Penalized Linear Discriminant Analysis of Hyperspectral Imagery for Noise Removal | |
Lu, Ming1,2,3; Hu, Luojia4; Yue, Tianxiang1; Chen, Ziyue4; Chen, Bin4; Lu, Xiaoqiang5; Xu, Bing4,6,7 | |
作者部门 | 光学影像学习与分析中心 |
2017-03-01 | |
发表期刊 | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS |
ISSN | 1545-598X |
卷号 | 14期号:3页码:359-363 |
产权排序 | 5 |
摘要 | The existence of noise in hyperspectral imagery (HSI) seriously affects image quality. Noise removal is one of the most important and challenging tasks to complete before hyperspectral information extraction. Though many advances have been made in alleviating the effect of noise, problems, including a high correlation among bands and predefined structure of noise covariance, still prevent us from the effective implementation of hyperspectral denoising. In this letter, a new algorithm named the penalized linear discriminant analysis (PLDA) and noise adjusted principal components transformation (NAPCT) was proposed. PLDA was applied to search for the best noise covariance structure, while the NAPCT was employed to remove the noise. The results of the tests with both HJ-1A HSI and EO-1 Hyperion showed that the proposed PLDA-NAPCT method could remove the noise effectively and that it could preserve the spectral fidelity of the restored hyperspectral images. Specifically, the recovered spectral curves using the proposed method are visually more similar to the original image compared with the control methods; quantitative matrices, including the noise reduction ration and mean relative deviation, also showed that the PLDA-NAPCT produced less bias than the control methods. Furthermore, the PLDA-NAPCT method is sensor-independent, and it could be easily adapted for removing the noise from different sensors. |
文章类型 | Article |
关键词 | Hyperspectral Imagery (Hsi) Noise Removal Penalized Linear Discriminant Analysis (Plda) Principal Components Transformation |
WOS标题词 | Science & Technology ; Physical Sciences ; Technology |
DOI | 10.1109/LGRS.2016.2643001 |
收录类别 | SCI ; EI |
关键词[WOS] | TRANSFORMATION |
语种 | 英语 |
WOS研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
项目资助者 | National Key Research and Development Program of China(2016YFA0600104) ; National Natural Science Foundation of China(91325204 ; 41421001) |
WOS类目 | Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology |
WOS记录号 | WOS:000395908600017 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.opt.ac.cn/handle/181661/28752 |
专题 | 光谱成像技术研究室 |
作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Beijing Normal Univ, Beijing 100875, Peoples R China 4.Beijing Normal Univ, Coll Global Change & Earth Syst Sci, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China 5.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Xian 710119, Peoples R China 6.Tsinghua Univ, Ctr Earth Syst Sci, Minist Educ, Key Lab Earth Syst Modelling, Beijing 100084, Peoples R China 7.Tsinghua Univ, Sch Environm, Beijing 100084, Peoples R China |
推荐引用方式 GB/T 7714 | Lu, Ming,Hu, Luojia,Yue, Tianxiang,et al. Penalized Linear Discriminant Analysis of Hyperspectral Imagery for Noise Removal[J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,2017,14(3):359-363. |
APA | Lu, Ming.,Hu, Luojia.,Yue, Tianxiang.,Chen, Ziyue.,Chen, Bin.,...&Xu, Bing.(2017).Penalized Linear Discriminant Analysis of Hyperspectral Imagery for Noise Removal.IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,14(3),359-363. |
MLA | Lu, Ming,et al."Penalized Linear Discriminant Analysis of Hyperspectral Imagery for Noise Removal".IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 14.3(2017):359-363. |
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