int. j. remote sensing, 1998 , vol. 19 , no. 9, 1695 ± 1709
A segmentation approach to classi® cation of remote sensing imagery
B. KARTIKEYAN
Space Applications Centre, Ahmedabad-380053, India
A. SARKAR
Department of Mathematics, Indian Institute of Technology,
Kharagpur-721302, India
and K. L. MAJUMDER
Space Applications Centre, Ahmedabad-380053, India
In this paper we propose a new approach for land cover classi® cation
of remote sensing imagery. It is a two-stage technique, where in the ® rst stage
the global feature-based technique of histogram thresholding generalized to multidimensional cases developed by Khotanzad and Bouarfa (1990 ) is used, and in
the second stage a local feature-based region growing technique is generalized to
grow multiple non-contiguous regions in parallel. The Khotanzad and Bouarfa
technique has the advantage of being a non-iterative unsupervised classi® cation
technique, but su € ers from a failure to detect regions of small spatial extent which
may have high local contrast but low weightage in the global feature space. Our
proposed technique divides the image into blocks of suitable size so that regions
of small spatial extent are detected in the block’s histogram, and they are grown
across neighboring blocks. The proposed technique is illustrated with actual
remote sensing imagery. A number of choices of feature space for the ® rst stage,
and di € erent measures of similarity for the second stage were investigated on
remote sensing data, both visually as well as quantitatively in terms of classi® cation accuracy. It was found that the xyz colour space (Ohta et al. 1980 ) for the
® rst stage, and the J± M distance for the second stage similarity measure, gave
the best results in terms of classi® cation accuracy. Though the approach is
unsupervised and non-iterative in nature, it has given a classi® cation accuracy of
better than 91 per cent for a ® ve-class landcover classi® cation.
Abstract.
1.
Introduction
Segmentation of colour images is an important step in the analyses for data from
remote sensing, biology, medical imaging, etc. Although there has been a lot of
development in segmentation of grey tone images in these ® elds and other ® elds,
like robotic vision, there has been little progress in segmentation of colour or multiband imagery. In the ® eld of remote sensing the ® nal objective of data analysis may
range from a simple level-1 landcover classi® cation to speci® c applications like vigour
estimation of vegetation, deforestation study, ¯ ood mapping to name a few. However,
the ® rst step in analysis is classi® cation, i.e., to infer on the re¯ ectance spectrum of
landcovers. To perform the classi® cation the input data is obtained by choosing
suitable regions ( bands) in the electromagnetic spectral domain and the re¯ ected
light in these bands was recorded by space-borne sensors. The segmentation
approach to classi® cation has so far been mostly popular in the panchromatic
image application areas, and there has been only limited progress in such
approaches for analysing colour imagery and remote sensing imagery. Remote
0143± 1161/98 $12.00
Ñ
1998 Taylor & Francis Ltd
1696
B. Kartikeyan et al.
sensing imagery being a generalization of colour imagery, the proposed approach
in this paper may be applied to any ® eld of multi-spectral image analyses.
The basic problem of image segmentation was initially considered for B&W
(single band or intensity or grey tone) images and some interesting approaches have
evolved which have also been attempted for colour imagery ( Trahanias and
Venetsanopoulos 1993, Nevatia 1977, Robinson 1977, Zenzo 1986, Cumani 1991,
Ohta et al. 1980, Goldberg and Shlien 1976, 1978, Dymond 1993, Jackson 1983,
Justice and Townshend 1982, Skidmore 1989, Cross et al. 1988, Thomas et al. 1987,
and Palylyk and Crown 1984 ). The approaches to segmentation can be divided into
two categories: (1 ) local behaviour based, and ( 2 ) global behaviour based.
In the ® rst category the variation of feature (grey tone or colour) in a small
neighbourhood is analysed, and the inferences in each neighbourhood are integrated
to obtain a segmentation. There are again two ways in which the local behaviour
can be analysed: (a ) edge detection, and (b ) region growing.
The edge detector for grey level images is usually a mask of small size which is
convolved with the neighbourhood of a pixel to obtain a response which would be
high in the presence of an edge at the pixel. In Nevatia ( 1977 ) and Robinson ( 1977)
the edge detectors of grey level images were applied to each spectral band and the
resulting responses at a pixel are accumulated in a heuristic manner to detect an
edge. Later in Machuca and Phillips ( 1983) it was proposed that the colour image
could be considered as a vector ® eld. N -dimensional edge detectors were then derived
and used in Ohta et al. 1980, Zenzo 1986, and Trahanias and Venetsanopoulos 1993.
In region growing methods a small neighbourhood of pixels is tested for some
homogeneity condition based on a suitable region model. The split and merge
technique has been generalized for colour images in (Cross et al. 1988) where they
assume regions of constant tone. In Skidmore ( 1989 ) a region is grown from a single
seed pixel. The most appropriate linearly transformed space to perform segmentation
of colour images is discussed in Ohta et al. ( 1980 ), Jackson ( 1983) and Thomas
et al. ( 1987 ).
The global behaviour-based methods are based on analysis of the data in the
feature space. The basic philosophy behind such methods is that observations from
a single class/ region tend to form a cluster in the feature space i.e., a peak in the
multi-dimensional histogram, and the objective of the analysis is to identify suitable
boundaries to these peaks. Goldberg and Shlien ( 1976, 1978 ), Dymond ( 1993),
Jackson (1983 ), Justice and Townshend ( 1982 ), Thomas et al. ( 1987), Palylyk and
Crown ( 1984 ), and Khotanzad and Bouarfa ( 1990 ) are examples of such methods.
The choice between local and global approach depends upon speci® c application.
In remote sensing there are two types of approaches in the feature-space based
methods: ( 1 ) supervised, and (2 ) unsupervised. In the supervised classi® cation techniques, (see Reeves et al. 1975, Dymond 1993 ), the location and spread of each
cluster is assumed known, and the appropriate boundary in the feature space is
derived based on some criterion like the maximization of likelihood. In the unsupervised methods, (see Goldberg and Shlien 1976, 1978, Justice and Townshend 1982,
Palylyk and Crown 1984, and Khotanzad and Bouarfa 1990 ), which may be considered as generalizations of the global feature based techniques of histogram thresholding methods to multi-dimensional case, very little is assumed to be known about the
regions a priori . The cells in the histogram space are clustered either on some distance
criterion like NN (Goldberg and Shlien 1976, Justice and Townshend 1982, Palylyk
and Crown 1984) or on relative probability of occurrence ( Knootz et al. 1976,
A segmentation approach to classi® cation of remote sensing imagery
1697
Goldberg and Shlien 1978). Among them the approach of Knootz et al. ( 1976) has
the advantage of being a non-iterative technique, and is a natural generalization of
histogram thresholding. However, it also su€ ers from the same problems of histogram
thresholding methods, namely a failure to detect regions of small spatial extent, but
high local contrast. To circumvent these limitations we developed a two-stage
approach of segmentation where in the ® rst stage the histogram-based clustering is
applied to sub-images of appropriate size, and in the second stage a region growing
type of technique is developed to combine the clusters in the neighbouring subimages. The second stage may be considered as a generalized region growing procedure where many regions are grown in parallel. The proposed method in this paper
is shown to be suitable for low detail ( large region sizes) and high detail (small
spatial regions) image segmentation and is suitable for segmenting any n -band
imagery. Our method is illustrated by examples of satellite multi-spectral imagery
and their performance is evaluated.
2.
Histogram-based clustering method
Khotanzad and Bouarfa ( 1990 ) have implemented the graph theoretic clustering
technique of Knootz et al. ( 1976) for clustering in the histogram textural features
for texture dominant images, and have also demonstrated its use on colour imagery.
We brie¯ y describe the technique for completeness.
For colour images the 3-dimensional histogram with each colour (red, green and
blue) feature quantized to some given resolution is constructed from the image. The
nonempty cells of the histogram are segmented into `hills’ by the steepest ascent
method. This method in a single band imagery ( 1-dimensional histogram) is complementary or dual to the valley seeking method of ® nding histogram thresholds, and
can therefore be considered as the most natural generalization of global featurebased methods used in single band imagery. One of the most important advantages
of this method is that it is essentially unsupervised, and is non-iterative.
The histogram-based clustering method described above has only one underlying
assumption that the N -dimensional probability density function ( pdf ) of pixels of
each region is unimodal in nature. No assumption is made on the speci® c form of
the pdf. The independence of the form of the pdf is especially useful in dealing with
remotely-sensed images of the Earth’s surface where often the pdf of di€ erent land
covers (regions) are di€ erent. For example the pdf of urban pixels may not be
Gaussian whereas those of agricultural areas may be Gaussian. It may be noted here
that regions obtained by this procedure are contiguous only in the feature space,
and need not be so in the spatial domain. However, this is a useful property in
dealing with remotely-sensed images. For example all pixels corresponding to spatially noncontiguous settlements/ built-up areas will cluster in the feature space. A
colour edge detector will not be able to cluster them into one region.
The performance of the proposed clustering method tends to deteriorate as we
consider larger and larger images due to smoothing of the histogram. To avoid this
loss, we propose a region growing technique by considering the regions obtained in
clustering over small areas. We call this the second stage of colour segmentation.
3.
Region growing
The second stage for segmentation is found necessary for analyses of large images
as mentioned earlier. In such cases we consider an optimal block size of say NB by
N B such that the histogram clustering technique of the ® rst stage is appropriate in
1698
B. Kartikeyan et al.
any NB by NB subimage of the large image considered. By optimum we mean that
N B should be small enough to bring out small sized high local contrast regions, and
large enough to faithfully bring out large sized regions of low contrast. The arbitrariness in the choice of NB is similar to the arbitrariness of mask sizes in edge operators.
To proceed with the second stage let us assume that a suitable NB has been chosen.
The basic philosophy behind the second stage is the same as that for convolutionmask-based region growing/ edge detection techniques. The input image is overlaid
with a rectangular grid where each cell is of size N B by N B (see ® gure 1). In each
grid cell the pixels are given their local region numbers/ labels using the ® rst stage
clustering procedure. A global region number is assigned to each local cluster as
follows in the second stage.
Consider a grid cell at the i -th grid row and j -th grid column, and call this
subimage G (i , j ). Let the ® rst stage cluster the pixels in this grid into NC (i , j ) regions.
Each region in G (i , j ) is compared for similarity with each region of G (k , l ) where
(k , l ) is a raster-previous neighbour of (i , j ), i.e.,
(k , l ): (k , l )×Neigh(i , j ) = {(i , jÕ 1 ), (iÕ 1 , jÕ 1 ), (iÕ 1 , j ), (iÕ 1 , j+1 )}
( 1)
The method of similarity evaluation will be described in section 4, but for the present
we assume that we can obtain a measure of similarity between a region R of G (i , j )
and R ¾ of G (k , l ). Consider a region R of G (i , j ). Let R ¾ be the region in G (k , l ) which
is most similar to R . If the similarity of R and R ¾ is high enough then the global
region number of R is given the global region number of R ¾ . It often happens that
a single region in G (i , j ) is very similar to regions in two or more neighbouring grid
cells of Neigh (i , j ). In such cases we link the global region numbers of these most
similar neighbouring regions. It may also happen that a given region R in G (i , j ) is
not similar enough to any region in any G (k , l ). In this case we assign a fresh global
region number to that R . The procedure of this second stage begins with G ( 1, 1).
Since ( 1, 1 ) has no raster previous neighbours, all local regions of G ( 1, 1) are given
fresh global region numbers. A raster scan is performed on the grid in which at each
block (i , j ) G (i , j ) is histogram clustered and each of the NC (i , j ) clusters so obtained
are then assigned their global region number by comparison with the clusters in the
raster-previous neighbours Neigh (i , j ). At the completion of the raster scan over the
grid, each pixel in the image has a global region number assigned to it, and, the
Figure 1.
Division of large image into grid cells.
A segmentation approach to classi® cation of remote sensing imagery
1699
global region numbers themselves are clustered into linked lists where each list
corresponds to an actual global cluster. These link lists are collapsed and a ® nal
region number is assigned to each global region number. The output of the raster
scanned image is again scanned pixel by pixel and each pixel is assigned its ® nal
region number.
In this procedure we have generalized the usual region growing technique to
parallely grow multiple regions. The measurement of similarity forms an important
step in this procedure. We now investigate various options available to do this and
evaluate each option.
4.
M easuring cluster similarity
There are two approaches to measure similarity between clusters (a ) parametric,
and (b ) nonparametric.
In a fully parametric approach each cluster is assumed to come from a speci® ed
class probability distribution. The parameters of this distribution are estimated using
the samples constituting the cluster. The similarity of the two clusters is then tested
by evaluating the statistical equality of their estimated parameters. For example, if
the clusters are assumed to come from the class of multi-variate Gaussian distribution
then the parametersÐ mean vector and covariance matrixÐ specifying the particular
distribution are estimated. Let the two regions R and R ¾ have estimated mean vectors
are m and m ¾ , and covariance matrices S and S ¾ respectively. An appropriate measure
of similarity is the j± m distance (Swain and Davis 1978 ) and is given by
jÕ
m (R , R ¾ ) = ( 1/ 8 ) ( m Õ
m¾ )
t
A B
S + S¾
2
(mÕ
m ¾ ) + ( 1/ 2) ln
A
B
|(S + S ¾ )/ 2|
(|S |¯ |S ¾ |)
1/2
( 2)
The smaller the value of this distance the more similar are the regions. There are
two types of disadvantages of such an approach. The ® rst type occurs when the
sample size representing the cluster is small, the estimates of the parameters (e.g. m
and S ) are unreliable. The second type of disadvantage is related to the Type I
(omission) and Type II (commission) errors which occur when the domains of the
underlying pdfs overlap. In spite of these disadvantages, the measure has been shown
to be comparable to other seperability measures ( Thomas et al. 1967 ).
The other approach, which may be called nonparametric, is proposed to tackle
small sample and unknown distribution cases. In such cases the mean vector is the
only reliable estimate, and a simple heuristic like nearest ( Euclidean) neighbouring
mean distance is used as a measure. We de® ne the dis-similarity between R and R ¾
as the Euclidean distance between the mean vectors m and m ¾ of R and R ¾ .
The measures of similarity discussed above are between clusters in the feature
space. In the next section we consider transformations of the original feature space
so that in the transformed space, the clusters are more compact and well seperated.
5.
Colour transforms
An expert in visual interpretation of remote sensing imagery considers tone as
the ® rst step or leading clue for classi® cation of a pixel. The similarity in colour is
better understood in a transformed space like the HSI ( Hue, Saturation, Intensity)
space ( Levkowitz and Herman 1993, Gillespie et al. 1986, 1987, and Daily 1983),
the Tasseled Cap transform ( Reeves et al. 1975 ), and other spectral indices (Ohta
et al. 1980, Jackson 1983, Thomas et al. 1987, and Baret et al. 1987) depending on
the context and objective of the analysis. The measurement of colour di€ erence in
1700
B. Kartikeyan et al.
HSI space has been standardized by CIE (Commission Internationale de L ’Eclairage )
for the human eye and is given by the distance in another transformed space called
the CIELAB coordinates (Agoston 1979 ) given by
AB
CA B A B D
CA B A B D
L = 116
a =500
b =200
X
1/3
Y
1/3
X0
Y
Y0
16
Õ
Y0
Y
Õ
1/3
Y0
1/3
Õ
Z
( 3)
( 4)
1/3
Z0
( 5)
where X , Y and Z are the red, green and blue values of re¯ ectance. Recently another
colour transform (Ohta et al. 1980) is ® nding applicability in colour image understanding. The transformation is given by
x=R Õ
G
( 6)
y=G Õ
B
( 7)
z = (R + G + B )/3
( 8)
In the next section we apply the segmentation algorithm in the original colour
coordinates (RGB ), the CIE-Lab space and in the xyz space, and use each of the
two distance measures for measuring similarity, and evaluate their performance on
di€ erent images.
Figure 2.
False colour composite of IRS LISS-I subimage of size 512 by 512.
A segmentation approach to classi® cation of remote sensing imagery
6.
Figure 3.
Single stage clustering of LISS-I image in the Lab space.
Figure 4.
Two stage clustering of LISS-I image using j± m distance.
1701
Test case
6.1. Data
The two stages of the colour segmentation technique are illustrated here using a
remote sensing image obtained from the IRS-1A LISS-I camera ( Kartikeyan et al.
1994 ). The image is of size 2048 pixels by 2048 pixels, and each pixel represents a
72´5 m by 72´5 m area on ground. Three bands ( band 2-green, band 3-red, and band
1
0
0
0
0
0
0
0
0
0
0
0
42
0
3
83
26
0
0
0
0
0
0
0
0
0
0
3
23
164
0
0
0
0
6
0
0
0
19
0
38
0
14
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
106
0
0
0
0
0
0
0
0
0
0
0
0
0
Landuse classes
0
0
0
0
0
0
0
0
0
0
0
15
0
0
64
0
0
0
8
0
0
0
0
0
0
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
4
24
0
0
32
0
0
0
0
0
0
4
0
0
0
0
0
0
0
0
0
0
0
104
0
0
0
0
0
0
0
0
0
0
0
0
0
5
0
0
0
0
0
0
0
0
0
0
0
0
0
0
54
0
0
0
29
0
0
0
0
0
0
6
Table 1.
0
0
0
235
1
0
0
0
0
71
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
7
0
0
0
0
249
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
8
0
0
0
0
0
0
0
0
0
25
0
0
0
0
28
0
0
0
219
0
0
0
0
0
0
9
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
60
3
86
0
0
10
0
0
0
0
19
0
0
0
0
0
0
0
0
0
0
0
1
0
90
0
0
0
0
0
0
11
0
0
0
0
0
22
0
0
36
0
16
0
26
0
0
0
0
0
0
0
0
0
0
0
0
12
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
50
0
0
0
0
0
0
13
0
0
0
30
0
0
7
0
0
0
0
0
0
0
6
0
0
8
0
0
0
0
0
0
0
14
Confusion matrix of spectral classes versus landuse classes.
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
2
15
20
21
20
20
24
32
20
21
35
22
31
21
35
20
21
20
33
19
23
24
26
24
28
22
29
B2
19
22
17
21
31
49
19
17
54
26
45
21
53
18
23
20
49
16
27
29
34
30
39
28
32
B3
Spectral
mean
51
44
60
28
37
47
18
69
52
40
44
48
52
62
40
42
48
14
38
40
39
36
40
34
28
B4
0
0
0
7
8
12
14
0
12
7
12
2
12
0
1
4
11
14
9
10
10
10
10
7
15
Label
1702
B. Kartikeyan et al.
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
68
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
36
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
18
0
0
0
0
0
0
0
0
0
0
0
0
15
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
101
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
2
0
18
17
7
10
0
0
0
0
1
0
0
0
0
0
0
0
0
0
1
49
1
5
0
0
0
0
0
0
0
0
0
0
0
0
0
0
23
2
25
0
0
0
0
0
146
0
0
0
0
11
0
83
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
24
381
0
0
0
0
0
0
0
0
91
0
0
0
0
0
0
0
0
0
0
0
0
0
6
6
0
0
0
0
0
0
0
7
8
5
0
31
25
30
33
31
19
23
31
24
33
31
33
35
26
26
20
19
36
21
33
31
34
21
20
22
19
37
31
43
49
47
19
25
44
29
45
43
40
40
30
27
20
18
44
21
47
48
51
20
17
22
19
31
38
44
49
46
33
39
44
30
43
41
33
27
25
22
21
15
47
41
48
45
49
17
12
15
17
11
13
12
12
12
7
13
11
13
11
11
11
11
15
15
14
14
11
3
12
12
12
15
15
15
14
A segmentation approach to classi® cation of remote sensing imagery
1703
1704
B. Kartikeyan et al.
4 near infrared) have been used for analysis. A 512 by 512 section of the original
image is shown as a colour composite in ® gure 2.
The various classes present in the area are: 3 classes for Wheat, 2 each for Gram,
Fallow land and Waterbodies, and, one class each for Scrub, Reserve Forest,
Wasteland, Eroded land, Salt-a€ ected land, Sand and Urban areaÐ making a total
of 16 level-2 landcover classes for which ground truth was available.
6.2. Results
Both single stage (only stage-1) and two stage segmentation were applied to
segment the image in each of the three colour spaces, and, in the two-stage case
both measures of dissimilarityÐ NN and j± m distance have been used. To determine
the suitable grid size Nb for the two-stage processing, the image was segmented using
a range of grid sizes from 8 Ö 8 to 512 by 512 with steps in powers of two. A grid
size of 256 by 256 was found to be optimal. A smaller grid size gives too many
regions whose similarity could not be captured by either method. In ® gure 3 the
segmentation result of ® rst stage using Lab feature space on the 512 by 512 section
(of ® gure 2) is shown. The number of clusters is 29 in this case. As seen from this
® gure, only the level-1 landcover classes (Agriculture, Forest, Barren, Urban and
Waterbody) are di€ erentiable, showing that the grid size is large to detect the 16
level-2 landcover classes. In ® gure 4 the results of segmentation using xyz feature
space with 256 by 256 grid size for the ® rst stage, and j± m distance for second stage
is shown. The number of clusters is 45 in this case, and all level-2 classes are
di€ erentiable here.
To evaluate the results of segmentation quantitatively in di€ erent cases, the
ground truth of di€ erent land cover classes have been used as follows. The entire
image ( 2048 pixels by 2048 pixels) was segmented according to the nine casesÐ the
® rst three corresponding to segmentation by Khotanzad’s algorithm over the entire
image in the three colour spaces, the next three corresponding to proposed two stage
processing using the NN similarity measure, and the last three corresponding to the
j± m measure for similarity. In each case the resulting segmentation assigns a cluster
number or spectral class number to each pixel. To evaluate the result each global
cluster number is to be given a label corresponding to the land cover class that it
belongs, and this is called the labelling problem. We have adopted the following
strategy to allocate labels to clusters. For each sample in the Ground truth of each
land cover class C g , the global cluster number Cs is noted. A matrix (similar to the
confusion matrix) is formed where the row corresponds to the C s and the column
corresponds to the C g . The entry in the C s row C g column is the number of samples
of land cover class C g assigned global cluster number C s by the segmentation
procedure. Under ideal conditions each row C s of this matrix should contain at the
most only one non-zero entry at column C g in which case the cluster C s may be
thought to represent a spectral subclass of the ground class C g . By this consideration
each cluster C s is given the global label C g if in row C s the maximum value appears
in column C g . For example, the confusion-like matrix obtained using the xyz transform and j± m distance as measure of dissimilarity is shown in table 1. Only nonnull rows of the matrix have been shown in this table. The last column of the table
indicates the label allocated to each cluster.
Each of the landuse classes for which ground truth is available is a subclass of
one of the ® ve land cover classesÐ Agriculture, Forest, Barren, Urban or Water. The
5 by 5 confusion matrices of land cover classes for the single stage segmentation in
A segmentation approach to classi® cation of remote sensing imagery
1705
the RGB, Lab, and xyz spaces are shown in tables 2, 3 and 4 respectively as cases
1, 2 and 3. The confusion matrices resulting from using NN ( Nearest Neighbour) as
dissimilarity measure in the three colour spaces are shown in tables 5, 6 and 7
as cases 4, 5 and 6. The corresponding confusion matrices using j± m distance as
dissimilarity measure are shown in tables 8, 9 and 10 as cases 7, 8 and 9. The
classwise omission and commission errors for the 9 cases are shown in table 11.
Table 2.
Confusion matrix for case-1 single stage clustering in RGB colour space.
To
From
A
F
B
U
W
Table 3.
A
F
B
U
W
983
317
0
0
4
0
0
0
0
0
0
6
1146
290
16
0
0
0
0
0
0
0
0
0
563
Confusion matrix for case-2 single stage clustering in lab colour space.
To
From
A
F
B
U
W
Table 4.
A
F
B
U
W
972
104
1
0
0
10
217
104
21
7
0
0
1041
207
5
1
2
0
62
1
0
0
0
0
570
Confusion matrix for case-3 single stage clustering in XY Z colour space.
To
From
A
F
B
U
W
Table 5.
A
F
B
U
W
935
1
0
0
4
0
46
0
0
0
48
275
1146
223
13
0
1
0
67
0
0
0
0
0
570
Confusion matrix for case-4 Euclidian distance in RGB colour space.
To
From
A
F
B
U
W
A
F
B
U
W
981
2
163
4
2
0
119
0
0
0
2
0
982
40
4
0
202
1
246
369
0
0
0
0
208
1706
B. Kartikeyan et al.
Table 6.
Confusion matrix for case-5 Euclidian distance in lab colour space.
To
From
A
F
B
U
W
Table 7.
A
F
B
U
W
983
178
744
289
341
0
145
0
0
41
0
0
402
1
1
0
0
0
0
0
0
0
0
0
200
Confusion matrix for case-6 Euclidian distance in XY Z colour space.
To
From
A
F
B
U
W
Table 8.
A
F
B
U
W
983
239
735
207
395
0
84
0
0
0
0
0
411
0
0
0
0
0
83
0
0
0
0
0
188
Confusion matrix for case-7 using J± M distance in RGB colour space.
To
From
A
F
B
U
W
Table 9.
A
F
B
U
W
962
16
191
140
0
0
237
0
0
0
21
70
955
14
8
0
0
0
136
0
0
0
0
0
575
Confusion matrix for case-8 using J± M distance in lab colour space.
To
From
A
F
B
U
W
A
F
B
U
W
845
61
0
3
0
2
262
0
0
23
136
0
1146
209
7
0
0
0
78
0
0
0
0
0
553
6.3. Discussion
For the single stage processing in the three colour spaces (cases 1,2,3 ), Forest
gets confused with agriculture for cases 1 and 2, whereas in case 3 Forest is confused
with Barren. The Barren and Water classes are classi® ed without much confusion,
and, the Urban class is confused with Barren in all three cases (see tables 2,3,4 ). In
the two stage segmentation where Euclidian Nearest Neighbour is used as measure
1707
A segmentation approach to classi® cation of remote sensing imagery
Table 10.
Confusion matrix for case-9 using J± M distance in XY Z colour space.
To
From
A
F
B
U
W
A
F
B
U
W
914
0
28
0
6
0
322
26
0
30
69
1
990
50
2
0
0
102
240
0
0
0
0
0
545
A = Agriculture, F = Forest, B = Barren, U = Urban,
W = Waterbody.
Table 11.
Classi® cation error percentages.
Class
Agri
Case
1
2
3
4
5
6
7
8
9
Forest
Barren
Urban
Water
OE
CE
OE
CE
OE
CE
OE
CE
OE
CE
0´00
1´12
4´88
0´20
0´00
0´00
2´14
14´04
7´02
24´62
9´75
0´11
14´84
61´22
61´59
26´51
7´04
3´59
100´00
32´82
85´76
63´16
55´11
73´99
26´63
18´89
0´31
0´00
39´55
0´00
0´00
22´04
0´00
0´00
8´71
14´81
0´00
9´16
0´00
14´31
64´92
64´14
16´67
0´00
13´61
21´40
16´92
32´79
4´47
0´50
0´00
10´58
23´50
10´97
100´00
78´62
76´90
15´17
100´00
71´38
53´10
73´10
17´24
0´00
6´06
1´47
69´93
0´00
0´00
0´00
0´00
29´82
3´43
2´23
2´23
64´32
65´69
67´75
1´37
5´15
6´52
0´00
0´00
0´00
0´00
0´00
0´00
0´00
0´00
0´00
OE = Omission Error, CE = Commission Error.
of dissimilarity, Urban classi® cation has improved substantially but Forest and
Water are confused with Urban in case 4, whereas, in cases 5 and 6 all classes get
confused with Agriculture. Using j± m distance as measure of dissimilarity in the two
stage segmentation seems to give the best results as seen from tables 8, 9 and 10.
The best results are obtained when the xyz colour space is used.
7.
Conclusions
A two stage segmentation procedure is proposed for segmenting multi-spectral
imagery where in the ® rst stage a non-iterative clustering algorithm is used to
produce natural clusters, and in the second stage the concept of region growing has
been generalized for growing multiple regions in parallel. The algorithms have been
tested on normal and remote sensing imagery in di€ erent colour transform spaces.
The two-stage method seems to perform reasonably well on remote sensing
imagery using j± m distance in the xyz colour space. Considered as an unsupervised
classi® cation technique and comparing with existing unsupervised classi® ers, the
proposed technique has two advantages: (i ) the number of clusters do not have to
be de® ned apriori, and (ii ) the technique is non-iterative. The quantitative evaluation
of performance involves labelling of clusters to land cover classes, and a heuristic
labelling procedure has been adopted for this purpose.
1708
B. Kartikeyan et al.
Further investigations can be carried out on (a ) the labelling problem, (b ) choosing
appropriate technique for testing similarity of clusters, (c) choosing suitable colour
transform, and (d ) choice of suitable block size for processing di€ erent types of images.
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