An Algorithm of Weighted “1 V m” SVM Multi-classification for Hyperspectral Remote Sensing Image with NPA

According to the SVM computation theory and the features of hyperspectral remote sensing(RS) image data,the optimal hyperplane between two classes is computed by the nearest points algorithm(NPA).Reasonable weight indicators are designed for each class and a new weighted "1 V m" SVM based on NPA is proposed to achieve Hyperspectral RS image classification.The new algorithm can reduce the computational complexity and calculation of SVM,and improve SVM feasibilities and efficiencies for hyperspectral RS image classification.Finally,a test was carried out on OMIS image and good results are obtained.