Hyperspectral image classification based on iterative Support Vector Machine by integrating spatial-spectral information

The well-known difficulty in supervised hyperspectral image classification is the limited availability of training data, which are expensive, and quite difficult to access and to obtain in real remote sensing scenarios. The Support Vector Machine (SVM) technique has been proven to be well suited to classify hyperspectral data by using limited number of training samples. In this paper, modifications over Iterative Support Vector Machine algorithm have been proposed incorporating both spatial and spectral information and correcting the training samples at each iteration in order to increase the classification performance over SVM. In order to demonstrate the effectiveness of the proposed framework, experiments on AVIRIS data over Indian Pine Site (IPS) are conducted to compare the performance of the proposed classification approach against some existing classification techniques such as Linear-SVM, SVM-RBF, ISVM and K-NN. Experimental results demonstrate that the proposed method clearly outperform the well-known classification algorithms.