红外与毫米波学报2018，Vol.37Issue(1)：119-128,10.DOI:10.11972/j.issn.1001-9014.2018.01.021
基于子空间中主成分最优线性预测的高光谱波段选择
Band selection of hyperspectral image based on optimal linear prediction of principal components in subspace
摘要
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 matrixbased 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.关键词
遥感/高光谱图像/波段选择/主成分/线性预测/子空间追踪/谱聚类Key words
remote sensing/hyperspectral image/band selection/principal component/linear prediction/subspace pursuit/spectral clustering分类
信息技术与安全科学引用本文复制引用
吴一全,周杨,盛东慧,叶骁来..基于子空间中主成分最优线性预测的高光谱波段选择[J].红外与毫米波学报,2018,37(1):119-128,10.基金项目
国家自然科学基金(61573183) （61573183）
中国科学院光谱成像重点实验室开放基金项目资助(LSIT201401) （LSIT201401）
江苏高校优势学科建设工程Supported by the National Natural Science Foundation of China(61573183) （61573183）
Supported by the Foundation of Key Laboratory of Spectral Imaging Technology CAS(LSIT201401) （LSIT201401）
Construction of Advantage Disciplines in Jiangsu Universities （）
