UNSUPERVISED ONE-CLASS SVM USING A WATERSHED ALGORITHM AND HYSTERESIS THRESHOLDING TO DETECT BURNT AREAS

Olivier Zammit 1 Xavier Descombes 1 Josiane Zerubia 1
1 ARIANA - Inverse problems in earth monitoring
CRISAM - Inria Sophia Antipolis - Méditerranée , SIS - Signal, Images et Systèmes
Abstract : This paper addresses the issue of color image classification. Support Vector Machines (SVM) have shown great performances concerning classification problems but require positive and negative training sets. One-Class SVM allow to avoid the negative training set choice. We also propose to automatically select the positive training set by using the watershed algorithm on the 3-D histogram. Finally a hysteresis thresholding allow to improve the cluster edges. Our method is applied to multispectral satellite images in order to assess burnt areas after a forest fire. The results are compared to official ground truths to validate the approach.
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Communication dans un congrès
Pattern Recognition and Image Analysis (PRIA), Sep 2008, Nijni Novgorod, Russia. 2008
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Dernière modification le : mercredi 31 janvier 2018 - 10:24:04
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Olivier Zammit, Xavier Descombes, Josiane Zerubia. UNSUPERVISED ONE-CLASS SVM USING A WATERSHED ALGORITHM AND HYSTERESIS THRESHOLDING TO DETECT BURNT AREAS. Pattern Recognition and Image Analysis (PRIA), Sep 2008, Nijni Novgorod, Russia. 2008. 〈inria-00316297〉

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