New cascade model for hierarchical joint classification of multisensor and multiresolution remote sensing data

Ihsen Hedhli 1 Gabriele Moser 2 Sebastiano B. Serpico 2 Josiane Zerubia 1
1 AYIN - Models of spatio-temporal structure for high-resolution image processing
CRISAM - Inria Sophia Antipolis - Méditerranée
2 DITEN - Dipartimento di Ingegneria Navale, Elettrica, Elettronica e delle Telecomunicazioni / Dept. of Electrical, Electronic, Telecommunications Engineering and Naval Architecture
Abstract : This paper addresses the problem of multisensor fusion of COSMO-SkyMed and RADARSAT-2 data together with optical imagery for classification purposes. The proposed method is based on an explicit hierarchical graph-based model that is sufficiently flexible to deal with multisource coregistered images collected at different spatial resolutions by different sensors. An especially novel element of the proposed approach is the use of multiple quad-trees in cascade, each associated with a set of images acquired by different SAR sensors, with the aim to characterize the correlations associated with distinct images from different instruments. Experimental results are shown with COSMO-SkyMed, RADARSAT-2, and Pléiades data 1 .
Keywords : Multisensor multiresolution remote sensing images supervised classification hierarchical Markov random fields
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Communication dans un congrès
IEEE International Geoscience and Remote Sensing Symposium, Jul 2015, Milan, Italy. 2015, 〈http://www.igarss2015.org/〉
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Ihsen Hedhli, Gabriele Moser, Sebastiano B. Serpico, Josiane Zerubia. New cascade model for hierarchical joint classification of multisensor and multiresolution remote sensing data. IEEE International Geoscience and Remote Sensing Symposium, Jul 2015, Milan, Italy. 2015, 〈http://www.igarss2015.org/〉. 〈hal-01161817〉

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