A new weakly supervised approach for ALS point cloud semantic segmentation

Although novel point cloud semantic segmentation schemes that continuously surpass state-of-the-art results exist, the success of learning an effective model typically relies on the availability of abundant labeled data. However, data annotation is a time-consumng and labor-intensive task, particularly for large-scale airborne laser scanning (ALS) point clouds involving multiple classes in urban areas. Therefore, simultaneously obtaining promising results while significantly reducing labeling is crucial. In this study, we propose a deep-learning-based weakly supervised framework for the semantic segmentation of ALS point clouds. This is to exploit implicit information from unlabeled data subject to incomplete and sparse labels. Entropy regularization is introduced to penalize class overlap in the predictive probability. Additionally, a consistency constraint is designed to improve the robustness of the predictions by minimizing the difference between the current and ensemble predictions. Finally, we propose an online soft pseudo-labeling strategy to create additional supervisory sources in an efficient and nonparametric manner. Extensive experimental analysis using three benchmark datasets demonstrates that our proposed method significantly boosts the classification performance without compromising the computational efficiency, considering the sparse point annotations. It outperforms the current weakly supervised methods and achieves a result comparable to that of full supervision competitors. Considering the ISPRS Vaihingen 3D data, using only 1 parts per thousand labels, our method achieved an overall accuracy of 83.0% and an average F1 score of 70.0%. These increased by 6.9% and 12.8%, respectively, compared to the model trained only using sparse label information.