In this paper, a new classification method based on contextual data fusion is proposed. The method is suited for land-use classification of remotely sensed images of the same scene captured at different dates from multiple sources. It incorporates a priori information about the likelihood of changes between the acquisitions of the different images to be fused. The contextual analysis of a multisensor image of a given site represents a way to improve the accuracy with respect to the non-contextual single-time classification. Experimental results on a multisensor data set consisting of two multisensor images are presented and the performances of the proposed method are compared with those of both a classifier based on Markov random fields and a statistical contextual classifier.