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Remote Sensing
Volume 12
Issue 14
10.3390/rs12142264
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Article

Hyperspectral Image Recovery Using Non-Convex Low-Rank Tensor Approximation

by
Hongyi Liu
1,
Hanyang Li
1,
Zebin Wu
2,* and
Zhihui Wei
2
1
School of Science, Nanjing University of Science and Technology, Nanjing 210094, China
2
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(14), 2264; https://doi.org/10.3390/rs12142264
Submission received: 1 June 2020 / Revised: 10 July 2020 / Accepted: 13 July 2020 / Published: 15 July 2020
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Abstract

Low-rank tensors have received more attention in hyperspectral image (HSI) recovery. Minimizing the tensor nuclear norm, as a low-rank approximation method, often leads to modeling bias. To achieve an unbiased approximation and improve the robustness, this paper develops a non-convex relaxation approach for low-rank tensor approximation. Firstly, a non-convex approximation of tensor nuclear norm (NCTNN) is introduced to the low-rank tensor completion. Secondly, a non-convex tensor robust principal component analysis (NCTRPCA) method is proposed, which aims at exactly recovering a low-rank tensor corrupted by mixed-noise. The two proposed models are solved efficiently by the alternating direction method of multipliers (ADMM). Three HSI datasets are employed to exhibit the superiority of the proposed model over the low rank penalization method in terms of accuracy and robustness.
Keywords: hyperspectral image (HSI); non-convex relaxation; tensor completion; tensor robust principal analysis hyperspectral image (HSI); non-convex relaxation; tensor completion; tensor robust principal analysis
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MDPI and ACS Style

Liu, H.; Li, H.; Wu, Z.; Wei, Z. Hyperspectral Image Recovery Using Non-Convex Low-Rank Tensor Approximation. Remote Sens. 2020, 12, 2264. https://doi.org/10.3390/rs12142264

AMA Style

Liu H, Li H, Wu Z, Wei Z. Hyperspectral Image Recovery Using Non-Convex Low-Rank Tensor Approximation. Remote Sensing. 2020; 12(14):2264. https://doi.org/10.3390/rs12142264

Chicago/Turabian Style

Liu, Hongyi, Hanyang Li, Zebin Wu, and Zhihui Wei. 2020. "Hyperspectral Image Recovery Using Non-Convex Low-Rank Tensor Approximation" Remote Sensing 12, no. 14: 2264. https://doi.org/10.3390/rs12142264

APA Style

Liu, H., Li, H., Wu, Z., & Wei, Z. (2020). Hyperspectral Image Recovery Using Non-Convex Low-Rank Tensor Approximation. Remote Sensing, 12(14), 2264. https://doi.org/10.3390/rs12142264

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