[1] Yang B S, Liang F X, Huang R G. Progress, challenges and perspectives of 3D LiDAR point cloud processing[J]. Acta Geodaetica et Cartographica Sinica, 46, 1509-1516(2017).
[2] Zhang J X, Lin X G, Ning X G. SVM-based classification of segmented airborne LiDAR point clouds in urban areas[J]. Remote Sensing, 5, 3749-3775(2013).
[3] Wei Y, Yao W, Wu J et al. Adaboost-based feature relevance assessment in fusing lidar and image data for classification of trees and vehicles in urban scenes[J]. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, I-7, 323-328(2012).
[4] Gevaert C M, Persello C, Sliuzas R et al. Classification of informal settlements through the integration of 2d and 3d features extracted from UAV data[J]. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 3, 317-324(2016).
[5] Wu Z R, Song S R, Khosla A et al. 3D ShapeNets: a deep representation for volumetric shapes[C], 1912-1920(2015).
[6] Maturana D, Scherer S. VoxNet: a 3D Convolutional Neural Network for real-time object recognition[C], 922-928(2015).
[7] Qi C R, Su H, Nießner M et al. Volumetric and multi-view CNNs for object classification on 3D data[C], 5648-5656(2016).
[8] Zhao Z Y, Cheng Y L, Shi X S et al. Terrain classification of LiDAR point cloud based on multi-scale features and PointNet[J]. Laser & Optoelectronics Progress, 56, 052804(2019).
[9] Qi C R, Yi L, Su H et al. PointNet++: deep hierarchical feature learning on point sets in a metric space[C], 5105-5114(2017).
[10] Li Y Y, Bu R, Sun M C et al. PointCNN: convolution on X-transformed points[C], 820-830(2018).
[11] Yang X W, Wang A B, Han X et al. Point cloud semantic segmentation based on KNN-PointNet[J]. Laser & Optoelectronics Progress, 58, 2410013(2021).
[12] He M Y, Cheng Y L, Liao X J et al. Building extraction algorithm by fusing spectral and geometrical features[J]. Laser & Optoelectronics Progress, 55, 042803(2018).
[13] Weinmann M, Jutzi B, Hinz S et al. Semantic point cloud interpretation based on optimal neighborhoods, relevant features and efficient classifiers[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 105, 286-304(2015).
[14] Shang W L, Sohn K, Almeida D et al. Understanding and improving convolutional neural networks via concatenated rectified linear units[EB/OL]. https://arxiv.org/abs/1603.05201
[15] Niemeyer J, Rottensteiner F, Soergel U. Contextual classification of lidar data and building object detection in urban areas[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 87, 152-165(2014).
[16] Xu S D, Vosselman G, Oude Elberink S. Detection and classification of changes in buildings from airborne laser scanning data[J]. Remote Sensing, 7, 17051-17076(2015).
[17] Wang H T, Lei X D, Zhao Z Z. 3D deep learning classification method for airborne LiDAR point clouds fusing spectral information[J]. Laser & Optoelectronics Progress, 57, 122802(2020).
[18] 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, 32, 960-979(2018).
[19] Yang J, Gao W, Duan X X et al. Extraction of building information based on object-oriented feature automatic selection[J]. Remote Sensing Information, 36, 130-135(2021).
[20] Shen H, Meng Q H, Liu Y B. Facial expression recognition by merging multilayer features of lightweight convolutional networks[J]. Laser & Optoelectronics Progress, 58, 0610005(2021).