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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

93 All Rights Reserved © 2012 IJARCSEE

Image Segmentation in Satellite Image using Optimal

Texture Measures

G.Viji1, N.Nimitha

2,A.Kalarani

2

1Assistant Professor, P.S.R.Rengasamy college of Engineering for women, Sivakasi 2Lecturer, M.Kumarasamy college of Engineering,karur.

2Assistant Professor, P.S.R.Rengasamy college of Engineering for women, Sivakasi.

Abstract— Texture in high resolution satellite images requires

substantial amendment in the conventional segmentation

algorithms. In this paper, a satellite image is segmented using

optimal texture measures. Satellite image used in this paper is a

high resolution data which will provide more details of the urban

areas, but it seems evident that it will create additional problems

in terms of information extraction using automatic classification.

This work improves the classification accuracy of intra-urban

land cover types. Four texture measures are evaluated using

grey-level co-occurrence matrix (GLCM). Four texture indices

with six window sizes are obtained from satellite image. Principle

Component Analysis (PCA) is applied to these texture measures.

The resultant image is then compared with homogeneity texture

feature image, obtained using 7×7 window. The per pixel

classification accuracy is improved in this work by varying the

window size.

Keywords - Gray Level Co-occurrence Matrix (GLCM), Principle

Component Analysis (PCA), Remote Sensing, Satellite Image,

Segmentation.

I. INTRODUCTION

Image segmentation plays an important role in human

vision, computer vision and pattern recognition fields.

Segmentation refers to the process of partitioning a digital

image into multiple segments. The goal of segmentation is to

simplify and or change the representation of an image into

something that is more meaningful and easier to analyse.

Image segmentation is typically used to locate objects and

boundaries (lines, curves, etc.) in images. More precisely,

image segmentation is the process of assigning a label to

every pixel in an image such that pixels with the same label

share certain visual characteristics. In order to better explain

the structure of this work, the preliminary information about

the satellite image and remote sensing is discussed [1].

Remote sensing is a science of obtaining information about

an object, area or phenomenon through the analysis of data

acquired by a device that is not in contact with the object [1].

Commonly remote sensing is referred to the collection and

analysis of data regarding the earth using electromagnetic

sensors, which are operated from the space borne platform.

Satellite image is a remotely sensed one and defined as a

picture of the earth taken from an earth orbital satellite. This

image consists of buildings, roads, vegetations, water bodies

and other open areas. Satellite images are an important

information source and provide current information on a

periodic basis at low cost.

Satellite image consists of micro textures and macro

textures. For micro textures the statistical approach seems to

be work well. The statistical approaches have included auto

correlation functions, digital transform, and gray level tone

co-occurrence. For macro textures the approach seems to be

moving in the direction of using histograms of primitive

properties and co-occurrence of primitive properties in

structural and statistical. These techniques are not sufficient to

segment high resolution images due to the variability of

spectral and structural information in such images [2].

Thus the spatial pattern or texture analysis becomes

necessary to segment high resolution image. The proposed

method is based on the feature extraction from the gray level

co-occurrence matrix, which is a well known method for

analysing the texture features. The segmentation based on this

texture features can improve the accuracy of this

interpretation. A problem that frequently arises when

segmenting an image is that the number of feature variables or

dimensionality is often quite large. It becomes necessary to

decrease the number of variables to manageable size, at the

same time, retaining as much discrimination information as

possible. In this paper an algorithm called principle

component analysis is introduced to solve this problem.

The paper is organized as follows. First in Section II,

Proposed Methodology is dealt, Principle Component

Analysis (PCA) in Section III, Results and discussion are dealt

in Section IV. Finally conclusions are given in Section V.

II. PROPOSED METHODOLOGY

The Fig.1 shows that representation of the proposed

methodology. The proposed methodology consists of two

steps: Step1: optimal window size and Step2: optimal

texture measure. Feature extraction acquired by this

experiment is derived from gray level co-occurrence matrix.

The more details of this texture analysis are shown by the

following subheadings.

A. Gray level Co-occurrence matrix

Gray level co-occurrence matrix is the two dimensional

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

http://en.wikipedia.org/wiki/Digital_image






obtained by calculating how often a pixel with gray level

value i occurs horizontally adjacent to a pixel with the value j.

Each element (i,j) in GLCM specifies the number of times that

the pixel with value i occurs horizontally adjacent to a pixel

with the value j. It is used to detect objects with different sizes

and directions. The co-occurrence matrix values are calculated

for six window sizes (3×3,5×5,7×7,9×9,11×11,13×13) [3].It is

popular in texture description and based on the repeated

occurrence of some gray level configuration in the texture.

This configuration varies with distance in fine textures, slowly

in coarse textures.

B. Feature extraction

In order to estimate the similarity between different gray

level co-occurrence matrices, [4] proposed 14 statistical

features extracted from them. To reduce the computational

complexity, only some of these features were selected. The

description of 4 most relevant features that are widely used in

literature [5, 6, 7] is given in Table1. These four features are

calculated from the gray level co-occurrence matrix of

different window sizes(3×3,5×5,7×7,9×9,11×11,13×13).

TABLE1

TEXTURE MEASURES

Homogeneity

1

0

1

0 1

),(n

i

n

j

d

ji

jiP

Dissimilarity

1

0

1

0

),(n

i

n

j

d jijiP

Entropy

1

0

1

0

),(log),(n

i

n

j

dd jiPjiP

Angular Second

Moment

1

0

1

0

2),(n

i

n

j

d jiP

where i,j – Coordinates in the co-occurrence matrix

Pd (i,j) – Co-occurrence matrix value at the

coordinates i,j

n – Dimension of the co-occurrence matrix

Homogeneity is a measure of the overall smoothness of an

image. It is high for GLCMs with elements localized near the

diagonal. The range of gray levels is small, Pd (i,j) will tend to

be clustered around the main diagonal [4]. Dissimilarity

measures can be used to quantify the differences between two

images.

Entropy is a statistical measure of randomness that can be

used to characterize the texture of the input image. It is high

when the elements of GLCM have relatively equal value [6],

low when the elements are close to either 0 or 1(when the

image is uniform in the window). Entropy is inversely

proportional to GLCM energy.

Angular Second Moment [6] is a measure of homogeneity

of the image. It is high when the GLCM has few entries of

large magnitude, low when all entries are almost equal. This is

the opposite of entropy. This information is specified by the

matrix of relative frequencies Pd(i,j) with which two

neighbouring pixels occur on the image, one with gray value i

and the other with gray value j.

In Step1 the classification procedure using textural

measures depends largely on the selected window size. The

optimal window size chosen in our implementation is 7×7,

since it gives superior performance [3]. If the window size is

too small, insufficient spatial information is extracted to

characterise a specific land cover and if the window size is too

large, it can overlap two types of ground cover and thus

introduce erroneous spatial information.

In Step2 the analysis of the correlation matrix among all the

texture measures with the six window sizes highlights high

correlations [3] between the same texture measures with

different window sizes and between the different texture

measures with different window sizes. The four texture

measures are calculated for a window size and principle

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

first component is extracted. Next a texture measure is

calculated for the six window sizes and PCA is applied for

each type of texture measure.

III. PRINCIPLE COMPONENT ANALYSIS

The steps involved in the implementation of PCA using the

covariance method is shown below.

Organize the data set

Calculate the mean

Calculate the deviations from the mean

Find the Covariance matrix.

Find the eigenvectors and eigenvalues of the

covariance matrix

Rearrange the eigenvectors and eigenvalues

Transform the eigen space into PCA parameter

IV. RESULTS & DISCUSSION

In this paper to improve the global accuracy, two types

of images are taken. In first type, 10 texture feature images

are integrated and classified using threshold method. In

second type, individual texture images are taken and classified

using threshold method. Both the results are compared with

the homogeneity [77] textural measure. The visualization

of the textural images show a simmilarity between the

dissimilarity and the angular second moment because these

two textural indices measure the homogeneity of images as

shown in Fig 2(b) and 2(d). The high value areas (white) refer

to homogeneous areas such as water. The low values (black)

characterize the heterogeneous areas such as the built-up

classes.




Fig. 1 Strategy of the Textural Analysis

Fig.3 shows that classification result of textural images.

The classification results, obtained using the integration of all

texture image is shown in Fig 3(a), which gives the high

global accuracy than other textural image, because, here the

regions are more homogeneous. Nevertheless, the

homogeneity measure with a 7×7 window size seems to be

optimal regarding the rate of correct classification and hence

the homogeneity feature image is used for comparison. In this

homogeneity texture feature image, the four regions 1, 2, 3, 4

correspond to buildings, roads, and water and vegetations

areas respectively. The number of pixels in these regions are

486311, 24357, 1728 and 132 respectively.

The success of proposed image segmentation is shown in

the form of confusion matrix, in Table 2. In this table the

number of pixels correctly and incorrectly classified in various

regions for different feature images, the integrated texture

feature images are reported. Please note that homogeneity

texture feature images (i.e. 1 &7) are not considered. Since

against homogeneity feature image only, classification

accuracy is compared.

From the Table 2, it is observed that, the accuracy of

integration of 10 texture feature images are high, when

compared to other texture feature images. In Table 2, if the

region is same for row and column, then the region is

correctly classified. Otherwise, the region is incorrectly

classified. For example, in the integration of 10 texture feature

images, if the region is 1 for row and column, it represents the

correct classification of buildings. If the region is 1 for row

and 2 for column, then it represents incorrect classification of

buildings as roads. The number of pixels correctly classified

in region 1 is 483802, region 2 is 10651, region 3 is 884 and

region 4 is 74. The other numbers in each row correspond to

the incorrectly classified pixels.




(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)




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

Region: 1-Buildings, 2-Roads, 3-Water, 4-Vegitations

V. CONCLUSIONS

This paper confirms the utility of textural analysis to

enhance the per-pixel classification accuracy for high resolution

images, especially in urban areas where the images are

spectrally more heterogeneous. For the texture analysis, it is

noted that the best co-occurrence based texture measure is the

homogeneity with a 7×7 window size. Satellite image consists

of both micro textures and macro textures. For micro textures

small window size is enough and for macro textures, large

window size is required. For this reason, one can improve the

per-pixel classification by varying the different window size.

The co-occurrence based principle components (integration of

all textural images) which give the high accuracy than other

textural image. Moreover, as window size for texture analysis is

related to image resolution and the contents within the image, it

would be interesting to choose different window sizes

according to the size of the features to be extracted.

REFERENCES

[1] ImagesManimala Singha et al “Color Image Segmentation for

Satallite” International Journal on Computer Science and Engineering

2011.

[2] A.P.Carleer, O.Debeir, E.Wolff, “Assessment of very High Spatial

Resolution Satellite Image Segmentations,” Photogrammetric

Engineering and Remote Sensing, vol. 71, no.11, pp.1285-1294, 2005.

[3] A.Puissant, J.Hirsch, and C.Weber, “The utility of texture analysis

to improve per-pixel classification for high to very high spatial

resolution imagery,” International Journal of Remote Sensing., vol.26,

no.4, pp. 733-745, 2005.

[4] R.M.Haralick, K.Shanmugam, and I.Dinstein, “Textural Features

for Image Classification,” IEEE Transactions on Systems, Man, and

Cybemetics, vol.SMC-3, no.6, pp. 610-621, Nov.1973.

[5] S.Arivazhagan and L.Ganesan, “Texture Classification using

Wavelet Transform Pattern Recognition Letters,’ vol.24, pp.1513-

1521, 2003.

[6] A.Baraldi and F.Parmiggiani, “An investigations of the Textural

Characteristics Associated with Gray Level Co-occurrence Matrix

Statistical Parameters,” IEEE Transaction on Geoscience and Remote

Sensing, vol.33, no.2, pp.293-304, 1995.

[7] R.M.Haralick, “Statistical and Structural Approaches to Texture,”

Proceedings of the IEEE, vol.67, no.5,pp. 786-804,May.1979.

H.Anys, A.Bannari, D.C.He, and D.Morin, ”Texture Analysis for

the Mapping of Urban Areas using Airborne MEIS-II Images, ”In

Proceedings of the First International Airborne Remote Sensing

Conference and Exhibition,vol.III,pp.231-245,Sep.1994.

[8] P.Dulyakam, Y.Rangsanseri, and P.Thitimajshima, ”Textural

Classification of urban Environment using Gray level Co- occurrence

Matrix Approach,” 2nd International Conference on Earth Observation

and Environmental Information, 2000.

[9] J.S.Weszka, C.R.Dyer, and A.Rosenfeld, “A Comparative Study

of Texture Measures for Terrain Classification,” IEEE Transaction on

Systems, Man and Cybernetics, vol.SMC-6, no.4, 1976.

[10] J.Gu, J.Chen, Q.M.Zhou and H.W.Zhang, “Quantitative

Textural Parameter Selection for Residential Extraction from High

Resolution remotely Sensed Imagery,” The International Archives of

the Photogrammetry,Remote Sensing and Spatial Information

Sciences,col.B4,no.37, 2008.

[11] G.Meinel and M.Neubert, “A Comparison of Segmentation

Programs for High Resolution Remote Sensing Data,” International

Archives of Photogrammetry and Remote Sensing, vol.35, pp.1097-

1105, 2004.

Texture

images Region 1 2 3 4

Accur

-acy

(%)

Integra-

tion of

10

texture

feature

images

1 483802 2509 0 0

96.66 2 13661 10651 45 0

3 0 819 884 25

4 0 0 58 74

2nd

texture

image

1 486311 0 0 0

94.92 2 24282 75 0 0

3 1334 307 73 14

4 40 41 32 19

3rd

texture

image

1 486311 0 0 0

94.92 2 24208 149 0 0

3 1108 602 17 1

4 3 81 40 8

4th

texture

image

1 486311 0 0 0

94.92 2 24208 149 0 0

3 1108 602 17 1

4 3 81 40 8

5th

texture

image

1 423591 62095 617 8

85.7 2 8044 14762 1504 47

3 16 857 767 88

4 8 30 55 39

6th

texture

image

1 485437 874 0 0

96 2 17310 7045 2 0

3 0 1215 507 6

4 0 0 83 49

8th

texture

image

1 486309 2 0 0

94.96 2 24075 282 0 0

3 1081 549 83 15

4 18 55 37 22

9th

texture

image

1 472032 14235 44 6

93.9 2 15114 8883 356 4

3 1 1314 394 19

4 0 54 58 20

10th

texture

image

1 472032 14235 44 6

93.9 2 15114 8883 356 4

3 1 1314 394 19

4 0 54 58 20




[12] O.O.Yashon, J.Tetuko and R.Tateishi, ”Analysis of co-

occurrence and Discrete Wavelet Transform Textures for

differentiation of Forest and Non-forest Vegetation in Very High

Resolution Optical-Sensor Imagery,” International Journal of Remote

Sensing,vol.29,no.12,pp.3417-3456, 2008.

[13] W.K.Pratt, “Digital Image Processing,” 2nd edition (New York;

Wiley).

[14] D.J.Marcead, P.J.Howarth, J.M.M.Dubois, and D.J.Gratton,

“Evaluation of the Gray Level Cooccurrence Matrix Method for

Land Cover Classification using SPOT Imagery,” IEEE Transactions

on Geoscience and Remote Sensing vol.28, pp.513- 519, 1990.

[14] N.Haala and C.Brenner, “Extraction of Buildings and Trees in

Urban Environments,” Photogrammetric Engineering and Remote

Sensing,”vol.54, pp.130-137, 1999.

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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