摘要
传统PPI算法采用最大噪声分离(MNF)方法进行降维,MNF变换中均设定数据之间线性相关,在某些情况下会使变换后的结果具有某些人为特征,在降维过程中会丢失信号较弱的信息,导致端元数量少;分段主成分分析(SPCA)降维方法具有不改变图像的物理意义,且信息保存较完整的优势。该研究采用不同降维方法利用纯净像元指数法(PPI)对不同下垫面地表提取端元,结果表明,在地表破碎区域SPCA降维后可找出信号较弱的端元提取的端元数量多与MNF降维提取的端元数,而地物聚集区MNF降维方法提取的端元质量更好。研究结果可以为不同下垫面的高光谱影像端元提取以及降维方法的选择提供参考。
The traditional PPI algorithm uses the maximum noise separation（MNF）to reduce dimension,MNF trans-form was set linear correlation between the datas,and in some cases,the results of the transform will have some hu-man characteristics.In the process of dimension reduction,the weak signal will be lost,which leads to a small num-ber of end elements.Segmented principal component analysis（SPCA）does not change the physical meaning of theimage and the information will be relatively complete preservation.This paper use the pure pixel index（PPI）for dif-ferent dimensionality reduction methods and for different underlying surface to extract endmember.The results showsthat the SPCA dimensionality reduction is more suit in broken underlying surface,it could find the weak signal end-member;and the MNF dimensionality reduction will find the better quality endmember in ground gathering area.Theresults of this research can provide reference for the endmember extraction of hyperspectral image and the selectionof dimension reduction method for different underlying surfaces.
出处
《安徽农学通报》
2017年第1期13-17,75,共6页
Anhui Agricultural Science Bulletin
