Graph Convolutional Network With Local and Global Feature Fusion for Hyperspectral Image Classification

In hyperspectral image (HSI) classification tasks, extracting surface features is crucial, but the complexity of HSI brings considerable challenges. Traditional convolutional neural networks (CNNs) and vision transformer (ViT) perform well but have large parameters and are time-consuming. In contrast, graph convolutional neural networks (GCNs) are fast to train with few parameters. The simple linear iterative clustering (SLIC) segmentation algorithm generates pixel-level graph data and superpixel-level graph data. Single-branch GCNs learn only one type of graph data resulting in insufficient feature richness. In two-branch GCNs, one branch extracts local spatial-spectral features from pixel-level data, another branch extracts global spatial-spectral features from superpixel-level data, and finally, the two branches are combined. However, the existing two-branch GCNs do not focus on the close fusion of local and global features. Therefore, in this article, we propose new two-branch GCNs with local and global feature fusion (LG-GCNs). The LG-GCN model proposed in this study not only has a short training time but also has a high accuracy. The LG-GCN can capture more comprehensively the local and global spatial-spectral semantic information of HSI. The experimental results on three publicly available HSI datasets show that LG-GCN outperforms other state-of-the-art models in terms of classification accuracy and robustness. In addition, it has a shorter training time and is more resistant to noise. The source code of the LG-GCN model is available at https://github.com/Dreamvai/LG-GCN.