Author(s): Ankita Dhyani,Deepa Gupta,Sonia Saini
Published in: International Journal of Engineering Research & Technology
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Volume/Issue: Vol.1 - Issue 2 (April- 2012)
The objective of this paper is to recognize different textures in an image, particularly a satellite image where properties of the image are not distinctly identified. Texture classification involves determining texture category of an observed image. The present study on Image Processing & Texture Classification was undertaken with a view to develop a comparative study about the texture classification methods. The algorithms implemented herein classify the different parts of the image into distinct classes, each representing one property, which is different from the other parts of the image. The aim is to produce a classification map of input image where each uniform textured region is identified with its respective texture class. The classification is done on the basis of texture of the image, which remains same throughout a region, which has a consistent property. The classified areas can be assigned different colours, each representing one texture of the image. In order to accomplish this, prior knowledge of the classes to be recognized is needed, texture features extracted and then classical pattern classification techniques are used to do the classification. Examples where texture classification was applied as the appropriate texture processing method include the classification of regions in satellite images into categories of land use. Here we have implemented two methods namely- Cross Diagonal Texture Matrix (CDTM) and Grey-Level Co- occurrence Matrix (GLCM), which are based on properties of texture spectrum (TS) domain for the satellite images. In CDTM, the texture unit is split into two separable texture units, namely, Cross texture unit and Diagonal texture unit of four elements each. These four elements of each texture unit occur along the cross direction and diagonal direction. For each pixel, CDTM has been evaluated using various types of combinations of cross and diagonal texture units. GLCM, on the other hand, is a tabulation of occurrence of different combinations of pixel brightness values (grey levels) in an image. Basically, the GLCM expresses the spatial relationship between a gray- level in a pixel with the gray-level in the neighboring pixels. The study focuses on extraction of entropy, energy, inertia and correlation features using several window sizes, which are calculated, based on the GLCM. A maximum likelihood supervised classifier is used for classification. While applying the algorithms on the images, we characterize our processed image by its texture spectrum. In this paper we deal with extraction of micro texture unit of 7X7 window to represent the local texture unit information of a given pixel and its neighborhood. The result shows that increasing the window size showed no significant contribution in improving the classification accuracy. In addition, results also indicate that the window size of 7x7 pixels is the optimal window size for classification. The texture features of a GLCM and CDTM have been used for comparison in discriminating natural texture images in experiments based on minimum distance. Experimental results reveal that the features of the GLCM are superior to the ones given by CDTM method for texture classification.
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