IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0232778.html
   My bibliography  Save this article

Method for extraction of airborne LiDAR point cloud buildings based on segmentation

Author

Listed:
  • Maohua Liu
  • Yue Shao
  • Ruren Li
  • Yan Wang
  • Xiubo Sun
  • Jingkuan Wang
  • Yingchun You

Abstract

The LiDAR technology is a means of urban 3D modeling in recent years, and the extraction of buildings is a key step in urban 3D modeling. In view of the complexity of most airborne LiDAR building point cloud extraction algorithms that need to combine multiple feature parameters, this study proposes a building point cloud extraction method based on the combination of the Point Cloud Library (PCL) region growth segmentation and the histogram. The filtered LiDAR point cloud is segmented by using the PCL region growth method, and then the local normal vector and direction cosine are calculated for each cluster after segmentation. Finally, the histogram is generated to effectively separate the building point cloud from the non-building.Two sets of airborne LiDAR data in the south and west parts of Tokushima, Japan, are used to test the feasibility of the proposed method. The results are compared with those of the commercial software TerraSolid and the K-means algorithm. Results show that the proposed extraction algorithm has lower type I and II errors and better extraction effect than that of the TerraSolid and the K-means algorithm.

Suggested Citation

  • Maohua Liu & Yue Shao & Ruren Li & Yan Wang & Xiubo Sun & Jingkuan Wang & Yingchun You, 2020. "Method for extraction of airborne LiDAR point cloud buildings based on segmentation," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-11, May.
    • Handle: RePEc:plo:pone00:0232778
    DOI: 10.1371/journal.pone.0232778
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0232778
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0232778&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0232778?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Naimin Xu & Guoxiang Sun & Yuhao Bai & Xinzhu Zhou & Jiaqi Cai & Yinfeng Huang, 2023. "Global Reconstruction Method of Maize Population at Seedling Stage Based on Kinect Sensor," Agriculture, MDPI, vol. 13(2), pages 1-15, January.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0232778. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.