Classifying remote sensing data with support vector machines and imbalanced training data

The classification of remote sensing data with imbalanced training data is addressed. The classification accuracy of a supervised method is affected by several factors, such as the classifier algorithm, the input data and the available training data. The use of an imbalanced training set, i.e., the number of training samples from one class is much smaller than from other classes, often results in low classification accuracies for the small classes. In the present study support vector machines (SVM) are trained with imbalanced training data. To handle the imbalanced training data, the training data are resampled (i.e., bagging) and a multiple classifier system, with SVM as base classifier, is generated. In addition to the classifier ensemble a single SVM is applied to the data, using the original balanced and the imbalanced training data sets. The results underline that the SVM classification is affected by imbalanced data sets, resulting in dominant lower classification accuracies for classes with fewer training data. Moreover the detailed accuracy assessment demonstrates that the proposed approach significantly improves the class accuracies achieved by a single SVM, which is trained on the whole imbalanced training data set.