Spectral-Spatial Fusion Hypergraph Convolutional Network for Multispectral Point Cloud Classification

Graph Neural Networks (GNNs) based on simple graph can directly model 3D multispectral point clouds, thus avoiding the problem of 3D spatial information loss when converting 3D point clouds to 2D feature images. However, considering the dense distribution of objects in remote sensing scenes, GNNs only consider the association between pairs of vertices, which is insufficient for exploring high-order correlations and long-term dependence correlations. Hypergraph neural network (HGNN) is a more generalized framework that overcomes the limitations of traditional simple graph. In this paper, we propose a spectral-spatial fusion hypergraph convolutional network (S2FHGCN) for 3D multispectral point cloud classification. S2FHGCN first generates two hypergraphs from multidimensional features including spectral feature and spatial feature, respectively. The hypergraph structure can better handle complex high-order correlations for representation learning. Then the two hypergraphs are fused to obtain global representation information. Finally, the multidimensional features and the fused hypergraph are input to the hypergraph convolution network. The experimental results on real multispectral point clouds demonstrate the effectiveness of our proposed S2FHGCN for multispectral point cloud classification.