[7] [7] SUSAKI J. Adaptive slope filtering of airborne LiDAR data in urban areas for digital terrain model (DTM) generation［J］. Remote Sensing, 2012, 4(6): 1804-1819.

[12] [12] RUSU R B, BLODOW N, BEETZ M. Fast point feature histograms (FPFH) for 3D registration［C］//2009 IEEE International Conference on Robotics and Automation. Kobe, Japan. IEEE, 2009: 3212-3217.

[14] [14] WEST K F, WEBB B N, LERSCH J R, et al. Context-driven automated target detection in 3D data［C］//Defense and Security. Proc SPIE 5426, Automatic Target Recognition XIV, Orlando, Florida, USA. 2004, 5426: 133-143.

[17] [17] KE G L, MENG Q, FINELY T, et al. Lightgbm: A highly efficient gradient boosting decision tree［C］//Proceedings of Conference on Neural Information Processing Systems. New York: ACM Press, 2017. 3149-3157.

[19] [19] FRIEDMAN J H. Greedy function approximation: A gradient boosting machine［J］. The Annals of Statistics, 2001, 29(5): 1189-1232.

[20] [20] HASTIE T, ROSSET S, ZHU J, et al. Multi-class AdaBoost［J］. Statistics and Its Interface, 2009, 2(3): 349-360.

[21] [21] CHEN T Q,GUESTRIN C.XGBoost: A scalable tree boosting system［C］//Proceedings of the 22nd ACM SIGKDD International Conference. San Francisco, USA, 2016: 785-794.

[22] [22] MUNOZ D, BAGNELL J A, VANDAPEL N, et al. Contextual classification with functional Max-Margin Markov Networks［C］//2009 IEEE Conference on Computer Vision and Pattern Recognition. Miami, FL, USA: IEEE, 2009: 975-982.

[23] [23] ROYNARD X, DESCHAUD J E, GOULETTE F. Paris-Lille-3D: A large and high-quality ground-truth urban point cloud dataset for automatic segmentation and classification［J］. The International Journal of Robotics Research, 2018, 37(6): 545-557.