Multimission, Multifrequency, and Multiresolution SAR Image Classification Through Hierarchical Markov Models and Convolutional Networks

The availability of multimodal remotely sensed images calls for the development of methods capable to jointly exploit the information deriving from images acquired at different spatial resolutions, frequencies, and bands, taking advantage from their possible complementary features. This letter proposes to address this task in the case of multimission synthetic aperture radar (SAR) images, through a combination of fully convolutional networks (FCNs), hierarchical probabilistic graphical models (PGMs), and decision tree ensembles. The objective is to model the multimodal information collected at multiple spatial resolutions by distinct space missions with SAR payloads through the nonparametric formulation of FCNs and decision trees, and the spatial and multiresolution modeling capabilities of FCNs and hierarchical PGMs. The experimental validation is conducted with multimission SAR imagery acquired at X-, L-, and C-band, respectively, by COSMO-SkyMed, SAOCOM, and Sentinel-1 over Northern Italy. The results suggest the advantages of incorporating multifrequency radar acquisitions to reach accurate classification maps and the multimodal fusion capabilities of the proposed methodology.