1. Research on compressive fusion for remote sensing images
- Author
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Xiaoxia Zhao, Yuanyuan Li, Sen-lin Yang, Guo-bin Wan, and Xin Chong
- Subjects
Image fusion ,Computer science ,business.industry ,Computation ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Reconstruction algorithm ,Pattern recognition ,Contourlet ,Weighting ,Compressed sensing ,Computer Science::Computer Vision and Pattern Recognition ,Computer data storage ,Computer vision ,Artificial intelligence ,business ,Block (data storage) ,Remote sensing - 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.
- Published
- 2014