DPAFNet: A Multistage Dense-Parallel Attention Fusion Network for Pansharpening

Abstract

Pansharpening is the technology to fuse a low spatial resolution MS image with its associated high spatial full resolution PAN image. However, primary methods have the insufficiency of the feature expression and do not explore both the intrinsic features of the images and correlation between images, which may lead to limited integration of valuable information in the pansharpening results. To this end, we propose a novel multistage Dense-Parallel attention fusion network (DPAFNet). The proposed parallel attention residual dense block (PARDB) module can focus on the intrinsic features of MS images and PAN images while exploring the correlation between the source images. To fuse more complementary information as much as possible, the features extracted from each PARDB are fused at multistage levels, which allows the network to better focus on and exploit different information. Additionally, we propose a new loss, where it calculates the L2-norm between the pansharpening results and PAN images to constrain the spatial structures. Experiments were conducted on simulated and real datasets and the evaluation results verified the superiority of the DPAFNet.

Keywords

• convolutional neural network (CNN)
• parallel attention guided fusion
• multispectral (MS) pansharpening
• multistage fusion