Abstract
The most common methodology to carry out an automatic unsupervised change detection in remotely sensed imagery is to find the best global threshold in the histogram of the so-called difference image. The unsupervised nature of the change detection process, however, makes it nontrivial to find the most appropriate thresholding algorithm for a given difference image, because the best global threshold depends on its statistical peculiarities, which are often unknown. In this letter, a solution to this issue based on the fusion of an ensemble of different thresholding algorithms through a Markov random field framework is proposed. Experiments conducted on a set of five real remote sensing images acquired by different sensors and referring to different kinds of changes show the high robustness of the proposed unsupervised change detection approach.