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A Bayesian approach to detect land cover changes is presented. Comparison of classification maps performed at different dates is a straightforward tool for obtaining changes occurred in between. Unfortunately the direct comparison of classification maps is characterized by an high probability of occurrence of false positive change events. To overcome this problem, it is necessary that the process might be driven by the a priori knowledge about considered scene. In this framework, the change detection is seen as a data fusion task where the a priori class transitions drives a multitemporal classification. The validity of the proposed method is confirmed by the results, which are discussed in this paper. In fact, when compared with an independent classification scheme, the approach based on a priori knowledge permits to achieve a higher overall accuracy without loosing in efficiency.