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29 September 2006 Classification methods for oil spill detection in ENVISAT ASAR images
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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.
© (2006) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Camilla Brekke and Anne Solberg "Classification methods for oil spill detection in ENVISAT ASAR images", Proc. SPIE 6365, Image and Signal Processing for Remote Sensing XII, 636512 (29 September 2006); https://doi.org/10.1117/12.687569
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