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We address the problem of SAR despeckling by resorting to nonlocal filtering guided by an optical image. In fact, given the increasing availability of remote-sensing optical images, it makes perfect sense trying to use them to improve the performance of despeckling. Our technique exploits the optical image to reliably estimate the statistical similarity among pixels, which is used to evaluate the weights of nonlocal filtering. Optical data are not used to estimate SAR values, but only to guide the overall process. In addition, they are discarded altogether in regions where SAR and optical images present different local geometries, identified by a preliminary classification step, avoiding thus any additional distortion. Experimental results show the proposed approach to provide images of better quality than state-of-the-art conventional filters.

Reliable segmentation of SAR images requires some forms of user supervision: we resort here to the interactive version of the Tree-Structured Markov Random Field (TS-MRF) segmentation suite. The TS-MRF model, and the associated segmentation tool, provide a flexible and spatially adaptive description of the data. In the interactive version, the user can drive the process based on the inspection of the current result, deciding step-by-step which direction to take, and switching from one segmentation modality to another. Experiments with the segmentation and classification of multitemporal SAR images prove the potential of the interactive approach and of the TS-MRF tool.