Classification of Multisensor and Multiresolution Remote Sensing Images Through Hierarchical Markov Random Fields

This letter proposes two methods for the supervised classification of multisensor optical and synthetic aperture radar images with possibly different spatial resolutions. Both the methods are formulated within a unique framework based on hierarchical Markov random fields. Distinct quad-trees associated with the individual information sources are defined to jointly address multisensor, multiresolution, and possibly multifrequency fusion, and are integrated with finite mixture models and the marginal posterior mode criterion. Experimental validation is conducted with Pléiades, COSMO-SkyMed, RADARSAT-2, and GeoEye-1 data.