Cue Integration for Urban Area Extraction in Remote Sensing Images

Authors:
Olfa Besbes;Nozha Boujemaa;Ziad Belhadj
Affiliations:
IMEDIA - INRIA Rocquencourt, Le Chesnay, France 78153 and URISA - SUPCOM, Parc Technologique, Ariana, Tunisia 2088;IMEDIA - INRIA Rocquencourt, Le Chesnay, France 78153;URISA - SUPCOM, Parc Technologique, Ariana, Tunisia 2088
Venue:
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
Year:
2009

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Abstract

In this paper, we present a probabilistic framework for urban area extraction in remote sensing images using a conditional random field built over an adjacency graph of superpixels. Our discriminative model performs a multi-cue combination by incorporating efficiently color, texture and edge cues. Both local and pairwise feature functions are learned using sharing boosting to obtain a powerful classifier based on feature selection. Urban area are accurately extracted in highly heterogenous satellite images by applying a cluster sampling method, the Swendsen-Wang Cut algorithm. Example results are shown on high resolution SPOT-5 satellite images.