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Very high spatial resolution multispectral images and lower spatial resolution hyperspectral images are complementary sources for\udurban object classification. The first enables a fine delineation of objects, while the second can better discriminate classes and\udconsider richer land cover semantics. This paper presents a decision fusion scheme taking advantage of both sources classification\udmaps, to produce a better classification map. The proposed method aims at dealing with both semantic and spatial uncertainties and\udconsists in two steps. First, class membership maps are merged at pixel level. Several fusion rules are considered and compared in\udthis study. Secondly, classification is obtained from a global regularization of a graphical model, involving a fit-to-data term related\udto class membership measures and an image based contrast sensitive regularization term. Results are presented on three datasets. The\udclassification accuracy is improved up to 5 %, with comparison to the best single source classification accuracy

the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences

A TWO-STEP DECISION FUSION STRATEGY: APPLICATION TO HYPERSPECTRAL AND MULTISPECTRAL IMAGES FOR URBAN CLASSIFICATION