Abstract
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.