Skip to Main Content
This paper is concerned with "structural" change detection in pair of very high resolution remote sensing images. This is a challenging and open problem since the difficulties stemming from the confusion between real changes (depending on the objects/structures inside the images) and visual changes (observed through the difference in terms of image luminance) are numerous. Many applications are concerned with this crucial task (agriculture, urban,...). We propose to solve this labeling problem as the minimization of a global cost-function using a min-cut/max-flow strategy. Because of the different nature of the input images (different sensor, shooting angle, ...) and of the variety of detailed information contained in an object, we propose to rely on several criteria, either able to detect abrupt or subtle changes. These criteria are computed on local patches whose size adaptively depend on the structure of the objects inside the images. Experimental quantitative and qualitative results are shown on synthetic and real data.