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SEMANTIC SEGMENTATION OF AERIAL IMAGES IN URBAN AREAS WITH CLASS-SPECIFIC HIGHER-ORDER CLIQUES
2015
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
In this paper we propose an approach to multi-class semantic segmentation of urban areas in high-resolution aerial images with classspecific object priors for buildings and roads. What makes model design challenging are highly heterogeneous object appearances and shapes that call for priors beyond standard smoothness or co-occurrence assumptions. The data term of our energy function consists of a pixel-wise classifier that learns local co-occurrence patterns in urban environments. To
doi:10.5194/isprsannals-ii-3-w4-127-2015
fatcat:3iv2i7kr7rddxe7wryqm5ljgeu