[1] [1] WU Z R, FAN L M, WU X Q, et al. Fast detection method for tree barrier of transmission line using airborne laser point cloud［J］. Applied Laser, 2022, 42(3): 128-134.

[10] [10] KIM H B, SOHN G. Point-based classification of power line corridor scene using random forests［J］. Photogrammetric Engineering & Remote Sensing, 2013, 79(9): 821-833.

[11] [11] KUPROWSKI M, DROZDA P. Feature selection for airbone LiDAR point cloud classification［J］. Remote Sensing, 2023, 15(3): 561.

[12] [12] LIU Y X, ALEKSANDROV M, ZLATANOVA S, et al. Classification of power facility point clouds from unmanned aerial vehicles based on adaboost and topological constraints［J］. Sensors, 2019, 19(21): 4717.

[13] [13] KUMAR V, NANDY A, SONI V, et al. Powerline extraction from aerial and mobile LiDAR data using deep learning［J］. Earth Science Informatics, 2024, 17(4): 2819-2833.

[17] [17] QI C R, YI L, SU H, et al. PointNet++［C］//Proceedings of the 31st International Conference on Neural Information Processing Systems. Long Beach, California, USA： ACM, 2017: 5105-5114.

[18] [18] SOROKIN M I, ZHDANOV D D, ZHDANOV A D. Conversion of point cloud data to 3D models using PointNet++ and transformer［J］. Programming and Computer Software, 2024, 50(3): 249-256.

[19] [19] CHEN L, CHEN W Y, XU Z W, et al. DAPnet: A double self-attention convolutional network for point cloud semantic labeling［J］. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14: 9680-9691.