Support Vector Machines for burnt area discrimination

Olivier Zammit 1 Xavier Descombes 1 Josiane Zerubia 1
1 ARIANA - Inverse problems in earth monitoring
CRISAM - Inria Sophia Antipolis - Méditerranée , Laboratoire I3S - SIS - Signal, Images et Systèmes
Abstract : This report addresses the problem of burnt area discrimination using remote sensing images. The detection is based on a single post-fire image acquired by SPOT 5 satellite. To delineate the burnt areas, we use a recent classification method called Support Vectors Machines (SVM). This approach is compared to more conventional classifiers such as K-means or K-nearest neighbours which are widely used in image processing. We also proposed a new automatic classification approach combining K-means and SVM. The results given by the different methods are finally compared to ground truths on various burnt areas.
Type de document :
[Research Report] RR-6343, INRIA. 2007, pp.37
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Soumis le : jeudi 8 novembre 2007 - 10:13:52
Dernière modification le : lundi 5 novembre 2018 - 15:52:01
Document(s) archivé(s) le : mardi 21 septembre 2010 - 15:07:28


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  • HAL Id : inria-00185101, version 2



Olivier Zammit, Xavier Descombes, Josiane Zerubia. Support Vector Machines for burnt area discrimination. [Research Report] RR-6343, INRIA. 2007, pp.37. 〈inria-00185101v2〉



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