Multi-feature fusion: Graph neural network and CNN combining for hyperspectral image classification

Due to its impressive representation power, the graph convolutional network (GCN) has attracted increasing attention in the hyperspectral image (HSI) classification. However, the most of available GCN-based methods for HSI classification utilize superpixels as graph nodes, which ignore the pixel-wise spectral-spatial features. To overcome the issues, we propose a novel multi-feature fusion network (MFGCN), where two different convolutional networks, i.e., multi-scale GCN and multi-scale convolutional neural network (CNN), are utilized in two branches, separately. The multi-scale superpixel-based GCN can reduce the computing power requirements, deal with the problem of labeled deficiency, and refine the multi-scale spatial features from HSI. The multi-scale CNN can extract the multi-scale pixel-wise local features for HSI classification. Furthermore, we introduced a 1D CNN to extract the spectral features for superpixels (nodes), which is different from most existing methods. Finally, a concatenate operation is employed to fuse the complementary multi-scale features. In comparison with the state-of-the-art models on three datasets, the proposed method achieves superior experimental results and outperforms competitive methods.