期刊
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
卷 53, 期 11, 页码 5943-5957出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2015.2429146
关键词
Compressive hyperspectral imaging (CHI); joint spatial-spectral; multidimensional multiplexing (MDMP); nonlinear compressed sensing (NCS); sparse tensor
类别
资金
- National Basic Research Program of China (973 Program) [2013CB329402]
- National Natural Science Foundation of China [91438103, 91438201, 61072108, 61173090]
- Fundamental Research Funds for the Central Universities [BDY021429]
- Huawei Innovation Research Program
- Kunshan Innovation Institute of Xidian University
- Foreign Scholars in University Research and Teaching Programs [B07048]
- Project of Development Plan of Science and Technology in Shaanxi Province [2013KJXX-63]
Recently, compressive hyperspectral imaging (CHI) has received increasing interests, which can recover a large range of scenes with a small number of sensors via compressed sensing (CS) theory. However, most of the available CHI methods separate and vectorize hyperspectral cubes into spatial and spectral vectors, which will result in heavy computational and storage burden in the recovery. Moreover, the complexity of real scene makes the sparsifying difficult and thus requires more measurements to achieve accurate recovery. In this paper, these two issues are addressed, and a new CHI approach via sparse tensors and nonlinear CS (NCS) is advanced for accurate maintenance of image structure with limited number of sensors. Based on a multidimensional multiplexing (MDMP) CS scheme, the observed measurements are denoted as tensors and a nonlinear sparse tensor coding is adopted, to develop a new tensor-NCS (T-NCS) algorithm for noniterative recovery of hyperspectral images. Moreover, two recovery schemes are advanced for T-NCS, including example-aided and self-learning CHI approaches. Finally, some experiments are performed on three real hyperspectral data sets to investigate the performance of T-NCS, and the results demonstrate its efficiency and superiority to the counterparts.
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