A Two-Stage Pansharpening Method for the Fusion of Remote-Sensing Images

Abstract

The pansharpening (PS) of remote-sensing images aims to fuse a high-resolution panchromatic image with several low-resolution multispectral images for obtaining a high-resolution multispectral image. In this work, a two-stage PS model is proposed by integrating the ideas of component replacement and the variational method. The global sparse gradient of the panchromatic image is extracted by variational method, and the weight function is constructed by combining the gradient of multispectral image in which the global sparse gradient can provide more robust gradient information. Furthermore, we refine the results in order to reduce spatial and spectral distortions. Experimental results show that our method had high generalization ability for QuickBird, Gaofen-1, and WorldView-4 satellite data. Experimental results evaluated by seven metrics demonstrate that the proposed two-stage method enhanced spatial details subjective visual effects better than other state-of-the-art methods do. At the same time, in the process of quantitative evaluation, the method in this paper had high improvement compared with that other methods, and some of them can reach a maximal improvement of 60%.