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Article

Global and Local Tensor Sparse Approximation Models for Hyperspectral Image Destriping

1
School of Automation, Northwestern Polytechnical University, Shanxi 710072, China
2
Ministry of Basic Education, Sichuan Engineering Technical College, Deyang 618000, China
3
Research & Development Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen 518057, China
4
Department of Electronics and Informatics, Vrije Universiteit Brussel, 1050 Brussel, Belgium
5
Department of Computer Engineering, Sejong University, Seoul 05006, Korea
*
Author to whom correspondence should be addressed.
Received: 14 January 2020 / Revised: 11 February 2020 / Accepted: 17 February 2020 / Published: 20 February 2020
(This article belongs to the Special Issue Advanced Techniques for Spaceborne Hyperspectral Remote Sensing)
This paper presents a global and local tensor sparse approximation (GLTSA) model for removing the stripes in hyperspectral images (HSIs). HSIs can easily be degraded by unwanted stripes. Two intrinsic characteristics of the stripes are (1) global sparse distribution and (2) local smoothness along the stripe direction. Stripe-free hyperspectral images are smooth in spatial domain, with strong spectral correlation. Existing destriping approaches often do not fully investigate such intrinsic characteristics of the stripes in spatial and spectral domains simultaneously. Those methods may generate new artifacts in extreme areas, causing spectral distortion. The proposed GLTSA model applies two 0 -norm regularizers to the stripe components and along-stripe gradient to improve the destriping performance. Two 1 -norm regularizers are applied to the gradients of clean image in spatial and spectral domains. The double non-convex functions in GLTSA are converted to single non-convex function by mathematical program with equilibrium constraints (MPEC). Experiment results demonstrate that GLTSA is effective and outperforms existing competitive matrix-based and tensor-based destriping methods in visual, as well as quantitative, evaluation measures. View Full-Text
Keywords: hyperspectral image destriping; non-convex optimization; mean cross-track profile; PADMM hyperspectral image destriping; non-convex optimization; mean cross-track profile; PADMM
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MDPI and ACS Style

Kong, X.; Zhao, Y.; Xue, J.; Chan, J.C.-W.; Kong, S.G. Global and Local Tensor Sparse Approximation Models for Hyperspectral Image Destriping. Remote Sens. 2020, 12, 704. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12040704

AMA Style

Kong X, Zhao Y, Xue J, Chan JC-W, Kong SG. Global and Local Tensor Sparse Approximation Models for Hyperspectral Image Destriping. Remote Sensing. 2020; 12(4):704. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12040704

Chicago/Turabian Style

Kong, Xiangyang, Yongqiang Zhao, Jize Xue, Jonathan C.-W. Chan, and Seong G. Kong 2020. "Global and Local Tensor Sparse Approximation Models for Hyperspectral Image Destriping" Remote Sensing 12, no. 4: 704. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12040704

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