Remote Sensing Images Fusion based on Block Compressed Sensing

A novel strategy for remote sensing images fusion is presented based on the block compressed sensing (BCS). Firstly, the multiwavelet transform (MWT) are employed for better sparse representation of remote sensing images. The sparse representations of block images are then compressive sampling by the BCS with an identical scrambled block hadamard operator. Further, the measurements are fused by a linear weighting rule in the compressive domain. And finally, the fused image is reconstructed by the gradient projection sparse reconstruction (GPSR) algorithm. Experiments result analyzes the selection of block dimension and sampling rating, as well as the convergence performance of the proposed method. The field test of remote sensing images fusion shows the validity of the proposed method.