期刊
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
卷 14, 期 12, 页码 2457-2461出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2017.2771212
关键词
Compressive sensing; hyperspectral images (HSIs); spatial-spectral structure; total variation (TV); Tucker decomposition
类别
资金
- National Natural Science Foundation of China [11501440, 61603292, 61673015, 61373114]
- Vice-Chancellor's Discretionary Fund of the Chinese University of Hong Kong
In this letter, we consider the problem of compressive sensing of hyperspectral images (HSIs). We propose a novel tensor-based approach by modeling the global spatial-spectral correlation and local smoothness properties hidden in HSIs. Specifically, we use the tensor Tucker decomposition to describe the global spatial-spectral correlation among all HSI bands, and a weighted 3-D total variation to characterize the local smooth structure in both spatial and spectral modes. We then design an efficient algorithm to solve the resulting optimization problem by using the alternating direction method of multipliers. Experimental results on several HSI data sets demonstrate improved reconstruction performance of the proposed approach, as compared with other competing approaches.
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