Hyperspectral remote sensing classification based on SVM with end-member extraction

In order to enhance the accuracy of hyperspectral remote sensing classification, a classification method based on SVM with end-member extraction is presented. Firstly, the end-members are extracted using pure pixel index approach, and then the ground target is identified based on the spectral feature fitting , followed by the spectral classification of the hyperspectral remote sensing images with the Support Vector Machines. The experiment results indicated that the validity and efficiency of our method are more accurately than the traditional SVM solutions which simply use the regions of interest selected from image as the training samples.