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Proceedings Paper

Mixing geometric and radiometric features for change classification
Author(s): Alexandre Fournier; Xavier Descombes; Josiane Zerubia
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Paper Abstract

Most basic change detection algorithms use a pixel-based approach. Whereas such approach is quite well defined for monitoring important area changes (such as urban growth monitoring) in low resolution images, an object based approach seems more relevant when the change detection is specifically aimed toward targets (such as small buildings and vehicles). In this paper, we present an approach that mixes radiometric and geometric features to qualify the changed zones. The goal is to establish bounds (appearance, disappearance, substitution ...) between the detected changes and the underlying objects. We proceed by first clustering the change map (containing each pixel bitemporal radiosity) in different classes using the entropy-kmeans algorithm. Assuming that most man-made objects have a polygonal shape, a polygonal approximation algorithm is then used in order to characterize the resulting zone shapes. Hence allowing us to refine the primary rough classification, by integrating the polygon orientations in the state space. Tests are currently conducted on Quickbird data.

Paper Details

Date Published: 26 February 2008
PDF: 12 pages
Proc. SPIE 6814, Computational Imaging VI, 681408 (26 February 2008); doi: 10.1117/12.777084
Show Author Affiliations
Alexandre Fournier, INRIA (France)
Xavier Descombes, INRIA (France)
Josiane Zerubia, INRIA (France)

Published in SPIE Proceedings Vol. 6814:
Computational Imaging VI
Charles A. Bouman; Eric L. Miller; Ilya Pollak, Editor(s)

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