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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)
作者部门光学影像学习与分析中心
2018-03-01
发表期刊IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
ISSN0196-2892
卷号56期号:3页码:1704-1717
产权排序1
摘要

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.

文章类型Article
关键词Distance-based Discrimination Hyperspactral Imagery Sparse Representation Target Detection
WOS标题词Science & Technology ; Physical Sciences ; Technology
DOI10.1109/TGRS.2017.2767068
收录类别SCI ; EI
关键词[WOS]Binary Hypothesis Model ; Target Detection ; Detection Algorithms ; Object Detection ; Classification
语种英语
WOS研究方向Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号WOS:000426789800038
EI入藏号20175004531676
引用统计
被引频次:8[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.opt.ac.cn/handle/181661/29993
专题光学影像学习与分析中心
通讯作者Lu, Xiaoqiang (luxq666666@gmail.com)
作者单位1.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
推荐引用方式
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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