A feature perturbation weakly supervised learning network for airborne multispectral LiDAR pointcloud classification

Currently, most pointcloud classification methods heavily rely on huge numbers of labeled samples. Notably, labeling a large-scale multispectral LiDAR (MS-LiDAR) pointcloud is time-consuming and costly. To address this issue, we propose a feature perturbation weakly supervised network for classifying MS-LiDAR pointclouds using a few labeled samples, termed as FPWS-Net. In the FPWS-Net, we innovatively design a dual semantic inference structure, including a primary semantic inference module and an auxiliary semantic inference module. To provide the network with rich, accurate supervised signals, we embed kernel point convolution (KPConv) into the network for modelling the contextual information of MS-LiDAR pointclouds and propagating the signals between labeled and unlabeled points. Additionally, to constrain feature perturbations resulting from the dual semantic inference structure, fully leverage unlabeled points, and fit the architecture of the FPWS-Net, we combine consistency constraint and mutual pseudo-labeling loss. The proposed FPWS-Net is tested on two datasets, and achieves at least an average F1-score of 83.69 %, an mIoU of 78.81 %, and an OA of 95.97 % using only 0.1 % labeled points. The comparative experimental results demonstrate that the FPWS-Net not only outperforms the state-of-the-art (SOTA) weakly supervised networks, but also achieves the comparable classification performance to the fully supervised methods in the airborne MS-LiDAR pointcloud classification tasks.