期刊
REMOTE SENSING
卷 11, 期 2, 页码 -出版社
MDPI
DOI: 10.3390/rs11020193
关键词
hyperspectral image; compressive sensing; structured sparsity; tensor sparse decomposition; tensor low-rank approximation
类别
资金
- National Natural Science Foundation of China [61371152, 61771391]
- Shenzhen Municipal Science and Technology Innovation Committee [JCYJ20170815162956949]
- Fund for Scientific Research in Flanders (FWO) [G037115N]
- Research Foundation Flanders (FWO-Vlaanderen)
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.
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