Graph Convolutional Network with Local Topology and Spectral Feature Representation for Multispectral Point Cloud Classification

Abstract

Multispectral LiDAR contributes to the rapid acquisition of 3D spatial and spectral information of land covers, providing more comprehensive features for classification. Despite the impressive performance of existing Graph Neural Networks (GNNs) in point cloud classification, extracting local features with discriminative ability remains challenging in multispectral LiDAR scenes due to the uneven distribution of geometric and spectral information. To enhance the local representation of spectral features, we propose a novel Graph Convolutional Network with Local Topology and Spectral Feature Representation (GCN-LTSFR). The network constructs optimal local topological graphs of corresponding scales based on the feature distribution density of the point cloud to enhance local spectral features. Experimental results demonstrate that the proposed GCN-LTSFR outperforms several state-of-the-art methods on a real multispectral point cloud.