Clustering-Based Extraction of Border Training Patterns for Accurate SVM Classification of Hyperspectral Images

This letter presents an accurate support vector machine (SVM)-based hyperspectral image classification algorithm, which uses border training patterns that are close to the separating hyperplane. Border training patterns are obtained in two consecutive steps. In the first step, clustering is performed to training data of each class, and cluster centers are taken as initial training data for SVM. In the second step, the reduced-size training data composed of cluster centers are used in SVM training, and cluster centers obtained as support vectors at this step are regarded to be located close to the hyperplane border. Original training samples are contained in clusters for which the cluster centers are obtained to be close to the hyperplane border and the corresponding cluster centers are then together assigned as border training patterns. These border training patterns are then used in the training of the SVM classifier. Experimental results show that it is possible to significantly increase the classification accuracy of SVM using border training patterns obtained with the proposed approach.