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

Abstract : In this paper, we propose a novel method for the joint classification of multidate, multiresolution and multisensor remote sensing imagery, which represents a vital and fairly unexplored classification problem. The proposed classifier is based on an explicit hierarchical graph-based model sufficiently flexible to deal with multisource coregistered time series of images collected at different spatial resolutions. An especially novel element of the proposed approach is the use of multiple quad-trees in cascade, each associated with each new available image at different dates, with the aim to characterize the temporal correlations associated with distinct images in the input time series. Experimental results are shown with multitemporal and multiresolution Pléiades data
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https://hal.inria.fr/hal-01071034
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Ihsen Hedhli, Gabriele Moser, Josiane Zerubia, Sebastiano B. Serpico. New cascade model for hierarchical joint classification of multitemporal, multiresolution and multisensor remote sensing data. IEEE ICIP - International Conference on Image Processing, Oct 2014, Paris, France. ⟨hal-01071034⟩

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