Accurate SVM classification using border training patterns

This paper proposes to use border training patterns in order to improve Support Vector Machine (SVM) classification accuracy of hyperspectral images. In the proposed approach, border training patterns which are close to the separating hyperplane, are obtained in two consecutive steps and considered as final training set. In the first step, clustering is performed to the full initial training data of each class. Then, cluster centers of each class are taken as the reduced size training data and forwarded to the second step. In the second step, this reduced size training data is used in the training of SVM and cluster centers which are obtained as support vectors at this step are regarded to be located close to the hyperplane border. Finally, cluster centers which are found as support vectors and original training samples contained in these clusters only are assigned as border training patterns. Experimental results are presented to show that the proposed approach improves SVM classification accuracy.