A hyperspectral image fusion algorithm based on Compressive Sensing

This paper introduces a self-adaptive weighted average method of image fusion for hyperspectral imagery that utilizes recently developed theory of Compressive Sensing. In the proposed algorithm, images are transformed into Fourier Domain and sampled in Double-star shaped sampling pattern. Then the sampled images are fused with the proposed fusion principle. Finally the fused images are reconstructed by Minimum Total Variation algorithm. Results are presented on real hyperspectral data collected in Shandong, China and the multispectral images obtained in London. Experimental comparison on these datasets shows the quality and efficiency of proposed algorithm and the distinct advantages of Compressive Sensing based image fusion.