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  • Master Tutor

  • Name (Pinyin):

    wangbo

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  • Date of Employment:

    2020-03-23

  • School/Department:

    遥感与测绘工程学院

  • Administrative Position:

    地理空间信息工程系系主任

  • Education Level:

    With Certificate of Graduation for Doctorate Study

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    Male

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    在岗

  • Alma Mater:

    南京师范大学 地理科学学院

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Paper Publications

Home > Scientific Research > Paper Publications

UAV-Based Terrain Modeling in Low-Vegetation Areas: A Framework Based on Multiscale Elevation Variation Coefficients

  • Time:2024-06-09
  • Hits:

  • DOI Number:

    10.3390/rs15143569

  • Journal:

    Remote Sensing

  • Key Words:

    terrain modeling; coefficients of elevation variation; virtual grid; elevation differences; DEM

  • Abstract:

    The removal of low vegetation is still challenging in UAV photogrammetry. According to the different topographic features expressed by point-cloud data at different scales, a vegetation-
    filtering method based on multiscale elevation-variation coefficients is proposed for terrain modeling. First, virtual grids are constructed at different scales, and the average elevation values of the corresponding point clouds are obtained. Second, the amount of elevation change at any two scales in each virtual grid is calculated to obtain the difference in surface characteristics (degree of elevation change) at the corresponding two scales. Third, the elevation variation coefficient of the virtual grid that corresponds to the largest elevation variation degree is calculated, and threshold segmentation is performed based on the relation that the elevation variation coefficients of vegetated regions are much larger than those of terrain regions. Finally, the optimal calculation neighborhood radius of the elevation variation coefficients is analyzed, and the optimal segmentation threshold is discussed. The experimental results show that the multiscale coefficients of elevation variation method can accurately remove vegetation points and reserve ground points in low- and densely
    vegetated areas. The type I error, type II error, and total error in the study areas range from 1.93 to 9.20%, 5.83 to 5.84%, and 2.28 to 7.68%, respectively. The total error of the proposed method is 2.43–2.54% lower than that of the CSF, TIN, and PMF algorithms in the study areas. This study provides a foundation for the rapid establishment of high-precision DEMs based on UAV photogrammetry.

  • Indexed by:

    Journal paper

  • Discipline:

    Economics

  • Document Type:

    J

  • Translation or Not:

    no

  • Included Journals:

    SCI

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