Mixed KPCA结合纹理特征的SVM盐碱土信息提取

核函数是核主成分分析（Kernel Principal Component Analysis，KPCA）的核心，目前使用的核函数都是单一核函数。尝试通过将光谱角径向基核函数（Spectral Angle Radial Basis Function，SA-RBF）与RBF组合形成混合核函数。在研究中，利用基于该混合核函数的KPCA进行特征提取，将其光谱特征波段和纹理特征相结合用于盐碱土的SVM分类，将分类结果与其他SVM分类进行比较，结果表明：该方法优于其他SVM方法，能有效提取玛纳斯河流域绿洲区的盐碱土专题信息，分类精度是89.000%，kappa系数是0.876。

Method of salt-affected soil information extraction based on Support Vector Machine with Mixed KPCAand texture features

The kernel function is key part of Kernel Principal Component Analysis,KPCA.The present used kernel functions are simple kernel functions.This paper makes an effort to present a mixed kernel function by combining Spectral Angle Radial Basis Function,SA-RBF with RBF.In this study,extracting spectral feature bands using KPCA based on the mixed kernel function,the SVM is used to classify salt-affected soil using a combination of spectral features and texture features as a data source.In addition,the combined approach is compared with other SVM methods.The results reveal that the proposed SVM method used here can effectively extract salt-affected soil thematic information for the Manasi River Oasis.Especially,the overall accuracy of this method is 89.000% and the kappa coefficient is 0.876,which indicates that this method is better than other classification methods.