Active learning for identifying marine oil spills using 10-year RADARSAT data

The potential of active learning (AL) methods for improving the marine oil spills identification system is exploited using 10-year (2004-2013) RADARSAT data. Six basic AL methods are proposed according to the uncertainty criteria and coupled with the support vector machine (SVM) classifier. As many as 56 commonly used features are used for the classification. The AUC measures are estimated using the 6-fold cross validation technique to achieve bias-reduced evaluation of performance of the AL-based classifiers. The experiment results show that 22 to 74 percent of samples could be reduced for training SVM classifiers with certain destination performance, if the proper AL method such as AL-6 is selected and the criteria of exploitation and exploration could further improve the performance.

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