• Laser & Optoelectronics Progress
  • Vol. 57, Issue 13, 130104 (2020)
Fan Zhang1、*, Huashan Li2, and Tao Jiang1、**
Author Affiliations
  • 1School of Surveying and Mapping Science and Engineering, Shandong University of Science and Technology, Qingdao, Shandong 266590, China
  • 2CCCC Southern China Surveying & Mapping Technology Co., Ltd., Guangzhou, Guangdong 510221, China;
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    DOI: 10.3788/LOP57.130104 Cite this Article Set citation alerts
    Fan Zhang, Huashan Li, Tao Jiang. Digital Elevation Model Generation in LiDAR Point Cloud Based on Cloth Simulation Algorithm[J]. Laser & Optoelectronics Progress, 2020, 57(13): 130104 Copy Citation Text show less
    References

    [1] Sun T, Li D J, Zhu S H et al. LiDAR point cloud data filtering based on improved window size[J]. Jiangxi Science, 36, 150-155(2018).

    [2] Pingel T J, Clarke K C. McBride W A. An improved simple morphological filter for the terrain classification of airborne LiDAR data[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 77, 21-30(2013).

    [3] Yin F, Qi H, Xue X B et al. An adaptive TIN processing densification filtering algorithm[J]. Railway Investigation and Surveying, 36, 41-44(2010).

    [4] Jin S H, Yang H H, Wang L Y. Research on slope filtering of point cloud data based on gridding LiDAR[J]. Geomatics & Spatial Information Technology, 36, 154-156(2013).

    [5] Yang Y, Zhang Y S, Zou X L et al. An improved method of slope based filtering of airborne LiDAR point cloud[J]. Science of Surveying and Mapping, 33, 12-13, 280(2008).

    [6] Zhang K Q, Chen S C, Whitman D et al. A progressive morphological filter for removing nonground measurements from airborne LiDAR data[J]. IEEE Transactions on Geoscience and Remote Sensing, 41, 872-882(2003).

    [7] Axelsson P. DEM generation from laser scanner data using adaptive TIN models[J]. International Archives of Photogrammetry & Remote Sensing, 33, 110-117(2000).

    [8] Chen C F, Li Y Y, Li W et al. A multiresolution hierarchical classification algorithm for filtering airborne LiDAR data[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 82, 1-9(2013).

    [9] Fan J Q[J]. Technical research on generating high precision digital elevation moodel (DEM) based on airborne LiDAR point cloud New Exploration, 2016, 53-58.

    [10] Zhang Q Y. Extracting DEM from LiDAR data in forestry area[J]. Geotechnical Investigation & Surveying, 43, 78-82(2015).

    [11] Zhang H W, Zhang B M, Guo H T et al. A DEM generation method based on point cloud data[J]. Geomatics & Spatial Information Technology, 38, 4-6, 9(2015).

    [12] Shamos M I, Hoey D. Closest-point problems. [C]∥16th Annual Symposium on Foundations of Computer Science (sfcs 1975), October 13-15, 1975, Washington DC, USA. New York: IEEE, 151-162(1975).

    [13] Jiang H F. Study on divide-and-conquer algorithm of generating Delaunay triangulation[J]. Computer Engineering and Applications, 39, 81-82, 117(2003).

    [14] Tian W W. A study on data filtering and DEM extraction of LiDAR point cloud[D]. Changchun: Changchun University of Science and Technology(2018).

    [15] Zhang W M, Qi J B, Wan P et al. An easy-to-use airborne LiDAR data filtering method based on cloth simulation[J]. Remote Sensing, 8, 501(2016).

    Fan Zhang, Huashan Li, Tao Jiang. Digital Elevation Model Generation in LiDAR Point Cloud Based on Cloth Simulation Algorithm[J]. Laser & Optoelectronics Progress, 2020, 57(13): 130104
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