Chinese Journal of Lasers, Volume. 44, Issue 10, 1010007(2017)

Joint Classification Method for Terrestrial LiDAR Point Cloud Based on Intensity and Color Information

Cheng Xiaojun1、*, Guo Wang1, Li Quan1, and Cheng Xiaolong2
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
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    CLP Journals

    [1] Wang Guo, Xiaojun Cheng. Registration Method for Airborne and Terrestrial Light Detection and Ranging Point Cloud Based on Laser Intensity Classification[J]. Laser & Optoelectronics Progress, 2018, 55(6): 062803

    [2] Chu Jinghui, Wu Zerui, Lü Wei, Li Zhe. Breast Cancer Diagnosis System Based on Transfer Learning and Deep Convolutional Neural Networks[J]. Laser & Optoelectronics Progress, 2018, 55(8): 81001

    [3] Shanxin Zhang, Qiang Fan, Zhiping Zhou. Object Shape Classification Based on Bayesian Optimized Neural Network[J]. Laser & Optoelectronics Progress, 2018, 55(6): 061011

    [4] Zhenyang Hui, Penggen Cheng, Yunlan Guan, Yunju Nie. Review on Airborne LiDAR Point Cloud Filtering[J]. Laser & Optoelectronics Progress, 2018, 55(6): 060001

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    Cheng Xiaojun, Guo Wang, Li Quan, Cheng Xiaolong. Joint Classification Method for Terrestrial LiDAR Point Cloud Based on Intensity and Color Information[J]. Chinese Journal of Lasers, 2017, 44(10): 1010007

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    Paper Information

    Category: Remote Sensing and Sensors

    Received: Apr. 25, 2017

    Accepted: --

    Published Online: Oct. 18, 2017

    The Author Email: Xiaojun Cheng (cxj@tongji.edu.cn)

    DOI:10.3788/CJL201744.1010007

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