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Remote Sens. 2019, 11(2), 193; https://doi.org/10.3390/rs11020193

Nonlocal Tensor Sparse Representation and Low-Rank Regularization for Hyperspectral Image Compressive Sensing Reconstruction

1
School of Automation, Northwestern Polytechnical University, Xi’an 710072, China
2
Research & Development Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen 518057, China
3
Department of Telecommunications and Information Processing, Ghent University-TELIN-IMEC, 9000 Ghent, Belgium
4
Department of Electronics and Informatics, Vrije, Universiteit Brussel, 1050 Brussel, Belgium
*
Author to whom correspondence should be addressed.
Received: 7 December 2018 / Revised: 13 January 2019 / Accepted: 17 January 2019 / Published: 19 January 2019
(This article belongs to the Collection Learning to Understand Remote Sensing Images)
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Abstract

Hyperspectral image compressive sensing reconstruction (HSI-CSR) is an important issue in remote sensing, and has recently been investigated increasingly by the sparsity prior based approaches. However, most of the available HSI-CSR methods consider the sparsity prior in spatial and spectral vector domains via vectorizing hyperspectral cubes along a certain dimension. Besides, in most previous works, little attention has been paid to exploiting the underlying nonlocal structure in spatial domain of the HSI. In this paper, we propose a nonlocal tensor sparse and low-rank regularization (NTSRLR) approach, which can encode essential structured sparsity of an HSI and explore its advantages for HSI-CSR task. Specifically, we study how to utilize reasonably the l 1 -based sparsity of core tensor and tensor nuclear norm function as tensor sparse and low-rank regularization, respectively, to describe the nonlocal spatial-spectral correlation hidden in an HSI. To study the minimization problem of the proposed algorithm, we design a fast implementation strategy based on the alternative direction multiplier method (ADMM) technique. Experimental results on various HSI datasets verify that the proposed HSI-CSR algorithm can significantly outperform existing state-of-the-art CSR techniques for HSI recovery. View Full-Text
Keywords: hyperspectral image; compressive sensing; structured sparsity; tensor sparse decomposition; tensor low-rank approximation hyperspectral image; compressive sensing; structured sparsity; tensor sparse decomposition; tensor low-rank approximation
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Xue, J.; Zhao, Y.; Liao, W.; Chan, J. .-W. Nonlocal Tensor Sparse Representation and Low-Rank Regularization for Hyperspectral Image Compressive Sensing Reconstruction. Remote Sens. 2019, 11, 193.

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