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In this letter, we propose a novel approach for unsupervised change detection in multitemporal optical satellite images. Unlike the traditional methods, the proposed method, called the soft-change detection, models the change detection as a transparency computation problem and assigns to each pixel a set of soft labels. In order to extract the pixel opacity, we optimize an objective function by exploiting the Bayesian matting method. Comparisons between the proposed method and the state-of-the-art methods are reported. Experimental results demonstrate the effectiveness of the proposed method.