Title :
Classifiers and Confidence Estimation for Oil Spill Detection in ENVISAT ASAR Images
Author :
Brekke, Camilla ; Solberg, Anne H S
Abstract :
An improved classification approach is proposed for automatic oil spill detection in synthetic aperture radar images. The performance of statistical classifiers and support vector machines is compared. Regularized statistical classifiers prove to perform the best on this problem. To allow the user to tune the system with respect to the tradeoff between the number of true positive alarms and the number of false positives, an automatic confidence estimator has been developed. Combining the regularized classifier with confidence estimation leads to acceptable performance.
Keywords :
environmental science computing; pattern classification; remote sensing by radar; spaceborne radar; statistical analysis; support vector machines; synthetic aperture radar; water pollution measurement; ENVISAT ASAR images; automatic confidence estimator; automatic oil spill detection; confidence estimation; false positive; regularized statistical classifiers; support vector machine; synthetic aperture radar images; true positive alarm; Event detection; Gravity; Image segmentation; Informatics; Petroleum; Radar detection; Support vector machine classification; Support vector machines; Synthetic aperture radar; Testing; Classification; oil spill; synthetic aperture radar (SAR);
Journal_Title :
Geoscience and Remote Sensing Letters, IEEE
DOI :
10.1109/LGRS.2007.907174
