An approach to unsupervised change detection in multitemporal SAR images based on the Generalized Gaussian distribution

This paper presents a novel approach to unsupervised change detection in multitemporal SAR images. This approach is based on three main steps: (1) controlled preprocessing based on adaptive filtering (despeckling); (2) comparison between multitemporal images according to a proper operator; (3) automatic thresholding of the log-ratio image. 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 identification of changes by thresholding the log-ratio image according to a novel technique. Such a technique is based on the double thresholding Kittler & Illingworth (K&I) algorithm, which is reformulated under the Generalized Gaussian (GG) assumption for the changed and unchanged classes. Experimental results obtained on a multitemporal SAR data set confirm the effectiveness of the proposed approach.