激光与光电子学进展, 2020, 57 (12): 122802, 网络出版: 2020-06-03   

融合光谱信息的机载LiDAR点云三维深度学习分类方法 下载: 1397次

3D Deep Learning Classification Method for Airborne LiDAR Point Clouds Fusing Spectral Information
作者单位
河南理工大学测绘与国土信息工程学院, 河南 焦作 454000
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王宏涛, 雷相达, 赵宗泽. 融合光谱信息的机载LiDAR点云三维深度学习分类方法[J]. 激光与光电子学进展, 2020, 57(12): 122802.

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.

参考文献

[1] 李德仁, 邵振峰, 杨小敏. 从数字城市到智慧城市的理论与实践[J]. 地理空间信息, 2011, 9(6): 1-5.

    Li D R, Shao Z F, Yang X M. Theory and practice from digital city to smart city[J]. Geospatial Information, 2011, 9(6): 1-5.

[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, 2013, 5(8): 3749-3775.

[3] 何曼芸, 程英蕾, 廖湘江, 等. 融合光谱特征和几何特征的建筑物提取算法[J]. 激光与光电子学进展, 2018, 55(4): 042803.

    He M Y, Cheng Y L, Liao X J, et al. Building extraction algorithm by fusing spectral and geometrical features[J]. Laser & Optoelectronics Progress, 2018, 55(4): 042803.

[4] NiemeyerJ, RottensteinerF, Soergel U. Conditional random fields for lidar point cloud classification in complex urban areas[J]. ISPRS Annals of Photogrammetry, RemoteSensing and Spatial InformationSciences, 2012, I-3: 263- 268.

[5] 魏书法. 基于三维点云与二维图像的场景理解[D]. 北京: 中国科学院大学, 2016.

    Wei SF. Scene understanding based on point clouds and 2D images[D]. Beijing: University of Chinese Academy of Sciences, 2016.

[6] YiL, SuH, Guo XW, 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, 2017: 17355428.

[7] BaiS, BaiX, Zhou ZC, 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, 2016: 5023- 5032.

[8] MaturanaD, SchererS. 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, 2015: 922- 928.

[9] Wang L, Huang Y C, Shan J, et al. MSNet: multi-scale convolutional network for point cloud classification[J]. Remote Sensing, 2018, 10(4): 612.

[10] Charles RQ, HaoS, Mo KC, 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, 2017: 17355473.

[11] 张佳颖, 赵晓丽, 陈正. 基于深度学习的点云语义分割综述[J]. 激光与光电子学进展, 2020, 57(4): 040001.

    Zhang J Y, Zhao X L, Chen Z. A review of semantic segmentation of point cloud based on deep learning[J]. Laser & Optoelectronics Progress, 2020, 57(4): 040001.

[12] 赵中阳, 程英蕾, 释小松, 等. 基于多尺度特征和PointNet的LiDAR点云地物分类方法[J]. 激光与光电子学进展, 2019, 56(5): 052804.

    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, 2019, 56(5): 052804.

[13] 张剑清, 潘励, 王树根. 数字摄影测量[M]. 2版. 武汉: 武汉大学出版社, 2009: 26- 30.

    Zhang JQ, PanL, Wang SG. Digital photogrammetry [M]. 2nd ed. Wuhan: Wuhan University Press, 2009: 26- 30.

[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, 2018, 143: 191-204.

[15] Shang WL, SohnK, AlmeidaD, et al. Understanding and improving convolutional neural networks via concatenated rectified linear units[EB/OL]. [2019-09-01].https: ∥arxiv.org/abs/1603. 05201.

[16] Kingma DP, BaJ. Adam: a method for stochastic optimization[EB/OL]. [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, 2014, 87: 152-165.

[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, 2016, 37(17): 4172-4200.

[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, 2018, 32(5): 960-979.

王宏涛, 雷相达, 赵宗泽. 融合光谱信息的机载LiDAR点云三维深度学习分类方法[J]. 激光与光电子学进展, 2020, 57(12): 122802. 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.

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