APPLIED LASER, Volume. 43, Issue 6, 132(2023)
Airborne LiDAR Point Cloud Classification Based on Dynamic Graph Convolutionwith Enhanced Feature Fusion
[2] [2] JAKOVLJEVIC G, GOVEDARICA M, ALVAREZ-TABOADA F, et al. Accuracy assessment of deep learning based classification of LiDAR and UAV points clouds for DTM creation and flood risk mapping[J]. Geosciences, 2019, 9(7): 323.
[13] [13] HU X Y, YUAN Y. Deep-learning-based classification for DTM extraction from ALS point cloud[J]. Remote Sensing, 2016, 8(9): 730.
[14] [14] RIZALDY A, PERSELLO C, GEVAERT C, et al. Ground and multi-class classification of airborne laser scanner point clouds using fully convolutional networks[J]. Remote Sensing, 2018, 10(11): 1723.
[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
Received: Mar. 29, 2022
Accepted: --
Published Online: Feb. 2, 2024
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