Skip to Main Content
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.