THE INVESTIGATION OF SENSITIVITY OF SVM CLASSIFIER RESPECT TO THE NUMBER OF FETURES AND THE NUMBER OF TRAINING SAMPLES

Abstract. Supervised classification of hyperspectral images is a difficult task due to the unbalance between the high dimensionality of the data and the limited availability of labeled training samples. Recently support vector machine (SVM), has received considerable attention for classifying high dimensional data and is applied successfully for classification of hyperspectral images because it discriminates classes by a geometrical criterion not by statistical criteria. In this paper, we investigate sensitivity of SVM classifier respect to two factors. The first factor is the dimensional of data (the number of features) and the second factor is the number of training samples. We evaluate the effect of these factors on the performance of classification in the point of view both accuracy and reliability. Experiments are carried out on the three different common used hyperspectral datasets, Indian pines, Pavia University and Salinas.