Masking Graph Cross-Convolution Network for Multispectral Point Cloud Classification

Achieving accurate 3-D environment perception is a key task in the field of remote sensing. Multispectral point cloud has rich integrated 3-D spatial–spectral information, which provides a data basis for realizing more detailed scene understanding and perception. However, the diversity of land covers and the complexity of its features pose challenges to classification. In addition, the current methods mechanically pool and fuse local features to obtain global information, which has limited the utility for multispectral point cloud classification. In this article, we propose a masking graph cross-convolution network (MGC2N), which aims to address these problems by utilizing spectral features to construct point-to-point relationships independent of spatial distance. A self-attention masking (SAM) module and a spatial–spectral cross-convolution (S2C2) module are innovatively designed into the proposed MGC2N. The former is used to adaptively adjust the nodes and edges of the adjacency matrix to dynamically extract effective features for different land covers; the latter is used to extract spatial distribution features and local spectral features of the land covers in the scene to enhance the discriminative ability of the learned features. Our method achieves the best-in-class performance on two real multispectral point cloud datasets, demonstrating its effectiveness in improving classification accuracy and robustness.