Multiscale Dual-Domain Guidance Network for Pan-Sharpening

Abstract

The goal of pan-sharpening is to produce a high-spatial-resolution multi-spectral (HRMS) image from a low-spatial-resolution multi-spectral (LRMS) counterpart by super-resolving the LRMS one under the guidance of a texture-rich panchromatic (PAN) image. Existing research has concentrated on using spatial information to generate HRMS images, but has neglected to investigate the frequency domain, which severely restricts the performance improvement. In this work, we propose a novel pan-sharpening approach, named Multi-Scale Dual-Domain Guidance Network (MSDDN) by fully exploring and exploiting the distinguished information in both the spatial and frequency domains. Specifically, the network is inborn with multi-scale U-shape manner and composed by two core parts: a spatial guidance sub-network for fusing local spatial information and a frequency guidance sub-network for fusing global frequency domain information and encouraging dual-domain complementary learning. In this way, the model can capture multi-scale dual-domain information to help it generate high-quality pan-sharpening results. Employing the proposed model on different datasets, the quantitative and qualitative results demonstrate that our method performs appreciatively against other state-of-the-art approaches and comprises a strong generalization ability for real-world scenes. The source code is available at https://github.com/alexhe101/MSDDN.