Spatial-Spectral Total Variation Regularized Low-Rank Tensor Decomposition for Hyperspectral Image Denoising
文献类型：期刊论文
| 作者 | Fan, Haiyan1; Li, Chang2; Guo, Yulan3,4; Kuang, Gangyao3; Ma, Jiayi5 |
| 刊名 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
 |
| 出版日期 | 2018-10-01 |
| 卷号 | 56期号:10页码:6196-6213 |
| ISSN号 | 0196-2892 |
| 关键词 | Hyperspectral image (HSI) denoising low-rank tensor factorization (LRTF) spatial-spectral total variation (SSTV) |
| DOI | 10.1109/TGRS.2018.2833473 |
| 英文摘要 | Several bandwise total variation (TV) regularized low-rank (LR)-based models have been proposed to remove mixed noise in hyperspectral images (HSIs). These methods convert high-dimensional HSI data into 2-D data based on LR matrix factorization. This strategy introduces the loss of useful multiway structure information. Moreover, these bandwise TV-based methods exploit the spatial information in a separate manner. To cope with these problems, we propose a spatial-spectral TV regularized LR tensor factorization (SSTV-LRTF) method to remove mixed noise in HSIs. From one aspect, the hyperspectral data are assumed to lie in an LR tensor, which can exploit the inherent tensorial structure of hyperspectral data. The LRTF-based method can effectively separate the LR clean image from sparse noise. From another aspect, HSIs are assumed to be piecewisely smooth in the spatial domain. The TV regularization is effective in preserving the spatial piecewise smoothness and removing Gaussian noise. These facts inspire the integration of the LRTF with TV regularization. To address the limitations of bandwise TV, we use the SSTV regularization to simultaneously consider local spatial structure and spectral correlation of neighboring bands. Both simulated and real data experiments demonstrate that the proposed SSTV-LRTF method achieves superior performance for HSI mixed-noise removal, as compared to the state-of-the-art TV regularized and LR-based methods. |
| 资助项目 | National Natural Science Foundation of China[61503288] ; National Natural Science Foundation of China[61601481] ; National Natural Science Foundation of China[61602499] ; National Natural Science Foundation of China[61471371] ; National Postdoctoral Program for Innovative Talents[BX201600172] ; China Postdoctoral Science Foundation |
| WOS研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| WOS记录号 | WOS:000446300700048 |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/4868]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Fan, Haiyan |
| 作者单位 | 1.Space Engn Univ, Sch Space Command, Beijing 101416, Peoples R China 2.Hefei Univ Technol, Dept Biomed Engn, Hefei 230009, Anhui, Peoples R China 3.Natl Univ Def Technol, Sch Elect Sci & Engn, Changsha 410073, Hunan, Peoples R China 4.Chinese Acad Sci, Inst Comp Technol, Beijing 100089, Peoples R China 5.Wuhan Univ, Elect Informat Sch, Wuhan 430072, Hubei, Peoples R China |
| 推荐引用方式 GB/T 7714 | Fan, Haiyan,Li, Chang,Guo, Yulan,et al. Spatial-Spectral Total Variation Regularized Low-Rank Tensor Decomposition for Hyperspectral Image Denoising[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2018,56(10):6196-6213. |
| APA | Fan, Haiyan,Li, Chang,Guo, Yulan,Kuang, Gangyao,&Ma, Jiayi.(2018).Spatial-Spectral Total Variation Regularized Low-Rank Tensor Decomposition for Hyperspectral Image Denoising.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,56(10),6196-6213. |
| MLA | Fan, Haiyan,et al."Spatial-Spectral Total Variation Regularized Low-Rank Tensor Decomposition for Hyperspectral Image Denoising".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 56.10(2018):6196-6213. |
入库方式： OAI收割
来源：计算技术研究所
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