Main > Laser & Optoelectronics Progress >  Volume 57 >  Issue 20 >  Page 201005 > Article
  • Abstract
  • Abstract
  • View Summary
  • Figures (5)
  • Tables (0)
  • Equations (0)
  • References (14)
  • Get PDF(in Chinese)
  • Paper Information
  • Received: Jan. 19, 2020

    Accepted: Feb. 24, 2020

    Posted: Oct. 1, 2020

    Published Online: Oct. 17, 2020

    The Author Email: Wang Guo (

    DOI: 10.3788/LOP57.201005

  • Get Citation
  • Copy Citation Text

    Guo Wang, Qiang Wang, Zhenxin Zhang, Bang Xu, Guangxing Zhao. Classification of Airborne LiDAR Vegetation Piont Clouds Assisted by Aerial Images[J]. Laser & Optoelectronics Progress, 2020, 57(20): 201005

    Download Citation

  • Category
  • Image Processing
  • Share
Laser & Optoelectronics Progress, Vol. 57, Issue 20, 201005 (2020)

Classification of Airborne LiDAR Vegetation Piont Clouds Assisted by Aerial Images

Wang Guo1,*, Wang Qiang2, Zhang Zhenxin3, Xu Bang1, and Zhao Guangxing1

Author Affiliations

  • 1Institute of Civil Engineering, Henan University of Engineering, Zhengzhou, Henan 451191, China
  • 2Tianjin Geospatial Information Technology Engineering Center, Tianjin Normal University, Tianjin 300387, China
  • 3College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China


Since it is difficult to automatically distinguish between vegetation and buildings from non-ground point cloud data, this research work proposes a method to automatically classify vegetation in airborne LiDAR (Light Detection and Ranging) point clouds, which is assisted by aerial image. Based on the fact that the spectral characteristics of vegetation are clearly different from other ground objects, digital orthophoto generation and K-means clustering algorithm are employed to cluster and enhance the images. Then, the enhanced image and the point cloud data of the corresponding area are fused. Finally, the airborne LiDAR vegetation point cloud data is classified using the image processing results. Experiments are carried out on airborne LiDAR vegetation point cloud data and aerial images of a particular city. Quantitative analysis results prove that total classification accuracy of the proposed method is 96.47%, and the Kappa coefficient is 0.9248. The introduced method can pave the way for automatic classification of the vegetation in LiDAR point clouds.


Please Enter Your Email: