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10 November 2004 Change detection in multitemporal SAR images based on generalized Gaussian distribution and EM algorithm
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In this paper, we propose a novel automatic and unsupervised change-detection approach specifically oriented to the analysis of multitemporal single-channel single-polarization SAR images. Such an approach is based on a closed-loop process composed of three main steps: 1) pre-processing based on a controlled adaptive iterative filtering; 2) comparison between multitemporal images according to a standard log-ratio operator; 3) automatic analysis of the log-ratio image for generating the change-detection map. The first step aims at reducing the speckle noise in a controlled way in order to maximize the separability between changed and unchanged classes. The second step is devoted to compare the two filtered images in order to generate a log-ratio image. Finally, the third step deals with the automatic selection of the decision threshold to be applied to the log-ratio image. This selection is carried out according to a novel formulation of the Expectation Maximization (EM) algorithm under the assumption that changed and unchanged classes follow Generalized Gaussian (GG) distributions. Experimental results on real ERS-2 SAR images confirmed the effectiveness of the proposed approach.
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Yakoub Bazi, Lorenzo Bruzzone, and Farid Melgani "Change detection in multitemporal SAR images based on generalized Gaussian distribution and EM algorithm", Proc. SPIE 5573, Image and Signal Processing for Remote Sensing X, (10 November 2004);

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