Hyperspectral feature recognition based on kernel PCA and relational perspective map  

作　　者：苏红军 盛业华 

机构地区：[1]Key Laboratory of Virtual Geographic Environment(Ministry of Education),Nanjing Normal University

出　　处：《Chinese Optics Letters》2010年第8期811-814,共4页中国光学快报（英文版）

基　　金：supported by the National Natural Science Foundation of China(No.40901200);the China Scholarship Council(No.2009686004);the Outstanding Postgraduate Dissertation Cultivating Program of Nanjing Normal University (No.1243211601040)

摘　　要：A novel joint kernel principal component analysis （PCA） and relational perspective map （RPM） method called KPmapper is proposed for hyperspectral dimensionality reduction and spectral feature recognition. Kernel PCA is used to analyze hyperspectral data so that the major information corresponding to features can be better extracted. RPM is used to visualize hyperspectral data through two-dimensional （2D） maps, and it is an efficient approach to discover regularities and extract information by partitioning the data into pieces and mapping them onto a 2D space. The experimental results prove that the KPmapper algorithm can effectively obtain the intrinsic features in nonlinear high dimensional data. It is useful and impressing for dimensionality reduction and spectral feature recognition.A novel joint kernel principal component analysis （PCA） and relational perspective map （RPM） method called KPmapper is proposed for hyperspectral dimensionality reduction and spectral feature recognition. Kernel PCA is used to analyze hyperspectral data so that the major information corresponding to features can be better extracted. RPM is used to visualize hyperspectral data through two-dimensional （2D） maps, and it is an efficient approach to discover regularities and extract information by partitioning the data into pieces and mapping them onto a 2D space. The experimental results prove that the KPmapper algorithm can effectively obtain the intrinsic features in nonlinear high dimensional data. It is useful and impressing for dimensionality reduction and spectral feature recognition.

关 键 词：Clustering algorithms Content based retrieval Principal component analysis 

分 类 号：P237[天文地球—摄影测量与遥感]