Change detection based on adaptive Markov Random Fields

Usually changes in remote sensing images go along with the appearance or disappearance of some edges. In addition, pixels located along the edges are likely to weakly influenced by its neighborhood pixels, while pixels located far from the edges commonly have a tightly correlation among them. In this paper, we propose a novel change detection technique based on adaptive Markov Random Fields (MRFs) for high resolution satellite images with combined color and texture features. The technique is composed of two main steps: (1) the input images are marked with different region indexes by the combined color and edge features; (2) change maps are obtained under the MRF framework with alterable order of neighborhood and variable smooth weight coefficient controlled by the index map. The main contribution of this paper is that the spatial-contextual information included in the remote sensing imagery is correctly and adaptively exploited under an adaptive MRF framework. Experiments results obtained on a set of remote sensing imagery confirm the effectiveness of the proposed approach.