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ISSN: 2277 – 9043
International Journal of Advanced Research in Computer Science and Electronics Engineering (IJARCSEE)
Volume 1, Issue 6, August 2012

Image Segmentation in Satellite Image using Optimal
Texture Measures
G.Viji1, N.Nimitha2,A.Kalarani2
1
Assistant Professor, P.S.R.Rengasamy college of Engineering for women, Sivakasi
2
Lecturer, M.Kumarasamy college of Engineering,karur.
2
Assistant Professor, P.S.R.Rengasamy college of Engineering for women, Sivakasi.

Abstract— Texture in high resolution satellite images requires information source and provide current information on a
substantial amendment in the conventional segmentation periodic basis at low cost.
algorithms. In this paper, a satellite image is segmented using Satellite image consists of micro textures and macro
optimal texture measures. Satellite image used in this paper is a textures. For micro textures the statistical approach seems to
high resolution data which will provide more details of the urban
areas, but it seems evident that it will create additional problems
be work well. The statistical approaches have included auto
in terms of information extraction using automatic classification. correlation functions, digital transform, and gray level tone
This work improves the classification accuracy of intra-urban co-occurrence. For macro textures the approach seems to be
land cover types. Four texture measures are evaluated using moving in the direction of using histograms of primitive
grey-level co-occurrence matrix (GLCM). Four texture indices properties and co-occurrence of primitive properties in
with six window sizes are obtained from satellite image. Principle structural and statistical. These techniques are not sufficient to
Component Analysis (PCA) is applied to these texture measures. segment high resolution images due to the variability of
The resultant image is then compared with homogeneity texture spectral and structural information in such images [2].
feature image, obtained using 7×7 window. The per pixel Thus the spatial pattern or texture analysis becomes
classification accuracy is improved in this work by varying the
window size.
necessary to segment high resolution image. The proposed
method is based on the feature extraction from the gray level
Keywords - Gray Level Co-occurrence Matrix (GLCM), Principle co-occurrence matrix, which is a well known method for
Component Analysis (PCA), Remote Sensing, Satellite Image, analysing the texture features. The segmentation based on this
Segmentation. texture features can improve the accuracy of this
interpretation. A problem that frequently arises when
I. INTRODUCTION segmenting an image is that the number of feature variables or
dimensionality is often quite large. It becomes necessary to
Image segmentation plays an important role in human decrease the number of variables to manageable size, at the
vision, computer vision and pattern recognition fields. same time, retaining as much discrimination information as
Segmentation refers to the process of partitioning a digital possible. In this paper an algorithm called principle
image into multiple segments. The goal of segmentation is to component analysis is introduced to solve this problem.
simplify and or change the representation of an image into The paper is organized as follows. First in Section II,
something that is more meaningful and easier to analyse. Proposed Methodology is dealt, Principle Component
Image segmentation is typically used to locate objects and Analysis (PCA) in Section III, Results and discussion are dealt
boundaries (lines, curves, etc.) in images. More precisely, in Section IV. Finally conclusions are given in Section V.
image segmentation is the process of assigning a label to
every pixel in an image such that pixels with the same label II. PROPOSED METHODOLOGY
share certain visual characteristics. In order to better explain
the structure of this work, the preliminary information about The Fig.1 shows that representation of the proposed
the satellite image and remote sensing is discussed [1]. methodology. The proposed methodology consists of two
Remote sensing is a science of obtaining information about steps: Step1: optimal window size and Step2: optimal
an object, area or phenomenon through the analysis of data texture measure. Feature extraction acquired by this
acquired by a device that is not in contact with the object [1]. experiment is derived from gray level co-occurrence matrix.
Commonly remote sensing is referred to the collection and The more details of this texture analysis are shown by the
analysis of data regarding the earth using electromagnetic following subheadings.
sensors, which are operated from the space borne platform.
Satellite image is a remotely sensed one and defined as a A. Gray level Co-occurrence matrix
picture of the earth taken from an earth orbital satellite. This
image consists of buildings, roads, vegetations, water bodies Gray level co-occurrence matrix is the two dimensional
and other open areas. Satellite images are an important matrix of joint probabilities Pd,r(i,j) between pairs of pixels,
separated by a distance, d, in a given direction, r. It can be

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obtained by calculating how often a pixel with gray level Angular Second Moment [6] is a measure of homogeneity
value i occurs horizontally adjacent to a pixel with the value j. of the image. It is high when the GLCM has few entries of
Each element (i,j) in GLCM specifies the number of times that large magnitude, low when all entries are almost equal. This is
the pixel with value i occurs horizontally adjacent to a pixel the opposite of entropy. This information is specified by the
with the value j. It is used to detect objects with different sizes matrix of relative frequencies Pd(i,j) with which two
and directions. The co-occurrence matrix values are calculated neighbouring pixels occur on the image, one with gray value i
for six window sizes (3×3,5×5,7×7,9×9,11×11,13×13) [3].It is and the other with gray value j.
popular in texture description and based on the repeated In Step1 the classification procedure using textural
occurrence of some gray level configuration in the texture. measures depends largely on the selected window size. The
This configuration varies with distance in fine textures, slowly optimal window size chosen in our implementation is 7×7,
in coarse textures. since it gives superior performance [3]. If the window size is
too small, insufficient spatial information is extracted to
B. Feature extraction characterise a specific land cover and if the window size is too
large, it can overlap two types of ground cover and thus
In order to estimate the similarity between different gray introduce erroneous spatial information.
level co-occurrence matrices, [4] proposed 14 statistical In Step2 the analysis of the correlation matrix among all the
features extracted from them. To reduce the computational texture measures with the six window sizes highlights high
complexity, only some of these features were selected. The correlations [3] between the same texture measures with
description of 4 most relevant features that are widely used in different window sizes and between the different texture
literature [5, 6, 7] is given in Table1. These four features are measures with different window sizes. The four texture
calculated from the gray level co-occurrence matrix of measures are calculated for a window size and principle
different window sizes(3×3,5×5,7×7,9×9,11×11,13×13). component analysis (PCA) is applied to the 24 texture
measures [3]. Then, on the one hand, the first three
components are extracted, while on the other hand, only the
TABLE1 first component is extracted. Next a texture measure is
TEXTURE MEASURES
calculated for the six window sizes and PCA is applied for
Homogeneity n 1 n 1
Pd (i, j )
 1  i  j
each type of texture measure.
i 0 j 0 III. PRINCIPLE COMPONENT ANALYSIS
Dissimilarity n 1 n 1

 P (i, j) i  j
i 0 j 0
d The steps involved in the implementation of PCA using the
covariance method is shown below.
Entropy n 1 n 1

 P (i, j) log P (i, j)
d d  Organize the data set
i 0 j 0  Calculate the mean
Angular Second n 1 n 1  Calculate the deviations from the mean
Moment  P (i, j)
i 0 j 0
d
2
 Find the Covariance matrix.
 Find the eigenvectors and eigenvalues of the
where i,j – Coordinates in the co-occurrence matrix covariance matrix
 Rearrange the eigenvectors and eigenvalues
Pd (i,j) – Co-occurrence matrix value at the  Transform the eigen space into PCA parameter
coordinates i,j
IV. RESULTS & DISCUSSION
n – Dimension of the co-occurrence matrix
In this paper to improve the global accuracy, two types
Homogeneity is a measure of the overall smoothness of an of images are taken. In first type, 10 texture feature images
image. It is high for GLCMs with elements localized near the are integrated and classified using threshold method. In
diagonal. The range of gray levels is small, Pd (i,j) will tend to second type, individual texture images are taken and classified
be clustered around the main diagonal [4]. Dissimilarity using threshold method. Both the results are compared with
measures can be used to quantify the differences between two the homogeneity [7  7] textural measure. The visualization
images. of the textural images show a simmilarity between the
Entropy is a statistical measure of randomness that can be dissimilarity and the angular second moment because these
used to characterize the texture of the input image. It is high two textural indices measure the homogeneity of images as
when the elements of GLCM have relatively equal value [6], shown in Fig 2(b) and 2(d). The high value areas (white) refer
low when the elements are close to either 0 or 1(when the to homogeneous areas such as water. The low values (black)
image is uniform in the window). Entropy is inversely characterize the heterogeneous areas such as the built-up
proportional to GLCM energy. classes.

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Fig. 1 Strategy of the Textural Analysis

Fig.3 shows that classification result of textural images. texture feature images (i.e. 1 &7) are not considered. Since
The classification results, obtained using the integration of all against homogeneity feature image only, classification
texture image is shown in Fig 3(a), which gives the high accuracy is compared.
global accuracy than other textural image, because, here the From the Table 2, it is observed that, the accuracy of
regions are more homogeneous. Nevertheless, the integration of 10 texture feature images are high, when
homogeneity measure with a 7×7 window size seems to be compared to other texture feature images. In Table 2, if the
optimal regarding the rate of correct classification and hence region is same for row and column, then the region is
the homogeneity feature image is used for comparison. In this correctly classified. Otherwise, the region is incorrectly
homogeneity texture feature image, the four regions 1, 2, 3, 4 classified. For example, in the integration of 10 texture feature
correspond to buildings, roads, and water and vegetations images, if the region is 1 for row and column, it represents the
areas respectively. The number of pixels in these regions are correct classification of buildings. If the region is 1 for row
486311, 24357, 1728 and 132 respectively. and 2 for column, then it represents incorrect classification of
The success of proposed image segmentation is shown in buildings as roads. The number of pixels correctly classified
the form of confusion matrix, in Table 2. In this table the in region 1 is 483802, region 2 is 10651, region 3 is 884 and
number of pixels correctly and incorrectly classified in various region 4 is 74. The other numbers in each row correspond to
regions for different feature images, the integrated texture the incorrectly classified pixels.
feature images are reported. Please note that homogeneity

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(a) (b) (c)

(d) (e)
Fig. 2 Extract of different co-occurrence-based textured measure: (a) original image; (b) angular second moment; (c) homogeneity; (d) dissimilarity; (e) entropy

(a) (b) (c)

(d) (e) (f)

(g) (h)

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Fig. 3 Classification results of textural images with the texture measure (Hom 7×7). (a) Integration of 10 texture feature images; (b) 3rd Texture feature image;
(c) 4th Texture feature image; (d) 5th Texture feature image; (e) 6th Texture feature image; (f) 7rd Texture feature image; (g) 8th Texture feature image;
(h) 9th Texture feature image;

TABLE 2
CONFUSION MATRIX OF VARIOUS TEXTURAL IMAGES

Accur homogeneity with a 7×7 window size. Satellite image consists
Texture -acy of both micro textures and macro textures. For micro textures
Region 1 2 3 4 small window size is enough and for macro textures, large
images (%)
window size is required. For this reason, one can improve the
Integra- 1 483802 2509 0 0 per-pixel classification by varying the different window size.
tion of 2 13661 10651 45 0 The co-occurrence based principle components (integration of
10 96.66 all textural images) which give the high accuracy than other
texture 3 0 819 884 25 textural image. Moreover, as window size for texture analysis is
feature related to image resolution and the contents within the image, it
images 4 0 0 58 74
would be interesting to choose different window sizes
1 486311 0 0 0 according to the size of the features to be extracted.
2nd 94.92
2 24282 75 0 0
texture
3 1334 307 73 14
image REFERENCES
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“Evaluation of the Gray Level Cooccurrence Matrix Method for

Viji Gurusamy received the
B.Engg. degree in Electronics and
Communication Engineering from
Anna University, Chennai, in
2008 and the Master of Engg.
degree from Anna University,
Tirunelveli, in 2010. From June
2010 to May 2012, She was worked in
M.Kumarasamy College of Engg, Karur. Now she is
currently working in P.S.R.Rengasamy College of
Engg for women, Sivakasi. She had attended four
international conferences and one national
conference in various colleges. Her research area
includes Digital Signal processing, Digital Image
processing, Digital Communication.

Kalarani Athilingam
completed her B.Engg. degree in
Electronics and Communication
Engineering from Anna
University, Chennai, in 2008
and the Master of Engg. degree
from Anna University,
Tirunelveli, in 2010. From June 2010 to till now, She
is working in P.S.R.Rengasamy College of Engg for
women, Sivakasi. Her research area includes Digital
Electronics, Digital Image processing, Antenna,
Communication. She has been attended several
workshops and conferences in various engg colleges.

Nimitha.N received the B.Engg.
degree in Electronics and
Communication Engineering
from Anna University, Chennai,
in 2006 and doing Master of
Engg. Degree in Anna University,
Coimbatore. From June 2008 to till now, She is
working in M.Kumarasamy College of Engg, Karur.
Her research area includes wireless networks, Digital
Communication, Digital Image processing and
optical communication.

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