Pan-sharpening with a spatial-enhanced variational model

Abstract

Pan-sharpening is a procedure that fuses a low-resolution multispectral (MS) image and a high-resolution panchromatic (PAN) image into a high-resolution MS image. Existing variational pan-sharpening methods typically consider gradient as the descriptor of the spatial information and enforce this to be consistent between the PAN image and fused image because of their shared scenes. However, the gradient feature is not sufficient to exploit the abundant spatial details. Second-order derivative, which is balanced with the first-order derivative, is introduced to build a variational model such that additional geographic information is extracted from the PAN image. Then corresponding numerical schemes to determine the minimal solution of this model are provided. Through experiments on data from four different satellites, the proposed method is proven to enhance the spatial quality of the fused result and demonstrate stable performance in preserving the spectral information.