Multiscale unsupervised change detection by Markov random fields and wavelet transforms

Change-detection methods represent powerful tools for monitoring the evolution of the state of the Earth's surface. In order to optimize the accuracy of the change maps, a multiscale approach can be adopted, in which observations at coarser and finer scales are jointly exploited. In this paper, a multiscale contextual unsupervised change-detection method is proposed for optical images, which is based on discrete wavelet transforms and Markov random fields. Wavelets are applied to the difference image to extract multiscale features and Markovian data fusion is used to integrate both these features and the spatial contextual information in the change-detection process. Expectation-maximization and Besag's algorithms are used to estimate the model parameters. Experiments on real optical images points out the improved effectiveness of the method, as compared with single-scale approaches.