Combination of morphological and distributional filtering for UAV - LiDAR point cloud density to establish the Digital Terrain Model

  • Affiliations:

    1 Hanoi University of Mining and Geology, Ha Noi, Vietnam
    2 Northern QT Trading and Construction Joint Stock Company, Ha Noi, Vietnam

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  • Received: 24th-Apr-2022
  • Revised: 11st-Aug-2022
  • Accepted: 5th-Sept-2022
  • Online: 31st-Oct-2022
Pages: 1 - 10
Views: 2746
Downloads: 2304
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Filtering the LiDAR point cloud based the Unmaned Aerial Vehilce (UAV - LiDAR) in the dense land cover areas to build a Digital Terrain Model (DTM) is a basic requirement of large-scale topographic mapping. The aim of this paper is to study the use of the Simple Morphological Filter (SMRF) with suitable parameters to separate the non-terrain points (trees, noise points, etc.) and the topographical points. The methods of this article are algorithmic programming and combining the two filtering algorithms including SMRF and distributed filtering. The various data input was studied in the Ba Be case study. These parameters include the grid width called Gcell (m), the radius of filters called nwd and the threshold of the feature elevation called Eth (m). The point cloud of the terrain obtained after applying the SMRF continues to be filtered using distributional filter with the algorithm keeping only minimum elevation in the filtering window in order to remove the locations of high density of points. Then, it will contribute to lighten the point capacity to build DTM, to accurately interpolate the contour lines and to ensure the aesthetics of large-scale topographic maps. The results of the study are the fomulas to estimate reasonable input parameters (Gcell = 3 m, nwd = 3, Eth = 0.2 m) of the two filters for the establishment of a topographic map of 1:2000 scale, 1 m level in the Ba Be national forest, Bac Kan province, Vietnam.

How to Cite
Tran, A.Trung, Tran, H.Hong and Quach, T.Manh 2022. Combination of morphological and distributional filtering for UAV - LiDAR point cloud density to establish the Digital Terrain Model (in Vietnamese). Journal of Mining and Earth Sciences. 63, 5 (Oct, 2022), 1-10. DOI:

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