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Conditional mixed-state model for structural change analysis from very high resolution optical images

Benjamin Belmudez 1 Veronique Prinet 1 Jian-Feng Yao 2 Patrick Bouthemy 3 Xavier Descombes 4
3 VISTAS - Spatio-Temporal Vision and Learning
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
4 ARIANA - Inverse problems in earth monitoring
CRISAM - Inria Sophia Antipolis - Méditerranée , Laboratoire I3S - SIS - Signal, Images et Systèmes
Abstract : The present work concerns the analysis of dynamic scenes from earth observation images. We are interested in building a map which, on one hand locates places of change, on the other hand, reconstructs a unique visual information of the non-change areas. We show in this paper that such a problem can naturally be takled with conditional mixed-state random field modeling (mixed-state CRF), where the "mixed state" refers to the symbolic or continous nature of the unknown variable. The maximum a posteriori (MAP) estimation of the CRF is, through the Hammersley-Clifford theorem, turned into an energy minimisation problem. We tested the model on several Quickbird images and illustrate the quality of the results.
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Submitted on : Wednesday, June 24, 2009 - 11:07:59 AM
Last modification on : Wednesday, October 14, 2020 - 4:23:46 AM
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  • HAL Id : inria-00398062, version 1


Benjamin Belmudez, Veronique Prinet, Jian-Feng Yao, Patrick Bouthemy, Xavier Descombes. Conditional mixed-state model for structural change analysis from very high resolution optical images. 2009 IEEE International Geosciences and Remote Sensing Symposium, Jul 2009, Cape Town, South Africa. ⟨inria-00398062⟩



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