科研之友
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摘要
A statistical model-based pan-sharpening method under the framework of Shearlet transform is developed in this paper. The approximation coefficients of multi-spectral (MS) image are used for reconstruction level of fusion algorithm. The weighted average method is used to fuse the high-pass subbands. The weights are estimated by a statistical model in which a new objective function is proposed. It is partitioned into two parts: the first term maximizes the local variance of the high-pass subband of fused image, which means the details can be injected into MS image as much as possible. The second term is the correlation restriction between the high-pass subband of fused image and that of MS image, which is indirectly beneficial to avoid generating the unreasonable high-frequency information. Finally, the pan-sharpening result is obtained by performing the inverse Shearlet transform. A series of experiments conducted on real remote sensing images demonstrate the effectiveness of our model.
