A Dual-UNet With Multistage Details Injection for Hyperspectral Image Fusion
Jiajun Xiao, Jie Li, Qiangqiang Yuan, Liangpei Zhang
Abstract
Enhancement of hyperspectral image (HSI) resolution is significant for better application in practice. In this article, a dual U-Net (D-UNet) is proposed to improve the spatial resolution of HSI. The whole network contains two parts. One is the detail extraction network, whose network architecture is encoder–decoder and mainly extracts various spatial features from multispectral images (MSIs). Another is the spatio-spectral fusion network (SSFN), which aims at injecting the features from the detail extraction network into HSI for better reconstruction. Furthermore, in the primary stage of the whole network, a novel multiscale spatio-spectral attention module (MSSAM) is utilized to pay more attention to important features at different scales. Considering the complex ground scenes, the features of different scale and depth are continually extracted and fused in the whole network. The experimental results show that the proposed method is more effective compared with the state-of-the-art methods.