.

8.

9.

Crop land

Natural Vegetation

Forest vegetation

Fallow land

Soil moisture

Freshwater Tank

Shallow water

River sand

Barren land

69

121

36

186

146

22

52

239

112

52

93

25

178

148

11

11

237

105

236

233

236

191

191

8

8

219

134

5

7

17

10

13

5

10

44

4

4

7

12

25

17

4

13

10

5

While examining the resultant image of both Prewitt and Sobel, it could be

observed that boundary of waterbody and river is significantly brought out by Sobel

kernel operator relative to the former. Even a discontinuous and fragmented part of a

canal is more distinct in Sobel output rather than Prewitt. Also some patches of

agricultural vegetation along the river bank are highlighted in the Sobel output image. At

the same time, extraction of natural vegetation observed at the hilly part in the right hand

side corner of the image is significant in Prewitt output image rather than Sobel. This sort

of observation may lead to the inference that Prewitt attempts to extract features even in a


more homogeneous environment whereas Sobel mostly in heterogeneous while extracting

LULC features from the RSI. This may further be compared statistically as discussed in

the following section.

5.7 Comparison of Edge Detection Technique on Different Environment

The statistical appraisal of the resultant output image as derived from

implementing Prewitt and Sobel may throw some significant knowledge on the spectral

character of features and the applicability of these operators in extracting them.

In this comparative analysis, the general pattern of spectral values of urban

features predominant RSI, coastal features predominant RSI and LULC features

predominant RSI is carried out to understand the degree of difference among the various

RSI. While examining the values of mean and standard deviation of Prewitt and Sobel, it

is determined that the latter showed at least one numerical value greater than the Prewitt

signifying a relatively higher degree of enhancement and extraction of features, though

the value is small (Table 5.5) and is depicted in Figure 5.11.

At the same time, an interesting pattern emerged while examining even the lesser

forming mean and standard deviation values as derived from Prewitt operator of the

selected three different RSI. It revealed that RSI predominant with urban features showed

a lesser mean and standard deviation values contrary to the high reflecting spectra nature

of the urban objects, than the other two RSI of coast and LULC. A tabular column

showing difference in such values among the selected three RSI is given below in

Table 5.6.

The Table 5.6 clearly explains the statistical deviation shown by pixels after

implementing the edge detectors to extract features from the selected RSI. The statistical

values represent the general trend of the DN values or in other words spectral characters


Table 5.5 Mean and SD Values of the Spectral Values among the three Images

Band

Prewitt Sobel

urban coastal LULC urban coastal LULC

Band 1 7.098 15.412 20.609 7.289 15.855 21.177

Band 2 8.157 26.159 27.54 8.358 26.87 28.282

Band 3 7.384 23.722 25.746 7.581 24.364 26.428

SD-Band 1 7.434 20.057 20.057 7.521 20.595 20.595

SD-Band 2 7.087 29.291 29.291 7.193 30.224 30.224

SD-Band 3 6.819 25.853 25.853 6.886 26.691 26.691

Figure 5.11 Bar Chart Showing Mean and SD Value of Prewitt and Sobel Operators

of features after applying the weighted kernel values by the two operators. The resultant

values (mean and standard deviation) from the output image would imply the general

distribution of the pixel values in the three layers. Also, it could be observed from the

resultant output image

M

E

A

N

&

S

D


Table 5.6 Difference in Mean and Standard Deviation of Spectral Values among the

three Images Showing a) Urban Features, b) Coastal Features, and c) LULC

Features

Extracted

DN values Urban Coast Diff. Urban LULC Diff. Coast LULC Diff.

Band 1 7.098 15.412 -8.314 7.098 20.609 -13.511 15.412 20.609 -5.197

Band 2 8.157 26.159 -18.002 8.157 27.54 -19.383 26.159 27.54 -1.381

Band 3 7.384 23.722 -16.338 7.384 25.746 -18.362 23.722 25.746 -2.024

SD-Band1 7.434 20.057 -12.623 7.434 18.486 -11.052 20.057 18.486 1.571

SD-Band2 7.087 29.291 -22.204 7.087 23.95 -16.863 29.291 23.95 5.341

SD-Band3 6.819 25.853 -19.034 6.819 22.439 -15.62 25.853 22.439 3.414

of both Prewitt and Sobel operators that most of the features are not enhanced and

suppressed showing black color implying the pixels value at that xy position is zero.

Because of this reason, the processed image shows smaller statistical values, especially

mean value as it has been obtained after processing the image. So, it would be sufficient

to read the pattern of the resultant values rather than the real values as the image has been

processed to extract features implementing gradient operators. The bar graphs shown in

Figure 5.12 A, B and C illustrate such pattern in a spectacular way allowing the analyst to

understand the influence of predominant features present in each image.

From the Figure 5.12A, a comparison between urban predominant gradient

filtered output image with coastal features showed a vast difference in all the three layers.

From the bar graph, it could be noted that the values, both mean and standard deviation,

are very less compared to the output image obtained from coastal RSI. This would imply

the limitation of the Prewitt and Sobel algorithms in extracting urban features. This

would also imply the applicability of such algorithms in extracting features where the

spectral property is linearly uniform (less tonal variations) and presence of high

frequency of similar objects (textural property of the image indicating density of the

objects). This observation is well reiterated by the relatively higher value shown by the

output image of RSI predominant with coastal features. This lead to the inference of

applicability of such algorithms in separating various


A. Urban and Coastal features B. Urban and LULC C. Coastal and LULC

Figure 5.12 Spectral Difference among the Features in different Images

Note: 5.11 A - Blue color bar represents urban; red bar represents coast; In 5.11 B – blue is urban and red

is LULC. In 5.11 C – blue is coast and rd is LULC. Green colored bar in the above three figures indicates

the difference between the RSI.

features where the tonal variation is very high as well the textural property of the image is

coarse signifying fabric of different elements. Thus, from such pattern observation

regarding the efficiency of the algorithms could be inferred.

Secondly, Figure 5.12B signifies variation in the statistical pattern between urban

features extracted by the gradient filters – Prewitt and Sobel - and the land use features.

Here again, a similar trend is observed with land use features recording a higher

statistical value than the urban features. Also, it could be observed that the difference

between the mean value of urban and land use features is slightly higher that the

difference shown by urban features with the coastal features. This may lead to the

implication of more diverge features has been extracted from the satellite image showing

land use features, as well the degree of continuity of such features. It is evidently proved

by extracting the boundary of a large waterbody at the center of the RSI without showing

any discontinuity and a discrete feature, river, at the right hand side of the resultant

landuse output image. Such a finer extraction of boundary lines of large objects and

linear features are highly appreciated and reflected in the statistical output.


Lastly, comparing the output of the RSI predominent with almost similar objects

coast and landuse (Figure 5.12C) revealed an interseting pattern. Though there is a

marginal higher mean value shown by RSI of landuse features than the coastal features,

the observation on the pattern of standard deviation showed an increased value exhibited

by coastal features over the landuse features. This may be owing to the presence of larger

waterbody at the center of the coastal RSI as well as the large part of the image is covered

by sea showing zero value whie implementing algorithms and because of this the mean

value might have been slightly lesser than the landuse RSI. This clearly indicated the

significance of pixel value in spectral domain in extracting features using image

processing tools such as edge detection algorithms.

5.8 Summary

In the present analysis of extracting features implementing edge detection

algorithms – Prewitt and Sobel – it has been shown that the significance of understanding

features in spectral domain is important. To understand and appreciate such characters of

features in spectral domain, RSI database of three different terrain environments are

implemented with the edge detection algorithms to evaluate their applicability in

extracting and understanding linear features.

In the urban environment, it is observed that distinct pattern of roads and other

linear and curvilinear objects including tanks and ponds are seen more pronounced in the

Prewitt operator rather than the Sobel. The limitation of the Prewitt and Sobel algorithms

in extracting urban features has also been brought by extracting features where the

spectral property is linearly uniform (less tonal variations) and presence of high

frequency of similar objects (textural property of the image indicating density of the

objects).

Implementing Prewitt and Sobel algorithms on a coastal RSI signified that Sobel

operator is relatively more significant in bringing out linear features than the Prewitt.

Analysis and implementation of edge detectors on coastal RSI showed that the Sobel


operator performed relatively better in extracting discrete linear features such as beach,

dune sand, and vegetation than the Prewitt operator.

The results of implementing both Prewitt and Sobel algorithms to extract LULC

features, even a discontinuous and fragmented part of a canal is more distinct in Sobel

output rather than Prewitt including some patches of agricultural vegetation along the

river bank. At the same time, extraction of natural vegetation is observed in Prewitt

output image rather than Sobel. The analysis of land use RSI having land use

predominant features led to the inference that Prewitt attempts to extract features even in

a homogeneous environment whereas Sobel mostly in heterogeneous. The observation

and analysis of statistical parameters also implied that tonal variations (variation in terms

of DN values) and textural variation (in terms of frequency of same DN value) play a

significant role in extracting features while implementing edge detection algorithms.


extraction of linear features by edge detection...

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