Application of image texture analysis to improve land cover classification

Authors:
Xiaochen Zou;Daoliang Li
Affiliations:
College of Information and Electrical Engineering, China Agricultural University, Beijing, China and Key Laboratory of Modern Precision Agriculture System Integration, Ministry of Education, Beiji ...;College of Information and Electrical Engineering, China Agricultural University, Beijing, China and Key Laboratory of Modern Precision Agriculture System Integration, Ministry of Education, Beiji ...
Venue:
WSEAS Transactions on Computers
Year:
2009

Citing 4
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Abstract

Image texture analysis has received a considerable amount of attention over the last few years as it has played an important role in the classification of the remote sensing images. This paper provides an overview of several different approaches to image texture analysis and demonstrates their use on the problem of land cover classification. We used grey level co-occurrence matrix (GLCM) method to assistant the land cover classification and then compared and evaluated all of the result of classifications. In the experimentation, by comparing the classification result of contrast, energy and entropy we find out that the preferable texture features of grey level co-occurrence matrices method was contrast. In this thesis, it used the feature images helping the classification of remote sensing and obtained good result. And it also used C++ programming language to write a programme to compute the number of the feature of texture.