HAVANA: Hard Negative Sample-Aware Self-Supervised Contrastive Learning for Airborne Laser Scanning Point Cloud Semantic Segmentation

Deep Neural Network (DNN)-based point cloud semantic segmentation has presented significant breakthrough using large-scale labeled aerial laser point cloud datasets. However, annotating such large-scaled point clouds is time-consuming. Self-Supervised Learning (SSL) is a promising approach to this p...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2024-01, Vol.16 (3), p.485
Main Authors: Zhang, Yunsheng, Yao, Jianguo, Zhang, Ruixiang, Wang, Xuying, Chen, Siyang, Fu, Han
Format: Article
Language:English
Subjects:
Airborne lasers
Algorithms
ALS point cloud
Artificial neural networks
Classification
Cluster analysis
Clustering
Data mining
Datasets
Deep learning
end-to-end
Forecasts and trends
Image segmentation
Labels
Laser applications
Lasers
Learning
Machine learning
Neural networks
Self-supervised learning
self-supervision
Semantic segmentation
Semantics
Vector quantization
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Summary:Deep Neural Network (DNN)-based point cloud semantic segmentation has presented significant breakthrough using large-scale labeled aerial laser point cloud datasets. However, annotating such large-scaled point clouds is time-consuming. Self-Supervised Learning (SSL) is a promising approach to this problem by pre-training a DNN model utilizing unlabeled samples followed by a fine-tuned downstream task involving very limited labels. The traditional contrastive learning for point clouds selects the hardest negative samples by solely relying on the distance between the embedded features derived from the learning process, potentially evolving some negative samples from the same classes to reduce the contrastive learning effectiveness. This work proposes a hard-negative sample-aware self-supervised contrastive learning algorithm to pre-train the model for semantic segmentation. We designed a k-means clustering-based Absolute Positive And Negative samples (AbsPAN) strategy to filter the possible false-negative samples. Experiments on two typical ALS benchmark datasets demonstrate that the proposed method is more appealing than supervised training schemes without pre-training. Especially when the labels are severely inadequate (10% of the ISPRS training set), the results obtained by the proposed HAVANA method still exceed 94% of the supervised paradigm performance with full training set.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs16030485