Compressive sensing (CS) theory is a new type of sampling theory based on information technology.It breaks through the limitations of traditional Nyquist/Shannon sampling theorem, and reconstructs a signalor digital image at a far lower sampling rate. In this paper, we present an efficient remote sensing fusionmethod based on compressive sensing. First, a sparse model according to the wavelet-based algorithm is usedon the panchromatic image and the multispectral image separately. Then the sparse results are compressedthrough a measurement matrix and different fusion coefficients are chosen on each component of thecompressed images. Finally, after reconstruction and invert wavelet transform, we acquire the final fusionimage. Compared experiments are made and the simulation results show that the CS fusion algorithm has amore economic and effective performance than the other traditional methods.
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