Terrain Classification of LiDAR Point Cloud Based on Multi-Scale Features and PointNet

Zhongyang Zhao; Yinglei Cheng; Xiaosong Shi; Xianxiang Qin; Xin Li

doi:10.3788/LOP56.052804

[1] Lai X D[M]. Basic principle and application of airborne laser radar(2010).

[2] Hui Z Y, Cheng P G, Guan Y L et al. Review on airborne LiDAR point cloud filtering[J]. Laser & Optoelectronics Progress, 55, 060001(2018).

[3] 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, 1-5(2013).

[4] MacFaden S W, O'neil-Dunne J P M, Royar A R et al. . High-resolution tree canopy mapping for New York City using LIDAR and object-based image analysis[J]. Journal of Applied Remote Sensing, 6, 063567(2012).

[5] 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).

[6] Cheng X J, Guo W, Li Q et al. Joint classification method for terrestrial LiDAR point cloud based on intensity and color information[J]. Chinese Journal of Lasers, 44, 1010007(2017).

[7] Guo B, Huang X F, Zhang F et al. Classification of airborne laser scanning data using JointBoost[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 100, 71-83(2015).

[8] Wu Z R, Song S R, Khosla A et al. 3D ShapeNets: a deep representation for volumetric shapes. [C]∥IEEE Conference on Computer Vision and Pattern Recognition, 1912-1920(2015).

[9] Maturana D, Scherer S. VoxNet: a 3D convolutional neural network for real-time object recognition. [C]∥IEEE/RSJ International Conference on Intelligent Robots and Systems, 922-928(2015).

[10] Qi C R, Su H, Niebner M et al. Volumetric and multi-view CNNs for object classification on 3D data. [C]∥IEEE Conference on Computer Vision and Pattern Recognition, 5648-5656(2016).

[11] Yi L, Su H, Guo X W et al. SyncSpecCNN: synchronized spectral CNN for 3D shape segmentation. [C]∥IEEE Conference on Computer Vision and Pattern Recognition, 6584-6592(2017).

[12] Charles R Q, Su H, Mo K C et al. PointNet: deep learning on point sets for 3D classification and segmentation. [C]∥IEEE Conference on Computer Vision and Pattern Recognition, 77-85(2017).

[13] Lindeberg T. Scale-space theory in computer vision[M]. Berlin: Springer, 349-382(1994).

[14] Cai J R, Kuang L Q, Han X. Multi-scale feature point extraction algorithm based on scattered point cloud[J]. Computer Engineering and Design, 37, 3255-3259(2016).

[15] 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).

[16] Zhu J F, Hu X Y, Zhang Z X et al. Hierarchical outlier detection for point cloud data using a density analysis method[J]. Acta Geodaetica et Cartographica Sinica, 44, 282-291(2015).

[17] Kingma D P, Ba J. Adam: a method for stochastic optimization. [C]∥3 ^rd International Conference for Learning Representations (2015).

[18] Shang W, Sohn K, Almeida D et al. Understanding and improving convolutional neural networks via concatenated rectified linear units. [C]∥International Conference on Machine Learning, 2217-2225(2016).

[19] Hackel T, Savinov N, Ladicky L, Remote Sensing, Spatial Information Scienceset al. Semantic3D. net: a new large-scale point cloud classification benchmark[J]. IV-, 1/W1, 91-98(2017).

[20] Rottensteiner F, Sohn G, Gerke M et al. ISPRS test project on urban classification and 3D building reconstruction[J]. Commission III-Photogrammetric Computer Vision and Image Analysis, Working Group III/4-3D Scene Analysis, 1-17(2013).

[21] Garcia-Garcia A, Orts-Escolano S, Oprea S et al. A survey on deep learning techniques for image and video semantic segmentation[J]. Applied Soft Computing, 70, 41-65(2018).

[22] Lawin F J, Danelljan M, Tosteberg P et al. Deep projective 3D semantic segmentation. [C]∥International Conference on Computer Analysis of Images and Patterns, 95-107(2017).

[23] Boulch A, Saux B L, Audebert N. Unstructured point cloud semantic labeling using deep segmentation networks. [C]∥Eurographics Workshop on 3D Object Retrieval, 17-24(2017).

[24] Tchapmi L, Choy C, Armeni I et al. SEGCloud: semantic segmentation of 3D point clouds. [C]∥International Conference on 3D Vision, 537-547(2017).

[25] Zhao Z Y, Cheng Y L, He M Y. Registration of LiDAR point cloud and aerial image based on surface feature and SIFT feature[J]. Journal of Air Force Engineering University (Natural Science Edition), 19, 65-70(2018).