SAR Images Unsupervised Change Detection Based on Combination of Texture Feature Vector with Maximum Entropy Principle

doi:10.11947/j.AGCS.2016.20150022

Abstract

Abstract: Generally, spatial-contextual information would be used in change detection because there is significant speckle noise in synthetic aperture radar(SAR) images. In this paper, using the rich texture information of SAR images, an unsupervised change detection approach to high-resolution SAR images based on texture feature vector and maximum entropy principle is proposed. The difference image is generated by using the 32-dimensional texture feature vector of gray-level co-occurrence matrix(GLCM). And the automatic threshold is obtained by maximum entropy principle. In this method, the appropriate window size to change detection is 11×11 according to the regression analysis of window size and precision index. The experimental results show that the proposed approach is better could both reduce the influence of speckle noise and improve the detection accuracy of high-resolution SAR image effectively; and it is better than Markov random field.

Key words: GLCM, texture feature vector, maximum entropy principle, SAR, change detection

CLC Number:

P236

ZHUANG Huifu, DENG Kazhong, FAN Hongdong. SAR Images Unsupervised Change Detection Based on Combination of Texture Feature Vector with Maximum Entropy Principle[J]. Acta Geodaetica et Cartographica Sinica, 2016, 45(3): 339-346.

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