Penalized Linear Discriminant Analysis of Hyperspectral Imagery for Noise Removal
文献类型：期刊论文
| 作者 | Lu, Ming1,3,4; Hu, Luojia5; Yue, Tianxiang3; Chen, Ziyue5; Chen, Bin5; Lu, Xiaoqiang6; Xu, Bing2,5,7 |
| 刊名 | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
 |
| 出版日期 | 2017-03-01 |
| 卷号 | 14期号:3页码:359-363 |
| 关键词 | Hyperspectral imagery (HSI) noise removal penalized linear discriminant analysis (PLDA) principal components transformation |
| ISSN号 | 1545-598X |
| DOI | 10.1109/LGRS.2016.2643001 |
| 通讯作者 | Yue, Tianxiang() ; Xu, Bing() |
| 英文摘要 | 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. |
| WOS关键词 | TRANSFORMATION |
| 资助项目 | National Key Research and Development Program of China[2016YFA0600104] ; National Natural Science Foundation of China[91325204] ; National Natural Science Foundation of China[41421001] |
| WOS研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000395908600017 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Key Research and Development Program of China ; National Natural Science Foundation of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/64855]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Yue, Tianxiang; Xu, Bing |
| 作者单位 | 1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.Tsinghua Univ, Ctr Earth Syst Sci, Minist Educ, Key Lab Earth Syst Modelling, Beijing 100084, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 4.Beijing Normal Univ, Beijing 100875, Peoples R China 5.Beijing Normal Univ, Coll Global Change & Earth Syst Sci, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China 6.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Xian 710119, 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. |
入库方式： OAI收割
来源：地理科学与资源研究所
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