Combining one-class support vector machines and hysteresis thresholding: Application to burnt area mapping

Abstract

In this paper, we focus on burnt area mapping using a single post-fire high resolution satellite image. Concerning image classification problems, Support Vector Machines (SVM) have shown great performances. They learn how to distinguish two classes by finding the optimal hyperplane which maximizes the distance between the hyperplane and the training… (More)

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Cite this paper

@article{Zammit2008CombiningOS, title={Combining one-class support vector machines and hysteresis thresholding: Application to burnt area mapping}, author={Olivier Zammit and Xavier Descombes and Josiane Zerubia}, journal={2008 16th European Signal Processing Conference}, year={2008}, pages={1-5} }