Unsupervised change detection by multichannel SAR data fusion

In the contexts of environmental monitoring and disaster management, multichannel synthetic aperture radar (SAR) data present a good potential, thanks both to their insensitivity to atmospheric and Sun-illumination conditions, and to the improved discrimination capability they may provide as compared to single-channel SAR. In this paper an unsupervised contextual change-detection method is proposed for two-date multichannel SAR images, by adopting a data-fusion approach. Each SAR channel is modelled as a distinct information source and Markovian data fusion is used by introducing a suitable Markov random field model. The task of the estimation of the model parameters is addressed by combining the expectation- maximization algorithm with the recently proposed "method of log-cumulants." The proposed technique is experimentally validated on SIR-C/XSAR data.