中文摘要：

光谱端元提取是对高光谱数据进一步分析的重要前提。在各种端元提取算法中,N-FINDR算法因其全自动和选择效果较好等优点受到了广泛的关注。然而样本的排序对该算法的端元提取会造成一定影响,并且传统N-FINDR算法需要根据端元的个数进行降维处理,从而限制了该算法的应用。实际高光谱数据中存在的同一地物在高维空间中非紧密团聚现象也对端元提取增加了难度。为此该文提出改进的算法停机准则和数据特征预处理方法,并使用支持向量机对提取到的端元进行二次提取。实验结果表明,改进的停机准则进一步增加了由端元向量组组成的凸体体积。数据特征预处理和基于支持向量机的二次端元提取分别提升了数据的可分性和提取到端元的精度。

英文摘要：

Spectral endmember extraction is an important pretreatment for the further analysis of hyperspectral data.Regarding many kinds of endmember extraction algorithms,N-FINDR algorithm is widely utilized for its full-automation and better endmember extraction performance.However,the order of the samples has a certain effect on the endmember extraction,and traditional N-FINDR algorithm also needs to reduce the dimensionality based on the number of the endmembers,which will limit its application.In the actual hyperspectral data,the incompact clustering of the same species presented in the high dimensional space also increases the difficulty of endmember extraction.So this paper proposed an improved stop rule and the pretreatment of the features,and utilizing Support Vector Machine（SVM） to conduct the second endmember extraction.Experiments show that the improved stop rule further increased the volume of the convex polyhedron composed of the endmembers.The pretreatment of the features and the second SVM endmember extraction increase the separability of the data and the precision of the extracted endmembers respectively.