Pixel classification is one of the most basic and important contents of hyperspectal imagery(HSI) analysis,and SVM based method is very popular in HSI classification for its high efficiency.The importance of samples,features,and classes,however,is not reflected in original SVM based classification model,and the classification effect is deteriorated consequently.In this study,the distance of each sample deviating from its class-center was mapped into the sample as weighting coefficient.And within-class scatter matrix was introduced into the feature weighting measure,and the diagonal elements in SVM equation system were adjusted for the purpose of class weighting.The weighted methods can be used solely or jointly.Experiments show that the proposed weighting methods are helpful to improve the effect of HSI classification.