Adaptive estimation of the registration-noise distribution for accurate unsupervised change detection

We propose an approach aimed at reducing the effects of registration noise in unsupervised change detection. The proposed approach performs an adaptive estimation of the registration-noise distribution, given a specific scene of interest. The estimated distribution is used to define a local adaptive decision strategy for carrying out change detection by taking into account misregistration. Experimental results confirm the effectiveness of the proposed approach.