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In this paper, the problem of the classification of multiresolution 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 favordoi:10.1109/igarss.2019.8898060 dblp:conf/igarss/MontaldoFHMZS19 fatcat:5qnos44q5bdcbhsct4nlgxxnp4