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Nonlocal tensor sparse representation and low-rank regularization for hyperspectral image compressive sensing reconstruction

(2019) REMOTE SENSING. 11(2).
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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 l1 -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.

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MLA
Xue, Jize, et al. “Nonlocal Tensor Sparse Representation and Low-Rank Regularization for Hyperspectral Image Compressive Sensing Reconstruction.” REMOTE SENSING, vol. 11, no. 2, 2019.
APA
Xue, J., Zhao, Y., Liao, W., & Chan, J. (2019). Nonlocal tensor sparse representation and low-rank regularization for hyperspectral image compressive sensing reconstruction. REMOTE SENSING, 11(2).
Chicago author-date
Xue, Jize, Yongqiang Zhao, Wenzhi Liao, and Jonathan Chan. 2019. “Nonlocal Tensor Sparse Representation and Low-Rank Regularization for Hyperspectral Image Compressive Sensing Reconstruction.” REMOTE SENSING 11 (2).
Chicago author-date (all authors)
Xue, Jize, Yongqiang Zhao, Wenzhi Liao, and Jonathan Chan. 2019. “Nonlocal Tensor Sparse Representation and Low-Rank Regularization for Hyperspectral Image Compressive Sensing Reconstruction.” REMOTE SENSING 11 (2).
Vancouver
1.
Xue J, Zhao Y, Liao W, Chan J. Nonlocal tensor sparse representation and low-rank regularization for hyperspectral image compressive sensing reconstruction. REMOTE SENSING. 2019;11(2).
IEEE
[1]
J. Xue, Y. Zhao, W. Liao, and J. Chan, “Nonlocal tensor sparse representation and low-rank regularization for hyperspectral image compressive sensing reconstruction,” REMOTE SENSING, vol. 11, no. 2, 2019.
@article{8604464,
  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 l1 -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.}},
  articleno    = {{193}},
  author       = {{Xue, Jize and Zhao, Yongqiang and Liao, Wenzhi and Chan, Jonathan}},
  issn         = {{2072-4292}},
  journal      = {{REMOTE SENSING}},
  language     = {{eng}},
  number       = {{2}},
  pages        = {{24}},
  title        = {{Nonlocal tensor sparse representation and low-rank regularization for hyperspectral image compressive sensing reconstruction}},
  url          = {{http://dx.doi.org/10.3390/rs11020193}},
  volume       = {{11}},
  year         = {{2019}},
}

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