SSGAM-Net: A Hybrid Semi-Supervised and Supervised Network for Robust Semantic Segmentation Based on Drone LiDAR Data

Abstract

The semantic segmentation of drone LiDAR data is important in intelligent industrial operation and maintenance. However, current methods are not effective in directly processing airborne true-color point clouds that contain geometric and color noise. To overcome this challenge, we propose a novel hybrid learning framework, named SSGAM-Net, which combines supervised and semi-supervised modules for segmenting objects from airborne noisy point clouds. To the best of our knowledge, we are the first to build a true-color industrial point cloud dataset, which is obtained by drones and covers 90,000 m2. Secondly, we propose a plug-and-play module, named the Global Adjacency Matrix (GAM), which utilizes only few labeled data to generate the pseudo-labels and guide the network to learn spatial relationships between objects in semi-supervised settings. Finally, we build our point cloud semantic segmentation network, SSGAM-Net, which combines a semi-supervised GAM module and a supervised Encoder–Decoder module. To evaluate the performance of our proposed method, we conduct experiments to compare our SSGAM-Net with existing advanced methods on our expert-labeled dataset. The experimental results show that our SSGAM-Net outperforms the current advanced methods, reaching 85.3% in mIoU, which ranges from 4.2 to 58.0% higher than other methods, achieving a competitive level.