APPLIED LASER, Volume. 43, Issue 6, 132(2023)

Airborne LiDAR Point Cloud Classification Based on Dynamic Graph Convolutionwith Enhanced Feature Fusion

Yu Jin1, Liu Zhihui1, Fang Congqi1, and Lai Zulong2
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
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    References(12)

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    [15] [15] ZHAO R B, PANG M Y, WANG J D. Classifying airborne LiDAR point clouds via deep features learned by a multi-scale convolutional neural network[J]. International Journal of Geographical Information Science, 2018, 32(5): 960-979.

    [16] [16] TE G S, HU W, ZHENG A M, et al. RGCNN: Regularized graph CNN for point cloud segmentation[C]//Proceedings of the 26th ACM international conference on Multimedia. Seoul, Republic of Korea. New York: ACM, 2018: 746-754.

    [17] [17] QI C R, SU H, MO K C, et al. PointNet: deep learning on point sets for 3D classification and segmentation[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, HI, USA: IEEE, 2017: 77-85.

    [18] [18] QI C R, YI L, SU H, et al. PointNet++: Deep hierarchical feature learning on point sets in a metric space[C]// Advances in Neural Information Processing Systems(NIPS 2017). New York: Curran Associates, Inc, 2017: 5105-5114.

    [21] [21] CHEN Y, LIU G L, XU Y M, et al. PointNet++ network architecture with individual point level and global features on centroid for ALS point cloud classification[J]. Remote Sensing, 2021, 13(3): 472.

    [22] [22] WANG Y, SUN Y B, LIU Z W, et al. Dynamic graph CNN for learning on point clouds[J]. ACM Transactions on Graphics, 2019, 38(5): 1-12.

    [23] [23] SHAPOVALOV R, VELIZHEY A, BARINOVA O. Non-associative markov networks for 3d point cloud classification[J]. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2010, 38(A): 103-108.

    [24] [24] WU W X, QI Z A, LI F X. PointConv: deep convolutional networks on 3D point clouds[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Long Beach, CA, USA: IEEE, 2020: 9613-9622.

    [25] [25] WEN C C, YANG L N, LI X, et al. Directionally constrained fully convolutional neural network for airborne LiDAR point cloud classification[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 162: 50-62.

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    Yu Jin, Liu Zhihui, Fang Congqi, Lai Zulong. Airborne LiDAR Point Cloud Classification Based on Dynamic Graph Convolutionwith Enhanced Feature Fusion[J]. APPLIED LASER, 2023, 43(6): 132

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    Paper Information

    Received: Mar. 29, 2022

    Accepted: --

    Published Online: Feb. 2, 2024

    The Author Email:

    DOI:10.14128/j.cnki.al.20234306.132

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