Band selection of hyperspectral image based on optimal linear prediction of principal components in subspace
Received:February 15, 2017  Revised:June 15, 2017  download
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Author NameAffiliationE-mail
WU Yi-Quan Nanjing University of Aeronautics and Astronautics、Key Laboratory of Spectral Imaging Technology CASXi’an Institute of Optics and Precision Mechanics of CAS nuaaimage@163.com 
ZHOU Yang Nanjing University of Aeronautics and Astronautics 1414032393@qq.com 
SHENG Dong-Hui Nanjing University of Aeronautics and Astronautics  
YE Xiao-Lai Nanjing University of Aeronautics and Astronautics  
Abstract:In the case of hyperspectral anomaly detection, in order to make hyperspectral low-dimensional data preserve the spectral information more completely, a band selection method based on the optimal linear prediction of principal components in subspace was proposed. Hyperspectral bands are divided into different subspaces by spectral clustering with the improved correlation measure. The principal component analysis (PCA) of bands is presented in each subspace, and main components are selected as the reconstructed targets. The subspace tracking method serves as the search strategy, and several bands are selected from each subspace to perform the joint optimal linear prediction of reconstructed targets. The selected bands in each subspace are combined to obtain the optimal band subset. Experimental results show that, the proposed method can reconstruct the original data more completely. Compared with original data, and the band subsets obtained by adaptive band selection (ABS) method, linear prediction (LP) method, maximum-variance principal component analysis (MVPCA) method, auto correlation matrix-based band selection (ACMBS) method and optimal combination factors-based band selection (OCFBS) method, the band subset of proposed method has superior performance of anomaly detection.
keywords:remote sensing  hyperspectral image  band selection  principal component  linear prediction  subspace pursuit  spectral clustering
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Copyright:《Journal of Infrared And Millimeter Waves》