Critical class oriented active learning for hyperspectral image classification

In order to focus on the hard classes in a multi-class classification task, a critical class oriented query strategy is proposed, which combines the concepts of "guided learning" and "active learning". In conjunction with the SVM classifier, hard pair classes are first identified based on the instability of the classification hyperplane, whereby category level guidance for which class should be queried next is sought and then provided to the active query system. Samples with higher possibility of belonging to these classes as evaluated by the current learner are queried first. Two methods are proposed. The first method (SVM-CC) simply conducts category level query. The second method (SVM- CCMS) further incorporates the uncertainty measurement based on the idea of margin sampling, so as to directly focus on the most informative samples from the identified "trouble classes". Experiments are conducted on AVIRIS and Hyperion data. Results are compared to Random Sampling and the state-of-the-art active learning method SVM based simple margin sampling SVMMS. Superior performance is obtained, whereas hard classes are successfully identified first.