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Détection de zones brûlées après un feu de forêt à partir d'une seule image satellitaire SPOT 5 par techniques SVM

Olivier Zammit 1
1 ARIANA - Inverse problems in earth monitoring
CRISAM - Inria Sophia Antipolis - Méditerranée , Laboratoire I3S - SIS - Signal, Images et Systèmes
Abstract : This PhD investigates the problem of burnt area mapping from high-resolution satellite images. Our approach is based on the use of a single SPOT 5 image, acquired after the fire, to detect automatically the burnt areas.
The method is based on Support Vector Machines (SVM), a supervised classification technique that has greater accuracy and better generalization ability than traditional classifiers. Because all burnt pixels have similar spectral characteristics, while unburnt pixels vary greatly since they belong to different classes (forest, water, urban areas, roads, fields,...), we propose to use the One-Class SVM (OC-SVM), an extension of the original two-class SVM that uses only positive examples for the training and classification steps.
In order to take into account the spatial information provided by the image, the OC-SVM algorithm is used as a region-growing technique, thereby decreasing false positives and improving the boundaries of burnt areas.
Moreover, the samples of burnt areas required for the training step of the SVM are automatically selected from the image histogram.
Finally, the proposed classification approach is tested on several satellite images to validate its effectiveness with respect to vegetation type and burnt surface area. The burnt areas extracted are compared to ground truth provided by the French Space Agency, Infoterra France, SERTIT, local fire brigades, and the French Forestry Office.
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Submitted on : Tuesday, December 9, 2008 - 4:10:08 PM
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Olivier Zammit. Détection de zones brûlées après un feu de forêt à partir d'une seule image satellitaire SPOT 5 par techniques SVM. Mathématiques [math]. Université Nice Sophia Antipolis, 2008. Français. ⟨tel-00345683⟩

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