Publications sur polygonal approximation

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Article de conférence

1 - Mixing Geometric and Radiometric Features for Change Classification. A. Fournier et X. Descombes et J. Zerubia. Dans Proc. SPIE Symposium on Electronic Imaging, San Jose, USA, janvier 2008. Mots-clés : Change detection, directional Statistics, polygonal approximation, Classification. Copyright : Copyright 2008 SPIE and IS&T. This paper was published in the proceedings of IS&T/SPIE 20th Annual Symposium on Electronic Imaging and is made available as an electronic reprint (preprint) with permission of SPIE and IS&T. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited. @INPROCEEDINGS{fournier_spie08, author = {Fournier, A. and Descombes, X. and Zerubia, J.}, title = {Mixing Geometric and Radiometric Features for Change Classification}, year = {2008}, month = {janvier}, booktitle = {Proc. SPIE Symposium on Electronic Imaging}, address = {San Jose, USA}, url = {http://hal.inria.fr/inria-00269853/fr/}, keyword = {Change detection, directional Statistics, polygonal approximation, Classification} } 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.

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