Laser & Optoelectronics Progress, Volume. 57, Issue 12, 122802(2020)
3D Deep Learning Classification Method for Airborne LiDAR Point Clouds Fusing Spectral Information
[1] Li D R, Shao Z F, Yang X M. Theory and practice from digital city to smart city[J]. Geospatial Information, 9, 1-5(2011).
[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).
[4] Niemeyer J, Rottensteiner F, Remote Sensing, Spatial Information Sciences. I-, 3, 263-268(2012).
[5] Wei S F. Scene understanding based on point clouds and 2D images[D]. Beijing: University of Chinese Academy of Sciences(2016).
[6] Yi L, Su H, Guo X W et al. SyncSpecCNN: synchronized spectral CNN for 3D shape segmentation. [C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI. New York: IEEE, 17355428(2017).
[7] Bai S, Bai X, Zhou Z C et al. GIFT: a real-time and scalable 3D shape search engine. [C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 5023-5032(2016).
[8] Maturana D, Scherer S. VoxNet: a 3D convolutional neural network for real-time object recognition. [C]∥2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), September 28-October 2, 2015, Hamburg, Germany. New York: IEEE, 922-928(2015).
[9] Wang L, Huang Y C, Shan J et al. MSNet: multi-scale convolutional network for point cloud classification[J]. Remote Sensing, 10, 612(2018).
[10] Charles R Q, Hao S, Mo K C et al. PointNet: deep learning on point sets for 3D classification and segmentation. [C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI. New York:IEEE, 17355473(2017).
[13] Zhang J Q, Pan L, Wang S G[M]. Digital photogrammetry, 26-30(2009).
[14] Yousefhussien M, Kelbe D J, Ientilucci E J et al. A multi-scale fully convolutional network for semantic labeling of 3D point clouds[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 143, 191-204(2018).
[15] Shang W L, Sohn K, Almeida D et al[2019-09-01]. Understanding and improving convolutional neural networks via concatenated rectified linear units [2019-09-01].https: ∥arxiv., org/abs/1603, 05201.
[16] Kingma D P, Ba J[2019-09-10]. Adam: a method for stochastic optimization [2019-09-10].https: ∥arxiv., org/abs/1412, 6980.
[17] 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).
[18] Ramiya A M, Nidamanuri R R, Ramakrishnan K. A supervoxel-based spectro-spatial approach for 3D urban point cloud labelling[J]. International Journal of Remote Sensing, 37, 4172-4200(2016).
[19] 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).
Get Citation
Copy Citation Text
Hongtao Wang, Xiangda Lei, Zongze Zhao. 3D Deep Learning Classification Method for Airborne LiDAR Point Clouds Fusing Spectral Information[J]. Laser & Optoelectronics Progress, 2020, 57(12): 122802
Category: Remote Sensing and Sensors
Received: Sep. 26, 2019
Accepted: Oct. 29, 2019
Published Online: Jun. 3, 2020
The Author Email: Lei Xiangda (211804010013@home.hpu.edu.cn)