6-an object-based approach for urban landv (2013)
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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 1
An Object-Based Approach for Urban LandCover Classification: Integrating LiDAR
Height and Intensity DataWeiqi Zhou
AbstractDigital surface models (DSMs) derived from lightdetection and ranging (LiDAR) data have been increasinglyintegrated with high-resolution multispectral satellite/aerial im-agery for urban land cover classification. Fewer studies, however,have investigated the usefulness of LiDAR intensity in aid ofurban land cover classification, particularly in highly developedurban settings. In this letter, we use an object-based classificationapproach to investigate whether a combination of LiDAR heightand intensity data can accurately map urban land cover. Wefurther compare the approach to a method that uses multispectral
imagery as the primary data source, but LiDAR DSM as ancillarydata to aid in classification. The study site is a suburban area inBaltimore County, MD. The LiDAR data were acquired in March2005, from which DSM and two intensity layers (first and lastreturns), with 1-m spatial resolution were generated, respectively.Four classes were included: 1) buildings; 2) pavement; 3) treesand shrubs; and 4) grass. Our results indicated that the object-based approach provided flexible and effective means to integrateLiDAR height and intensity data for urban land cover classifi-cation. A combination of the LiDAR height and intensity dataproved to be effective for urban land cover classification. Theoverall accuracy of the classification was 90.7%, and the overallKappa statistics equaled 0.872, with the users and producersaccuracies ranging from 86.8% to 93.6%. The accuracy of theresults were far better than those using multispectral imagery
alone, and comparable to using DSM data in combination withhigh-resolution multispectral satellite/aerial imagery.
Index TermsBaltimore, high-resolution imagery, intensity,light detection and ranging (LiDAR), normalized digital surfacemodel (nDSM), object-based image analysis, urban land coverclassification.
I. Introduction
ACCURATE and timely information about urban land
cover is essential for urban land management, planning,
and landscape pattern analysis. Remote sensing provides the
primary source of data for urban land cover mapping. As an
Manuscript received November 19, 2012; revised January 8, 2013; acceptedFebruary 27, 2013. This work was supported in part by the Chinese Academyof Sciences One Hundred Talented Program, the State Key Laboratory ofUrban and Regional Ecology, the Ministry of Environmental Protection ofChina under Grant STSN-12-01, and the National Science Foundation LTERProgram (DEB 042376).
The author is with the State Key Laboratory of Urban and RegionalEcology, Research Center for Eco-Environmental Sciences, Chinese Academyof Sciences, Beijing 100085, China (e-mail: wzhou@rcees.ac.cn).
Color versions of one or more of the figures in this paper are availableonline at http://ieeexplore.ieee.org.
Digital Object Identifier 10.1109/LGRS.2013.2251453
urban environment is extremely complex and heterogeneous,
very high spatial resolution remotely sensed data are needed
to adequately characterize the fine-scale spatial heterogeneity
of urban landscape [1]. Consequently, high spatial resolution
satellite and aerial imagery has been frequently used for
detailed urban land cover mapping [2][4].
The recent availability of airborne light detection and rang-
ing (LiDAR) data provides new opportunities for detailed
urban land cover mapping at very fine scales. LiDAR is anactive remote sensing technology, operating in the visible or
near-infrared region of the electromagnetic spectrum. With
the recent advances of airborne LiDAR technology, there is
increasing interest in applying LiDAR data to urban land
cover classification. LiDAR point clouds can be directly used
for urban feature extraction [5]. More frequently, however,
LiDAR points are first interpolated into raster layer(s), and
then combined with high-resolution satellite/aerial imagery
for detail urban land cover mapping. Researchers commonly
used surface height information, or digital surface model
(DSM) derived from LiDAR data as ancillary data to aid
in classification [1], or as the primary data for classification
[4], [6]. Studies have shown that the accuracy of urban landcover classification can be greatly improved by integrating
multispectral imagery with LiDAR data [1], [4], [6].
In addition to height data, LiDAR also provides intensity
data that reflect the material characteristics of land cover
features, which can be potentially used for urban land cover
classification [6], [7]. While LiDAR intensity data have been
increasingly used in forest-type classification [8], only a few
very recent studies have used LiDAR intensity as ancillary
data to aid in urban land cover mapping [4], [6]. Few studies
have investigated the usefulness of LiDAR data alone, i.e.,
a combination of LiDAR height and intensity information,
in urban land cover classification [7], [9], particularly in
highly developed urban settings, where classification is morechallenging due to the fine-scale complexity of urban land
cover features. This letter aims to fill this gap.
Paralleled with the increasing availability of LiDAR data
are the advances in object-based image analysis (OBIA), an
image classification approach that has gained wide acceptance
in fine-scale urban land cover mapping [10]. Rather than clas-
sifying individual pixels, object-based classification segments
the imagery into objects. Consequently, in addition to spectral
response, object characteristics, such as shape and spatial
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2 IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
Fig. 1. Study site: a suburban area in the Baltimore County, MD, USA.
relations, can be used for classification [1], [10]. Many studies
have shown that OBIA techniques are superior to pixel-based
approaches for land cover classification from high-resolution
imagery [10].
In this letter, we used an object-based classification ap-
proach to investigate whether a combination of the LiDAR
height and intensity data can accurately characterize and map
urban land cover. We further compared this approach to a
method that used imagery as the primary data source, but
LiDAR height data as ancillary data for classification.
II. Data and Methodology
A. Study Site
The study site was a suburban area in Baltimore County,MD, USA (Fig. 1). Land use of this area was dominated by
medium- to high-density residential development, mixed with
small proportions of commercial and other institutional land
uses. Land cover features are typical of those in urban and
suburban environments, including detached and multifamily
houses, commercial buildings, paved surfaces, and vegetation
cover. Therefore, the variety of the land use/land cover in the
study site makes it well suited for the goal of this letter.
B. Data Preprocessing
1) LiDAR Data: The LiDAR data were acquired in March
2005. Both the first and last vertical returns were recorded foreach laser pulse, with the average point spacing of approx-
imately 1 m. The returns from bare ground and nonground
(e.g., tree canopy, building roofs) were separated. The LiDAR
point clouds were processed to generate three separate raster
datasets: a normalized digital surface model (nDSM), and two
intensity image layers.
Normalized digital surface model: The points returns from
bare ground were interpolated into 1-m spatial resolution dig-
ital elevation model (DEM), and all returns (i.e., both returns
from bare ground and nonground) into 1-m resolution DSM,
using the natural neighbor interpolation method available in
Fig. 2. Subset of the aerial imagery, LiDAR data layers, and classificationresults. (a) Multispectral emerge imagery. (b) nDSM. (c) First return intensity.(d) Last return intensity. (e) Classification results from Method 1 (shades ofgray from light to dark: grass, trees and shrubs, pavement, and buildings).(f) Classification results from Method 2.
ArcGIS 3-D analyst. The surface cover height model (referred
to as nDSM) was then generated by subtracting the DEM from
the DSM (Fig. 2).
Intensity layers: Two intensity layers were generated from
the first and last return measurements, respectively (Fig. 2).
The natural neighbor interpolation method in ArcGIS 3-D
analyst was used to generate the two 1-m spatial resolution
intensity layers. The mean and standard deviation of intensity
from first returns were 7.83 and 5.99, respectively, with the
range of 0.10 to 472.82; and those from last returns were 9.17
and 5.31, respectively, with the range of 0.14 to 502.32.2) High-Resolution Imagery Data: Color-infrared digi-
tal aerial image data with a pixel size of 0.6 m acquired
in 2004 were used in this letter for comparison purposes(Fig. 2). The imagery was 3-band color-infrared, with green
(510600 nm), red (600700 nm), and near-infrared bands
(800900 nm). The imagery data has an 8-bit radiometric
depth, and was orthorectified [2].
C. Land Cover Classification
In this letter, four land cover classes were identified: 1)
buildings; 2) pavement; 3) trees and shrubs; and 4) grass,
which are the most typical land cover types in urban and
suburban landscapes. Two methods were applied to perform
the land cover classification. Both methods used an OBIA
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ZHOU: OBJECT-BASED APPROACH FOR URBAN LAND COVER CLASSIFICATION 3
approach for classifications, which was implemented using the
software eCognition. The classification procedures, as detailed
below, however, were slightly different because the primary
data sources for classification within the two methods were
different.
1) Method 1: Classification Using LiDAR Data Alone:
Method 1 used LiDAR data alone for classification. A rule
set, a sequence of processing commands/algorithms, was de-
veloped to generate image segments and to classify them intodesired land cover classes [1], [2]. Specifically, a contrast-split
segmentation algorithm was first used to separate tall objects
from short objects based on nDSM [6]. The minimum and
maximum thresholds were set as 6 feet and 9 feet, respectively.
We then further separated tall objects into buildings and
trees, and classified short objects into pavement and grass,
as detailed follows.
Classification of tall objects into buildings and trees. A
multiresolution segmentation was run for the tall objects,
using both nDSM and the intensity layer from the first return
measurements. The multiresolution segmentation algorithm
initialized with each pixel previously classified as tall objects
in the image as a separate segment, which was merged withneighboring segments based on their level of similarity in
subsequent steps. The process stops when there are no more
possible merges given a defined scale parameter. The scale
parameter specifies the maximum heterogeneity that is allowed
within each object, which indirectly controls the size of
objects. The greater the scale parameter, the larger the average
size of the objects. The user can also specify color and shape
parameters to change the relative weighting of reflectance and
shape in defining segments. In this letter, the scale parameter
was set as 10 to conduct the segmentation at a very fine
scale. The color criterion was given a weight of 0.9, while the
shape was assigned with the remaining weight of 0.1, giving
equal weights to compactness (i.e., 0.05) and smoothness. The
scale parameter of 10 and the values for the color and shape
parameters were determined by visual inspection of the image
segmentation results, where objects were considered to be
internally homogenous, i.e., all pixels within an image object
belonged to one cover class [1]. Following the segmentation,
tall objects were classified as buildings if the difference in
intensity from the first and last returns was less than 1, and
standard deviation of nDSM was less than 6 feet. In addition,
tall objects that share boundaries with these previously clas-
sified buildings were further identified as buildings, if: 1) the
standard deviation of nDSM
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4 IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
TABLE I
Error Matrix of the Four Classes, With Calculated Producer,
User and Overall Accuracy for Method 1
Classified Reference data Row TotalUserAcc.(%)
data Tree Pavement Grass Building
Tree 88 0 4 4 96 91.67
Pavement 1 52 4 0 57 91.23
Grass 1 7 86 0 97 88.66
Building 4 0 0 46 50 92.00
Producer 93.62 88.14 91.49 86.79Acc. (%)
Overall accuracy: 90.67%, overall Kappa statistic: 0.872.
TABLE II
Error Matrix of the Four Classes, With Calculated Producer,
User and Overall Accuracy for Method 2
Classified Reference data Row TotalUserAcc.(%)
data Tree Pavement Grass Building
Tree 93 0 4 1 98 94.90
Pavement 0 56 2 3 61 91.80
Grass 5 6 79 1 91 86.81
Building 2 0 0 48 50 96.00Producer Acc. (%) 93.00 90.32 88.76 92.31Acc. (%)
Overall accuracy: 92.00%, overall Kappa statistic: 0.883.
were also far better than those of urban land cover classi-
fication using multispectral imagery alone [11]. One of the
advantages of the LiDAR system over passive multispectral
remote sensing is that LiDAR is an active remote sensing
technology. Therefore, LiDAR nDSM and intensity are not
affected by shadows that can affect a significant proportion of
high spatial resolution imagery in urban areas [2].
The overall accuracy of the classification based on Method
1 was slightly lower than that from Method 2 (Table II). The
accuracies based on Method 1 were also comparable to those
of urban land cover classifications, in which LiDAR data were
integrated with multispectral imagery and existing GIS layers
[4], [6]. These results suggested that with an object-based
classification approach, LiDAR data alone could potentially
be a very useful and effective data source for accurate urban
land cover mapping.
LiDAR data also have some limitations in urban land
cover classification. Using LiDAR data in urban land cover
classification generally requires data processing of LiDAR
point clouds into raster layers, which is relatively computation-
ally intensive. More importantly, the process of interpolating
LiDAR points into raster layers may introduce some uncer-tainty, which may affect the later urban land cover classi-
fication. For example, the accuracy assessment and evalua-
tion indicated that small pieces of pavement surrounded by
grass (e.g., paved sidewalks) were commonly misclassified
as grass. This is likely because the raster layers of intensity
were generated by interpolating the LIDAR sampling points
using the natural neighbor method, which tended to create a
smooth surface. Similarly, tree canopies right next to buildings
sometimes were misclassified into buildings, and the edges
of buildings may be misclassified as trees. In addition, laser
pulses generally cannot penetrate very dense tree canopies.
Consequently, the use of the feature of intensity difference
led to some misclassifications between buildings and trees.
IV. Summary and Conclusion
This research investigated whether the use of LiDAR data
alone can be effectively map detailed urban land cover, using
an object-based classification approach. Our results indicated
that using an object-based classification approach, a combina-
tion of the LiDAR height and intensity data could accuratelycharacterize and map urban land cover. The accuracy of the
results was far better than those using multispectral imagery
alone, and comparable to those integrating LiDAR data with
multispectral imagery and existing GIS layers. The object-
based approach provided a flexible and effective means of
integrating LiDAR height and intensity information for urban
land cover classification, and was superior to a pixel-based
approach. As LiDAR nDSM are relatively consistent and
stable across a heterogeneous urban landscape, and thus allows
for automatic feature extraction for a large region, using
an object-based approach, the integration of LiDAR nDSM
and intensity provides great potential for accurate large-scale
mapping of detailed urban land cover.
Acknowledgment
The authors would like to thank the reviewers for their
helpful comments. Many thanks are given to W. Yu and
Dr. L. Han for their help with accuracy assessment and format
editing.
References
[1] W. Zhou and A. Troy, An object-oriented approach for analysing andcharacterizing urban landscape at the parcel level, Int. J. Remote Sens.,vol. 29, pp. 31193135, May 2008.
[2] W. Zhou, G. Huang, A. Troy, and M. L. Cadenasso, Object-based landcover classification of shaded areas in high spatial resolution imageryof urban areas: A comparison study, Remote Sens. Environ., vol. 113,pp. 17691777, Aug. 2009.
[3] D. Lu, S. Hetrick, and E. Morgan, Land cover classification in acomplex urban-rural landscape with Quickbird imagery, Photogram.
Eng. Remote Sens., vol. 76, pp. 11591168, Oct. 2010.[4] S. W. MacFaden, J. P. M. ONeil-Dunne, A. R. Royar, J. W. T. Lu, and
A. G. Rundle, High-resolution tree canopy mapping for New York Cityusing LIDAR and object-based image analysis, J. Appl. Remote Sens.,vol. 6, pp. 123, Sep. 2012.
[5] K. Zhang, J. Yan, and S. C. Chen, Automatic construction of buildingfootprints from airborne LIDAR data, IEEE Trans. Geosci. RemoteSens., vol. 44, no. 9, pp. 25232533, Sep. 2006.
[6] J. P. M. ONeil-Dunne, S. W. MacFaden, A. R. Royar, and K. C.Pelletier, An object-based system for LiDAR data fusion and featureextraction, Geocarto Int., pp. 116, Jun. 2012.
[7] J. Im, J. R. Jensen, and M. E. Hodgson, Object-based land coverclassification using high-posting-density LiDAR data, GISci. RemoteSens., vol. 45, pp. 209228, Apr. 2008.
[8] A. S. Antonarakis, K. S. Richards, and J. Brasington, Object-based landcover classification using airborne LiDAR, Remote Sens. Environ., vol.112, pp. 29882998, Jun. 2008.
[9] A. Shaker and N. El-Ashmawy, Land cover information extractionusing lidar data, in Proc. XXII ISPRS Congr. Int. Archives Photogram-metry, Remote Sens. Spatial Inform. Sci., vol. XXXIX-B7. Melbourne,Australia, Aug.Sep. 2012, p. 25.
[10] T. Blaschke, Object based image analysis for remote sensing, ISPRSJ. Photogrammetry Remote Sens., vol. 65, pp. 216, Jan. 2010.
[11] N. Thomas, C. Hendrix, and R. G. Congalton, A comparison of urbanmapping methods using high resolution digital imagery, Photogram.
Eng. Remote Sens., vol. 69, pp. 963972, Sep. 2003.
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