Spectral-Spatial Kernel Regularized for Hyperspectral Image Denoising

doi:10.1109/TGRS.2014.2385082

OPT OpenIR > 光谱成像技术研究室

	Spectral-Spatial Kernel Regularized for Hyperspectral Image Denoising
	Yuan, Yuan; Zheng, Xiangtao; Lu, Xiaoqiang
	2015-07-01
发表期刊	IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
卷号	53 期号:7 页码:3815-3832
摘要	Noise contamination is a ubiquitous problem in hyperspectral images (HSIs), which is a challenging and promising theme in many remote sensing applications. A large number of methods have been proposed to remove noise. Unfortunately, most denoising methods fail to take full advantages of the high spectral correlation and to simultaneously consider the specific noise distributions in HSIs. Recently, a spectral-spatial adaptive hyperspectral total variation (SSAHTV) was proposed and obtained promising results. However, the SSAHTV model is insensitive to the image details, which makes the edges blur. To overcome all of these drawbacks, a spectral-spatial kernel method for HSI denoising is proposed in this paper. The proposed method is inspired by the observation that the spectral-spatial information is highly redundant in HSIs, which is sufficient to estimate the clear images. In this paper, a spectral-spatial kernel regularization is proposed to maintain the spectral correlations in spectral dimension and to match the original structure between two spatial dimensions. Moreover, an adaptive mechanism is developed to balance the fidelity term according to different noise distributions in each band. Therefore, it cannot only suppress noise in the high-noise band but also preserve information in the low-noise band. The reliability of the proposed method in removing noise is experimentally proved on both simulated data and real data.
文章类型	Article
关键词	Adaptive Kernel Hyperspectral Image (Hsi) Denoising Nonlocal Means (Nlm) Spectral-spatial Kernel Regularization
WOS标题词	Science & Technology ; Physical Sciences ; Technology
DOI	10.1109/TGRS.2014.2385082
收录类别	SCI ; EI
关键词[WOS]	PRINCIPAL COMPONENT ANALYSIS ; NOISE-REDUCTION ; DIMENSIONALITY REDUCTION ; NONLOCAL REGULARIZATION ; SPARSE REPRESENTATION ; QUALITY ASSESSMENT ; ALGORITHMS ; SELECTION ; SHRINKING ; REMOVAL
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:000351461000021
引用统计	被引频次：69[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/181661/24117
专题	光谱成像技术研究室
作者单位	Chinese Acad Sci, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Ctr Opt Imagery Anal & Learning, Xian 710119, Peoples R China
推荐引用方式 GB/T 7714	Yuan, Yuan,Zheng, Xiangtao,Lu, Xiaoqiang. Spectral-Spatial Kernel Regularized for Hyperspectral Image Denoising[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2015,53(7):3815-3832.
APA	Yuan, Yuan,Zheng, Xiangtao,&Lu, Xiaoqiang.(2015).Spectral-Spatial Kernel Regularized for Hyperspectral Image Denoising.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,53(7),3815-3832.
MLA	Yuan, Yuan,et al."Spectral-Spatial Kernel Regularized for Hyperspectral Image Denoising".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 53.7(2015):3815-3832.

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