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  • Received: Sep. 26, 2019

    Accepted: Oct. 29, 2019

    Posted: Jun. 1, 2020

    Published Online: Jun. 3, 2020

    The Author Email: Lei Xiangda (211804010013@home.hpu.edu.cn)

    DOI: 10.3788/LOP57.122802

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    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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Laser & Optoelectronics Progress, Vol. 57, Issue 12, 122802 (2020)

3D Deep Learning Classification Method for Airborne LiDAR Point Clouds Fusing Spectral Information

Wang Hongtao, Lei Xiangda*, and Zhao Zongze

Author Affiliations

  • School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo, Henan 454000, China

Abstract

Aim

ing at the problem that the traditional 2D deep learning method can not realize the 3D point cloud classification, this study proposes a novel classification method for airborne LiDAR point clouds based on 3D deep learning. First, airborne LiDAR point clouds and multi-spectral imagery are fused to expand the spectral information of point clouds. Then, 3D point clouds are placed on grids to make the LiDAR data suitable for the 3D deep learning. Subsequently, the local and global features in different scales are extracted by multi-layer perceptron. Finally, airborne LiDAR point clouds are classified into semantic objects using the 3D deep learning algorithm. The data sets provided by the International Society of Photogrammetry and Remote Sensing (ISPRS) are used to validate the proposed method, and the experimental results show that the classification accuracy can be increased by 13.39% by fusing the LiDAR point clouds and multi-spectral images. Compared with some of the methods submitted to ISPRS, the proposed method achieves better performance by simplifying the process of feature extraction.

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