Imbalanced Hyperspectral Image Classification Based on Maximum Margin

Hyperspectral remote sensing images own rich spectral information to distinguish different land-cover classes. Sometimes, it may encounter the case that some classes have much fewer pixels than other classes. In this case, traditional classification methods are not appropriate because they are prone to assign all the pixels to the classes with a large number of pixels. For such an imbalanced problem, ensemble learning is a good method by partitioning the majority classes into different groups with small sizes. However, the existing ensemble schemes are independent of classifiers, which will not get the best performance for a certain classifier. In this letter, the selected classifier, i.e., a support vector machine (SVM), is considered in an ensemble procedure to improve the classification accuracy. Specifically, the criterion of the SVM, i.e., the maximum margin, is adopted to guide the ensemble learning procedure for imbalanced hyperspectral image classification. Experiments state that our method obtains higher classification accuracy than the SVM and several representative imbalanced classification methods for hyperspectral images.