caa a22 4500 606209794 CHVBK 20210128101030.0 cr unu---uuuuu 210128e20151201xx s 000 0 eng 10.1007/s11220-015-0115-2 doi (NATIONALLICENCE)springer-10.1007/s11220-015-0115-2 Distributed Compressive Sensing of Hyperspectral Images Using Low Rank and Structure Similarity Property [Elektronische Daten] [Bingchao Huang, Ke Xu, Jianwei Wan, Xu Liu] An efficient method and system for distributed compressive sensing of hyperspectral images is presented, which exploit the low rank and structure similarity property of hyperspectral imagery. In this paper, by integrating the respective characteristics of DSC and CS, a distributed compressive sensing framework is proposed to simultaneously capture and compress hyperspectral images. At the encoder, every band image is measured independently, where almost all computation burdens can be shifted to the decoder, resulting in a very low-complexity encoder. It is simple to operate and easy to hardware implementation. At the decoder, each band image is reconstructed by the method of total variation norm minimize. During each band reconstruction, the low rand structure of band images and spectrum structure similarity are used to give birth to the new regularizers. With combining the new regularizers and other regularizer, we can sufficiently exploit the spatial correlation, spectral correlation and spectral structural redundancy in hyperspectral imagery. A numerical optimization algorithm is also proposed to solve the reconstruction model by augmented Lagrangian multiplier method. Experimental results show that this method can effectively improve the reconstruction quality of hyperspectral images. Springer Science+Business Media New York, 2015 Hyperspectral image nationallicence Distributed compressive sensing nationallicence Low rank nationallicence Structure similarity property nationallicence Huang Bingchao College of Electronic Science and Engineering, National University of Defense Technology, 410073, Changsha, Hunan, People's Republic of China aut Xu Ke College of Electronic Science and Engineering, National University of Defense Technology, 410073, Changsha, Hunan, People's Republic of China aut Wan Jianwei College of Electronic Science and Engineering, National University of Defense Technology, 410073, Changsha, Hunan, People's Republic of China aut Liu Xu College of Electronic Science and Engineering, National University of Defense Technology, 410073, Changsha, Hunan, People's Republic of China aut Sensing and Imaging Springer US; http://www.springer-ny.com 16/1(2015-12-01), 1-8 1557-2064 16:1<1 2015 16 11220 https://doi.org/10.1007/s11220-015-0115-2 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s11220-015-0115-2 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Huang Bingchao College of Electronic Science and Engineering, National University of Defense Technology, 410073, Changsha, Hunan, People's Republic of China aut NATIONALLICENCE 700 1- Xu Ke College of Electronic Science and Engineering, National University of Defense Technology, 410073, Changsha, Hunan, People's Republic of China aut NATIONALLICENCE 700 1- Wan Jianwei College of Electronic Science and Engineering, National University of Defense Technology, 410073, Changsha, Hunan, People's Republic of China aut NATIONALLICENCE 700 1- Liu Xu College of Electronic Science and Engineering, National University of Defense Technology, 410073, Changsha, Hunan, People's Republic of China aut NATIONALLICENCE 773 0- Sensing and Imaging Springer US; http://www.springer-ny.com 16/1(2015-12-01), 1-8 1557-2064 16:1<1 2015 16 11220