A Dual-Branch Detail Extraction Network for Hyperspectral Pansharpening
Jiahui Qu, Shaoxiong Hou, Wenqian Dong, Song Xiao, Qian Du, Yunsong Li
Abstract
Hyperspectral (HS) pansharpening aims at creating a high-resolution hyperspectral (HR-HS) image by integrating a high spatial resolution panchromatic (HR-PAN) image with a low-resolution hyperspectral (LR-HS) image. It is an important preprocessing procedure in many remote sensing tasks. Most of the existing pansharpening methods train a specific convolutional neural network (CNN) model for each type of dataset with the same number of spectral bands. The main contribution of this study is to propose a new dual-branch detail extraction pansharpening network (called DBDENet) that can sharpen HS images with any number of spectral bands using a single pre-trained model by fine-tuning the parameters of a small module in the network. Specifically, DBDENet extracts spatial details from LR-HS and HR-PAN images by two bidirectional branches of the dual-branch detail extraction network level by level. For each level, the spatial details captured from the HR-PAN and those of the LR-HS images are fused by a spatial cross attention fusion module (SCAFM). The spatial details fused by the last SCAFM module are injected into the upsampled HS image to obtain an HR-HS image. Experimental results prove to show the proposed DBDENet is superior to other widely accepted state-of-the-art methods in terms of objective indicators and visual appearance.