Attention-Based Multistage Fusion Network for Remote Sensing Image Pansharpening

Abstract

Pansharpening is a significant branch in the field of remote sensing image processing, the goal of which is to fuse panchromatic (PAN) and multispectral (MS) images through certain rules to generate high-resolution MS (HRMS) images. Therefore, how to improve the spatial and spectral resolutions of the fused image is the problem that we need to solve urgently. In this article, a multistage remote sensing image fusion network (MRFNet) is proposed on the basis of in-depth research and exploration on the fusion of the PAN and MS images to obtain a clear fused image that can reflect the ground features more comprehensively and completely. The proposed network consists of three stages that are connected by cross-stage fusion. The first two stages are used to extract the features of the PAN and MS images. The structure of the encoder–decoder and the channel attention module are used to extract the features of the remote sensing image in the channel domain. The third stage is the image reconstruction stage fusing the extracted features with the original image to improve the spatial and spectral resolutions of the fused result. A series of experiments are conducted on the benchmark datasets WorldView II, GF-2, and QuickBird. Qualitative analysis and quantitative comparison show the superiority of MRFNet in visual effects and the values of evaluation indicators.