[1] Wu J, Liu R, Guo N et al. Aerial LiDAR data classification using weighted support vector machines[J]. Geomatics and Information Science of Wuhan University, 38, 1-6(2013).

[2] Axelsson P. Processing of laser scanner data—algorithms and applications[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 54, 138-147(1999).

[3] Sithole G, Vosselman G. Experimental comparison of filter algorithms for bare-Earth extraction from airborne laser scanning point clouds[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 59, 85-101(2004).

[4] Kilian J, Haala N, Englich M. Capture and evaluation of airborne laser scanner data[J]. International Archives of Photogrammetry and Remote Sensing, 31, 383-388(1996).

[5] Axelsson P. DEM generation from laser scanner data using adaptive TIN models[J]. International Archives of Photogrammetry and Remote Sensing, 33, 110-117(2000).

[6] Huang Z W, Liu F, Hu G W. Improved method for LiDAR point cloud data filtering based on hierarchical pseudo-grid[J]. Acta Optica Sinica, 37, 0828004(2017).

[7] Chen X Y, Yun T, Xue L F et al. Classification of tree species using LiDAR point cloud data[J]. Laser & Optoelectronics Progress, 56, 122801(2019).

[8] Anguelov D, Taskar B, Chatalbashev V et al. Discriminative learning of Markov random fields for segmentation of 3D scan data. [C]∥2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), June 20-25, 2005, San Diego, CA, USA. New York: IEEE, 8624033(2005).

[9] Xiao Y, Hu S X, Xiao S et al. A fast statistical method of tree information from 3D laser point clouds[J]. Chinese Journal of Lasers, 45, 0510007(2018).

[10] Lodha S K, Kreps E J, Helmbold D P et al. Aerial LiDAR data classification using support vector machines (SVM). [C]∥Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06), June 14-16, 2006, Chapel Hill, NC, USA. New York: IEEE, 10285115(2006).

[11] He M Y, Cheng Y L, Nie Y Z et al. A building extraction method on comprehensive delaunay TIN models and region growing[J]. Journal of Air Force Engineering University(Natural Science Edition), 18, 54-59(2017).

[12] Zhao X Q, Guo Q H, Su Y J et al. Improved progressive TIN densification filtering algorithm for airborne LiDAR data in forested areas[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 117, 79-91(2016).

[13] Liu Y, Dong L, Gao G F. The machine carries the ground of laser radar data thing classification method and treatment[J]. Geomatics & Spatial Information Technology, 38, 108-110(2015).

[14] Boser B E, Guyon I M, Vapnik V N. A training algorithm for optimal margin classifiers. [C]∥Proceedings of the Fifth Annual Workshop on Computational Learning Theory - COLT '92, July 27-29, 1992, Pittsburgh, Pennsylvania, USA. New York: ACM, 144-152(1992).

[15] Li H L, Wang C H, Yuan B Z. An improved SVM: NN-SVM[J]. Chinese Journal of Computers, 26, 1015-1020(2003).

[16] Wang X, Luo Y P, Jiang T et al. A new classification method for LIDAR data based on unbalanced support vector machine. [C]∥2011 International Symposium on Image and Data Fusion, August 9-11, 2011, Tengchong, Yunnan, China. New York: IEEE, 12318534(2011).

[17] Fan S J, Zhang A W, Hu S X et al. A method of classification for airborne full waveform LiDAR data based on random forest[J]. Chinese Journal of Lasers, 40, 0914001(2013).

[18] Abdelhamid D, Chaouki B M, Abdelmalik T A. A new approach to multi-class SVM learning based on OC-SVM for huge databases[M]. ∥Abd Manaf A, Zeki A, Zamani M, et al. Informatics engineering and information science. Communications in computer and information science. Berlin, Heidelberg: Springer, 252, 677-690(2011).

[19] Bigdeli B, Samadzadegan F. Classification of LiDAR data based on multi-class SVM. [C]∥The 2010 Canadian Geomatics Conference and Symposium of Commission I, ISPRS Convergence in Geomatics - Shaping Canada's Competitive Landscape, June 15-18, 2010, Calgary, Alberta, Canada. USA: NASA(2010).

[20] Lague D, Brodu N, Leroux J. Accurate 3D comparison of complex topography with terrestrial laser scanner: application to the Rangitikei canyon (N-Z)[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 82, 10-26(2013).

[21] Yue C, Liu C J, Wang X F. Classification algorithm for laser point clouds of high-steep slopes based on multi-scale dimensionality features and SVM[J]. Geomatics and Information Science of Wuhan University, 41, 882-888(2016).

[22] Xie J Y. SVM based feature selection algorithms for classification Xi'an:[D]. Xidian University, 15-31(2012).

[23] Hsu C W, Chang C C, Lin C J[S.l.: s.n.]. A practical guide to support vector classification [S.l.: s.n.], 1-28(2003).