Exploration of Multitemporal COSMO-SkyMed Data via Interactive Tree-Structured MRF Segmentation

We propose a new approach for remote sensing data exploration, based on a tight human-machine interaction. The analyst uses a number of powerful and user-friendly image classification/segmentation tools to obtain a satisfactory thematic map, based only on visual assessment and expertise. All processing tools are in the framework of the tree-structured MRF model, which allows for a flexible and spatially adaptive description of the data. We test the proposed approach for the exploration of multitemporal COSMO-SkyMed data, that we appropriately registered, calibrated, and filtered, obtaining a performance that is largely superior, in both subjective and objective terms, to that of comparable noninteractive methods.