21 February 2014 Research on compressive fusion for remote sensing images
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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.
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Senlin Yang, Senlin Yang, Guobin Wan, Guobin Wan, Yuanyuan Li, Yuanyuan Li, Xiaoxia Zhao, Xiaoxia Zhao, Xin Chong, Xin Chong, "Research on compressive fusion for remote sensing images", 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; https://doi.org/10.1117/12.2055309
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