SSU-Net: A Novel Spectral-Spatial Dual-Branch U-Net for Spectral Super-Resolution in Wide-Area Multispectral Remote Sensing Imagery

Due to the narrow swath width of hyperspectral remote sensing images, the limited availability of such data is insufficient to meet the demands of large-scale applications. To address this issue, spectral super-resolution leveraging the extensive coverage of existing multispectral images offers a promising solution. However, current research methods overlook the issue of heterogeneous land cover in real-world data, thereby limiting the practical applicability of deep neural networks. To overcome these challenges, we propose a Spectral-Spatial residual attention U-Net (SSU-Net) to improve spectral super-resolution performance in heterogeneous land cover scenarios. Specifically, A weight-adaptive allocation module is integrated at the model's front to address inconsistent spectral reconstruction accuracy caused by varying land cover distributions. Moreover, given the long-range spectral dependencies and local spatial correlations in hyperspectral images, our model incorporates a dual-branch design, consisting of a spectral branch and a spatial branch, to effectively extract spectral and spatial features separately. Considering the scarcity of large-scale datasets, we also constructed two real-world datasets to validate the effectiveness of the proposed network. Extensive experiments demonstrate that the proposed SSU-Net achieves state-of-the-art performance, exhibiting strong practical applicability for large-scale, real-world scenarios. Our dataset and codes will be released at https://github.com/WenjuanZhang-aircas/SSU-Net .