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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.
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Submitted on : Thursday, November 8, 2007 - 10:13:52 AM
Last modification on : Friday, February 4, 2022 - 3:14:48 AM
Long-term archiving on: : Tuesday, September 21, 2010 - 3:07:28 PM


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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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