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Proceedings Paper

Research on compressive fusion for remote sensing images
Author(s): Senlin Yang; Guobin Wan; Yuanyuan Li; Xiaoxia Zhao; Xin Chong
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Paper Abstract

A compressive fusion of remote sensing images is presented based on the block compressed sensing (BCS) and non-subsampled contourlet transform (NSCT). Since the BCS requires small memory space and enables fast computation, firstly, the images with large amounts of data can be compressively sampled into block images with structured random matrix. Further, the compressive measurements are decomposed with NSCT and their coefficients are fused by a rule of linear weighting. And finally, the fused image is reconstructed by the gradient projection sparse reconstruction algorithm, together with consideration of blocking artifacts. The field test of remote sensing images fusion shows the validity of the proposed method.

Paper Details

Date Published: 21 February 2014
PDF: 7 pages
Proc. SPIE 9142, Selected Papers from Conferences of the Photoelectronic Technology Committee of the Chinese Society of Astronautics: Optical Imaging, Remote Sensing, and Laser-Matter Interaction 2013, 91421S (21 February 2014); doi: 10.1117/12.2055309
Show Author Affiliations
Senlin Yang, Xi'an Univ. of Arts and Science (China)
Guobin Wan, Northwestern Polytechnical Univ. (China)
Yuanyuan Li, Xi'an Univ. of Arts and Science (China)
Xiaoxia Zhao, Xi'an Univ. of Arts and Science (China)
Xin Chong, Emerson Network Power Ltd. (China)


Published in SPIE Proceedings Vol. 9142:
Selected Papers from Conferences of the Photoelectronic Technology Committee of the Chinese Society of Astronautics: Optical Imaging, Remote Sensing, and Laser-Matter Interaction 2013
Jorge Ojeda-Castaneda; Shensheng Han; Ping Jia; Jiancheng Fang; Dianyuan Fan; Liejia Qian; Yuqiu Gu; Xueqing Yan, Editor(s)

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