Classification of Multimission SAR Images Based on Probabilistic Graphical Models and Convolutional Neural Networks

The problem of the semantic segmentation of multimodal images is characterized by the challenge of jointly exploiting information deriving from images possibly acquired at different spatial resolutions, frequencies, and bands. This paper 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 spatial and multiresolution information contained in multimodal remote sensing images collected by distinct space missions with SAR payloads. The experimental validation is conducted with COSMO-SkyMed and SAOCOM images over Northern Italy. The results show that the proposed methodology is capable to reach accurate classification maps from input multimission SAR imagery.