A genetic expectation-maximization method for unsupervised change detection in multitemporal SAR imagery

In this paper, we present a novel, automatic and unsupervised change-detection approach to the analysis of single-channel single-polarization multitemporal SAR images. The statistical parameters of the changed and unchanged classes, which are assumed to follow a generalized Gaussian (GG) distribution in the analysed log-ratio image, are explicitly estimated by the expectation-maximization (EM) algorithm initialized with a robust strategy based on genetic algorithms (GAs). In addition, the proposed approach integrates two further processing capabilities. The first one intends to cope with the problem of the automatic detection of multiple changes in the scene. This is carried out by modelling the log-ratio image histogram with a multimodal GG mixture whose number of components is estimated basing on the Bayesian information criterion (BIC). The second processing capability allows exploitation of spatial contextual information in the change detection process through a Markovian formulation. Results obtained on both simulated and real data are reported and discussed.