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Achieving high-precision automatic classification in real-world applications for airborne laser scanning point clouds is a challenging task duo to their unstructured nature, uneven density distribution, high redundancy, incompleteness and scene complexity. Graph Convolutional Neural Networks can process scattered point clouds directly without regularization, which avoids the loss of depth informationand has recently become a topic of increased interest. Therefore, this study proposed an extension of the Graph-Unet network architecture named DGCN-ED for airborne LiDAR point classification, which uses a Graph Convolutional Neural Network as a representation to describe complex object relationships and an encoder-decoder architecture to capture the multi-scaled point features and describe objects in the high-level feature space. The two-layer dynamic update Graph Convolutional Neural Network is designed to expand the effective range of nodes and enhance the representation ability of learned pointwise features. The effectiveness of the proposed method is evaluated by an experiment on the ISPRS Vaihingen 3D semantic labelling benchmark dataset. Moreover, experiments on the IEEE 2019 Data Fusion Contest Dataset were conducted to demonstrate the generalization abilities of the proposed method. The results show that our method achieved on average 2.8% higher overall accuracy than existing methods, with an overall accuracy of 98.0% and an average F1 score of 0.797.

DGCN-ED: dynamic graph convolutional networks with encoder–decoder structure and its application for airborne LiDAR point classification