Locality-preserving sparse representation-based classification in hyperspectral imagery

被引:11
|
作者
Gao, Lianru [1 ]
Yu, Haoyang [1 ,2 ]
Zhang, Bing [1 ]
Li, Qingting [1 ]
机构
[1] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth Sci, 9 Dengzhuang South Rd, Beijing 100094, Peoples R China
[2] Univ Chinese Acad Sci, 19A Yuquan Rd, Beijing 100049, Peoples R China
来源
基金
中国国家自然科学基金;
关键词
hyperspectral image classification; locality-preserving projections; sparse representation; COLLABORATIVE REPRESENTATION; SUBSPACE; REDUCTION;
D O I
10.1117/1.JRS.10.042004
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
This paper proposes to combine locality-preserving projections (LPP) and sparse representation (SR) for hyperspectral image classification. The LPP is first used to reduce the dimensionality of all the training and testing data by finding the optimal linear approximations to the eigenfunctions of the Laplace Beltrami operator on the manifold, where the high dimensional data lies. Then, SR codes the projected testing pixels as sparse linear combinations of all the training samples to classify the testing pixels by evaluating which class leads to the minimum approximation error. The integration of LPP and SR represents an innovative contribution to the literature. The proposed approach, called locality-preserving SR-based classification, addresses the imbalance between high dimensionality of hyperspectral data and the limited number of training samples. Experimental results on three real hyperspectral data sets demonstrate that the proposed approach outperforms the original counterpart, i.e., SR-based classification. (C) 2016 Society of Photo-Optical Instrumentation Engineers (SPIE)
引用
收藏
页数:15
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