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UNSUPERVISED ONE-CLASS SVM USING A WATERSHED ALGORITHM AND HYSTERESIS THRESHOLDING TO DETECT BURNT AREAS
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.
Cited literature [12 references]
https://hal.inria.fr/inria-00316297
Contributor : Olivier Zammit <>
Submitted on : Wednesday, September 3, 2008 - 9:40:21 AM
Last modification on : Monday, October 12, 2020 - 10:30:11 AM
Long-term archiving on: : Thursday, June 3, 2010 - 6:04:27 PM
Contributor : Olivier Zammit <>
Submitted on : Wednesday, September 3, 2008 - 9:40:21 AM
Last modification on : Monday, October 12, 2020 - 10:30:11 AM
Long-term archiving on: : Thursday, June 3, 2010 - 6:04:27 PM
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PRIA-08-zammit.pdf
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