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Proceedings Paper

Classification methods for oil spill detection in ENVISAT ASAR images
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Paper Abstract

In this paper, we present results from a study on classifiers for automatic oil slick classification in ENVISAT ASAR images. First, based on our basic statistical classifier, we improve the classification performance by introducing regularization of the covariance matrixes. The new improved classifier reduces the false alarm rate from 19.6% to 13.1%. Second, we compare the statistical classifier with SVM, finding that the statistical classifier outranks SVM for this particular application. Experiments are done on a set of 103 SAR images.

Paper Details

Date Published: 29 September 2006
PDF: 11 pages
Proc. SPIE 6365, Image and Signal Processing for Remote Sensing XII, 636512 (29 September 2006); doi: 10.1117/12.687569
Show Author Affiliations
Camilla Brekke, Norwegian Defence Research Establishment (Norway)
Univ. of Oslo (Norway)
Anne Solberg, Univ. of Oslo (Norway)


Published in SPIE Proceedings Vol. 6365:
Image and Signal Processing for Remote Sensing XII
Lorenzo Bruzzone, Editor(s)

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