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Joint classification of multiresolution and multisensor data using a multiscale Markov mesh model

Abstract : In this paper, the problem of the classification of multireso-lution and multisensor remotely sensed data is addressed by proposing a multiscale Markov mesh model. Multiresolution and multisensor fusion are jointly achieved through an explicitly hierarchical probabilistic graphical classifier, which uses a quadtree structure to model the interactions across different spatial resolutions, and a symmetric Markov mesh random field to deal with contextual information at each scale and favor applicability to very high resolution imagery. Differently from previous hierarchical Markovian approaches, here, data collected by distinct sensors are fused through either the graph topology itself (across its layers) or decision tree ensemble methods (within each layer). The proposed model allows taking benefit of strong analytical properties, most remarkably causality, which make it possible to apply time-efficient non-iterative inference algorithms.
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https://hal.inria.fr/hal-02157082
Contributor : Ihsen Hedhli <>
Submitted on : Saturday, June 15, 2019 - 5:26:38 AM
Last modification on : Monday, July 20, 2020 - 1:06:06 PM

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Alessandro Montaldo, Luca Fronda, Ihsen Hedhli, Gabriele Moser, Josiane Zerubia, et al.. Joint classification of multiresolution and multisensor data using a multiscale Markov mesh model. IGARSS 2019 - IEEE International Geoscience and Remote Sensing Symposium, Jul 2019, Yokohama, Japan. ⟨hal-02157082⟩

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