Interactive change detection techniques in multitemporal multispectral remote sensing images

This paper proposes an interactive change detection method in multitemporal remote sensing images. The user needs to input markers related to change and no-change classes in the Difference image. Then this information is used by a support vector machine classifier to generate a spectral-change map. Then two different solutions based on Markov Random Field or Level-Set methods are used to incorporate the spatial contextual information in the decision process. While the Markov Random Field method is region driven, the level-set method exploits both region and contour for performing the segmentation task. Experiments conducted on two real remote-sensing images confirm the promising capabilities of the proposed method.