Spatial-Spectral Total Variation Regularized Low-Rank Tensor Decomposition for Hyperspectral Image Denoising

doi:10.1109/TGRS.2018.2833473

	Spatial-Spectral Total Variation Regularized Low-Rank Tensor Decomposition for Hyperspectral Image Denoising
	Fan, Haiyan 1; Li, Chang 2; Guo, Yulan 3,4; Kuang, Gangyao 3; Ma, Jiayi 5
	2018-10-01
发表期刊	IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
ISSN	0196-2892
卷号	56 期号:10 页码:6196-6213
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.
关键词	Hyperspectral image (HSI) denoising low-rank tensor factorization (LRTF) spatial-spectral total variation (SSTV)
DOI	10.1109/TGRS.2018.2833473
收录类别	SCI
语种	英语
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
WOS类目	Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:000446300700048
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：128[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/4869
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.