PDAF: Prompt-Driven Dynamic Adaptive Fusion Network for Pansharpening Remote Sensing Images

Abstract

The goal of pansharpening is to fuse a high spatial resolution panchromatic (PAN) image with a lower spatial resolution multispectral (MS) image to produce a high-resolution multispectral image. Most deep learning-based methods consider only local or global features, and focusing solely on one type of feature may limit the network's representational capacity. In addition, the fusion process often overlooks the heterogeneous and complementary information unique to PAN and MS images. Therefore, we propose a prompt-driven dynamic adaptive fusion network. To better combine the advantages of local and global features, we introduce a local and global adaptive modulation module. We also innovatively propose a prompt-driven dynamic fusion module that effectively integrates unique heterogeneous information while reconstructing corresponding complementary information. Finally, an expert mixing mechanism is employed to enhance the fused features, achieving superior fusion results. Our proposed method outperforms recent pansharpening methods, as demonstrated by reduced-resolution experiments and full-resolution validation.

Keywords

• Deep learning (DL)
• dynamic fusion
• pansharpening