Parameter selection for variational pan-sharpening by using evolutionary algorithm

Pan-sharpening is a technique that generates a high spatial resolution multi-spectral image making use of both the spectral information contained in a low spatial resolution multi-spectral image and the spatial information contained in a high spatial resolution panchromatic image. The pan-sharpening method usually contains some parameters. They are usually problem dependent and need to be set properly. In this article, we propose a variational method for pan-sharpening and use an evolutionary algorithm (EA) to choose the optimal parameters automatically. In our method, two quality measurements are combined to form an optimization objective function of the EA, and the parameters are encoded as an individual vector in the EA. The optimal parameters are generated by optimizing the objective function of the EA. The new method is compared with some other variational methods using QuickBird data. We also applied the selected parameters to different images to discuss the applicable scope. The experimental results show that our method can generate a high-quality fused image, and the same parameters' values can be used for similar images.