Robust Unsupervised Change Detection with Markov Random Fields

Because of the strong statistical variability of remote sensing images, the selection of the best thresholding algorithm to detect changes between two successive temporal images of the same study area without any prior knowledge is often not easy. In this paper, we face this problem through a new robust change detection approach. In order to achieve robustness, the proposed unsupervised approach is based on a Markov random field (MRF) fusion of change maps provided by an ensemble of different thresholding algorithms. Experimental results obtained on three images acquired by different sensors and referring to different kinds of changes confirm the robustness of the proposed approach.