Hierarchical segmentation of multiresolution remote sensing images

Authors:
Camille Kurtz;Nicolas Passat;Anne Puissant;Pierre Gançarski
Affiliations:
Université de Strasbourg, LSIIT, UMR CNRS 7005, Strasbourg, France;Université de Strasbourg, LSIIT, UMR CNRS 7005, Strasbourg, France;Université de Strasbourg, LIVE, ERL CNRS 7230, Strasbourg, France;Université de Strasbourg, LSIIT, UMR CNRS 7005, Strasbourg, France
Venue:
ISMM'11 Proceedings of the 10th international conference on Mathematical morphology and its applications to image and signal processing
Year:
2011

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Abstract

The extraction of urban patterns from very high spatial resolution optical images presents challenges related to the size, the accuracy and the complexity of the data. In order to efficiently carry out this task, a multiresolution hierarchical approach is proposed. It enables to progressively segment several images (of increasing resolutions) of a same scene, based on low level criteria. The process, based on binary partition trees, is partially performed in an interactive fashion, and then automatically completed. Experiments on urban images datasets provide encouraging results which may be further used for detection and classification purpose.