期刊
2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019)
卷 -, 期 -, 页码 1963-1966出版社
IEEE
DOI: 10.1109/igarss.2019.8899047
关键词
Hyperspectral imaging; blind compressive sensing; random projections; low-rank tensor recovery; spatial-spectral total variation
资金
- National Natural Science Fund of China for International Cooperation and Exchanges [61520106001]
- Science and Technology Talents Program of Hunan Association for Science and Technology [2017TJ-Q09]
- Fund of Hunan Province for the Science and Technology Plan Project [2017RS3024]
- Science and Technology Plan Project Fund of Hunan Province [CX2018B171]
- Science and Technology Innovation Platform and Talents Program of Hunan Province [2018TP1013]
Hyperspectral compressive sensing (HCS) is considered for reconstructing the hyperspectral image (HSI) from a few random sampled measurements. HCS is crucial for the onboard imaging systems to cut down the acquisition time and data storage volume, and simultaneously maintain image quality. In this paper, a spatial-spectral total variation (SSTV) regularized low-rank tensor decomposition (LRTD) method is proposed for HCS. Specifically, for the HSI, the tensor nuclear norm based LRTD is utilized to characterize the global correlation among all bands, and an anisotropic SSTV regularization is explored to describe the local spatial smooth structure and spectral correlation of adjacent bands. In addition, an efficient algorithm based on the alternative direction multiplier method is developed to solve the resulting optimization problem. Experimental results demonstrate that the proposed method is superior to the existing state-of-the-art ones.
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