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

Ihsen Hedhli (1, 2) , Gabriele Moser (2) , Sebastiano Serpico (2) , Josiane Zerubia (1)

1 AYIN - Models of spatio-temporal structure for high-resolution image processing
2 DITEN - Dipartimento di Ingegneria Navale, Elettrica, Elettronica e delle Telecomunicazioni / Dept. of Electrical, Electronic, Telecommunications Engineering and Naval Architecture

This letter proposes two methods for the supervised classification of multisensor optical and SAR images with possibly different spatial resolutions. Both 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, multiresolu-tion, 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.

Mots clés: Multifrequency fusion; Marginal posterior mode (MPM); Hierarchical Markov Random Field (MRF); Finite mixture models; Quad-tree; Multisensor; Multiresolution

Domaines: Traitement du signal et de l'image [eess.SP]; Applications [stat.AP]

Ihsen Hedhli, Gabriele Moser, Sebastiano Serpico, Josiane Zerubia. Classification of Multisensor and Multiresolution Remote Sensing Images through Hierarchical Markov Random Fields. IEEE Geoscience and Remote Sensing Letters, 2017, 14 (2), pp.2448-2452. ⟨10.1109/LGRS.2017.2768398⟩. ⟨hal-01632907⟩