ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Download
Publications Copernicus
Download
Citation
Articles | Volume V-2-2021
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2021, 43–50, 2021
https://doi.org/10.5194/isprs-annals-V-2-2021-43-2021
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2021, 43–50, 2021
https://doi.org/10.5194/isprs-annals-V-2-2021-43-2021

  17 Jun 2021

17 Jun 2021

WEAKLY SUPERVISED PSEUDO-LABEL ASSISTED LEARNING FOR ALS POINT CLOUD SEMANTIC SEGMENTATION

P. Wang and W. Yao P. Wang and W. Yao
  • Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hung Hom, Hong Kong

Keywords: Semantic segmentation, Pseudo labels, Weakly supervised learning, Airborne Laser Scanning, Point clouds

Abstract. Competitive point cloud semantic segmentation results usually rely on a large amount of labeled data. However, data annotation is a time-consuming and labor-intensive task, particularly for three-dimensional point cloud data. Thus, obtaining accurate results with limited ground truth as training data is considerably important. As a simple and effective method, pseudo labels can use information from unlabeled data for training neural networks. In this study, we propose a pseudo-label-assisted point cloud segmentation method with very few sparsely sampled labels that are normally randomly selected for each class. An adaptive thresholding strategy was proposed to generate a pseudo-label based on the prediction probability. Pseudo-label learning is an iterative process, and pseudo labels were updated solely on ground-truth weak labels as the model converged to improve the training efficiency. Experiments using the ISPRS 3D sematic labeling benchmark dataset indicated that our proposed method achieved an equally competitive result compared to that using a full supervision scheme with only up to 2‰ of labeled points from the original training set, with an overall accuracy of 83.7% and an average F1 score of 70.2%.