Multiscale Unsupervised Change Detection on Optical Images by Markov Random Fields and Wavelets

Change-detection methods represent powerful tools for monitoring the evolution of the Earth's surface. In order to optimize the accuracy of the change maps, a multiscale approach can be adopted that jointly exploits observations at coarser and finer scales. In this letter, a multiscale contextual unsupervised change-detection method is proposed for optical images. It 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 context in the change-detection process. Expectation-maximization and Besag's algorithms are used to estimate the model parameters. The selection of the optimal wavelet-transform operator within a predefined dictionary is automated by a minimum-energy criterion. Experiments on real optical images point out the effectiveness of this method as compared with state-of-the-art techniques.