FusGAT: Graph Attention-Based Fusion Network for Unsupervised Hyperspectral Image Super-Resolution

Jin Xu, Yuanchao Su, Xu Sun, Jiaxin Li, Haiwei Li, Jianjian Gao, Xiaohua Feng, Ronghua Liu

Abstract: Unsupervised hyperspectral image super-resolution (HSI-SR) has recently emerged as a popular and active research topic in remote sensing data fusion. However, most methods neglect the nonlocal features of the data in representation learning, which limits their fusion performances. To overcome the issue, we propose a graph attention-based fusion network (FusGAT) in this letter. This approach first extracts local features from the input data using multiscale convolutions, and then, the graph attention mechanism is employed to model relationships between nodes in the spectral stream for deriving nonlocal features of the image and transferring them to the spatial stream. FusGAT will iteratively update the node connections and refine node embedding, facilitating the extraction of nonlocal features and enabling effective information flow between the streams. We conducted several experiments on two datasets to prove the effectiveness of the proposed method. The source code will be available at: https://github.com/yuanchaosu/FusGAT-GRSL

External IDs:dblp:journals/lgrs/XuSSLLGFL25