A multiscale contextual approach to change detection in multisensor VHR remote sensing images

The problem of unsupervised change detection from multisensor very high resolution images is addressed in this paper by focusing on the case in which multitemporal SAR data but only a single-date optical observation are available. This peculiar and challenging scenario is especially interesting in disaster management applications in which SAR acquisitions are feasible both before and after the event and an optical image is available only at one date (e.g., from the archive). The proposed method combines a novel Markov random field model with multiscale region-based analysis in order to fuse the information associated both with the statistics of the ratio of the multitemporal SAR images and with the spatial-geometrical structure of the observed scene captured by the optical image. Parameter estimation is based on a dictionary of parametric families and is carried out through the expectation-maximization algorithm and the method of log-cumulants. Graph cuts are used to minimize the energy function of the proposed MRF model. Experimental results are presented with COSMO-SkyMed and GeoEye-1 images.