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A Hybrid Sparsity and Distance-Based Discrimination Detector for Hyperspectral Images
Lu, Xiaoqiang1; Zhang, Wuxia1,2; Li, Xuelong1; Lu, Xiaoqiang (luxq666666@gmail.com)
Department光学影像学习与分析中心
2018-03-01
Source PublicationIEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
ISSN0196-2892
Volume56Issue:3Pages:1704-1717
Contribution Rank1
Abstract

Hyperspectral target detection is an approach which tries to locate targets in a hyperspectral image on the condition of given targets spectrum. Many classical target detectors are based on the linear mixing model (LMM) and sparsity model. The LMM has a poor performance in dealing with the spectral variability. Therefore, more studies focus on the sparsity-based detectors, most of which are based on residual reconstruction. Owing to the fact that the impure dictionary for the test pixel weakens the detection performance and the discrimination ability of residual function has direct influence on the detecting accuracy, the dictionary purity and discriminative residual function are two most important factors affecting the accuracy of sparsity-based target detectors. In order to obtain more purified dictionary and discriminative residual function, this paper proposes a novel sparsity-based detector named the hybrid sparsity and distance-based discrimination (HSDD) detector for target detection in hyperspectral imagery. The residual function is constrained by the discrimination information during the dictionary construction, which enhances the dictionary purification. Only background samples are used to construct the dictionary because it is easier to remove the target pixel than to select it on the condition that majority of pixels are the background pixels. Hence, a purification process is applied for background training samples in order to construct an effective competition between the residual term and discriminative term. Extensive experimental results with four hyperspectral data sets demonstrate that the proposed HSDD algorithm has a better performance than the state-of-the-art algorithms.

SubtypeArticle
KeywordDistance-based Discrimination Hyperspactral Imagery Sparse Representation Target Detection
WOS HeadingsScience & Technology ; Physical Sciences ; Technology
DOI10.1109/TGRS.2017.2767068
Indexed BySCI ; EI
WOS KeywordBinary Hypothesis Model ; Target Detection ; Detection Algorithms ; Object Detection ; Classification
Language英语
WOS Research AreaGeochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
WOS SubjectGeochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000426789800038
EI Accession Number20175004531676
Citation statistics
Document Type期刊论文
Identifierhttp://ir.opt.ac.cn/handle/181661/29993
Collection光学影像学习与分析中心
Corresponding AuthorLu, Xiaoqiang (luxq666666@gmail.com)
Affiliation1.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Ctr Opt Imagery Anal & Learning, State Key Lab Transient Opt & Photon, Xian 710119, Shaanxi, Peoples R China
2.Univ Chinese Acad Sci, Xian Inst Opt & Precis Mech, Beijing 100049, Peoples R China
Recommended Citation
GB/T 7714
Lu, Xiaoqiang,Zhang, Wuxia,Li, Xuelong,et al. A Hybrid Sparsity and Distance-Based Discrimination Detector for Hyperspectral Images[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2018,56(3):1704-1717.
APA Lu, Xiaoqiang,Zhang, Wuxia,Li, Xuelong,&Lu, Xiaoqiang .(2018).A Hybrid Sparsity and Distance-Based Discrimination Detector for Hyperspectral Images.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,56(3),1704-1717.
MLA Lu, Xiaoqiang,et al."A Hybrid Sparsity and Distance-Based Discrimination Detector for Hyperspectral Images".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 56.3(2018):1704-1717.
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