Weakly supervised MS-LiDAR point cloud classification based on kernel convolutional semantic query network

Xin Zheng; Gang Xu; Zhihai Shu; Yamin Ji; Xuan Chen; Changfu Xu

doi:10.1117/12.3026623

9 February 2024 Weakly supervised MS-LiDAR point cloud classification based on kernel convolutional semantic query network

Xin Zheng, Gang Xu, Zhihai Shu, Yamin Ji, Xuan Chen, Changfu Xu

Author Affiliations +

Proceedings Volume 13073, Third International Conference on High Performance Computing and Communication Engineering (HPCCE 2023); 130731G (2024) https://doi.org/10.1117/12.3026623
Event: Third International Conference on High Performance Computing and Communication Engineering (HPCCE 2023), 2023, Changsha, China

Abstract

Due to the large amount of airborne multispectral light detection and ranging (MS LiDAR) point cloud data, it is required to annotate it to complete supervised learning. However, the annotation cost of large-scale point clouds is high, which can easily lead to incomplete or inaccurate annotation, affecting the accuracy of point cloud classification. Therefore, this article proposes a new weakly supervised MS LiDAR point cloud classification method based on kernel point convolutional semantic query network. Firstly, using kernel convolutional semantic query network to detect weak targets in point clouds. On this basis, sparsify the point cloud data. Introduce weakly supervised learning methods to classify MS LiDAR point clouds. The experimental results have verified that the research method can accurately classify different types of point cloud data, and the time consumption can be controlled within 5ms. Compared with traditional methods, it has significant application advantages.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xin Zheng, Gang Xu, Zhihai Shu, Yamin Ji, Xuan Chen, and Changfu Xu "Weakly supervised MS-LiDAR point cloud classification based on kernel convolutional semantic query network", Proc. SPIE 13073, Third International Conference on High Performance Computing and Communication Engineering (HPCCE 2023), 130731G (9 February 2024); https://doi.org/10.1117/12.3026623

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KEYWORDS

Point clouds

Classification systems

LIDAR

Target detection

Semantics

Feature extraction

Statistical modeling

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