GOGCN: an attentional network with geometry and orientation-awareness for airborne LiDAR point cloud classification-Reference-Cited by-同舟云学术

GOGCN: an attentional network with geometry and orientation-awareness for airborne LiDAR point cloud classification

Published:2024-04-23 Issue: Volume: Page:1-8
ISSN:0039-6265
Container-title:Survey Review
language:en
Short-container-title:Survey Review

Author:

Chen Yang¹,Li Jianzhou²,Xing Yin³,Li Xiao¹,Luo Lili¹

Affiliation:

1. School of Information Technology, Suzhou Institute of Trade & Commerce, Suzhou 215009, People’s Republic of China

2. Changjiang River Scientific Research Institute, Wuhan 430010, People’s Republic of China

3. School of Geography Science and Geomatics Engineering, Suzhou University of Science and Technology, Suzhou 215009, People’s Republic of China

Funder

Open Foundation of Basic Scientific Research Operating Expenses of Central-Level Public Academies and Institutes

Young Teachers Enterprise Practice in Higher Vocational College on Jiangsu Province

Natural Science Foundation of the Jiangsu Higher Education Institutions of China

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/00396265.2024.2341502

Reference31 articles.

1. DGCNN network architecture with densely connected point pairs in multiscale local regions for ALS point cloud classification;Chen Y.;IEEE Geoscience and remote sensing letters,2022

2. Segmentation-based hierarchical interpolation filter using both geometric and radiometric features for LiDAR point clouds over complex scenarios

3. Hu Q. et al. 2019. RandLA-Net: efficient semantic segmentation of large-scale point clouds. arXiv:1911.11236.

4. GraNet: Global relation-aware attentional network for semantic segmentation of ALS point clouds

5. Jiang M. et al. 2018. PointSIFT: a SIFT-like network module for 3D point cloud semantic segmentation. In Computer vision and pattern recognition computer science – computer vision and pattern recognition. arXiv preprint arXiv:1807.00652.