测绘通报 ›› 2024, Vol. 0 ›› Issue (4): 35-40.doi: 10.13474/j.cnki.11-2246.2024.0407

• 学术研究 • 上一篇    

无人机载LiDAR点云密度对DEM精度的影响分析

肖杰   

  1. 山西省测绘地理信息院, 山西 太原 030001
  • 收稿日期:2023-08-07 发布日期:2024-04-29
  • 作者简介:肖杰(1980—),男,博士,高级工程师,主要从事卫星导航定位及基准建设方面的研究工作。E-mail:xiaojie19801208@163.com
  • 基金资助:
    山西省测绘地理信息院科技资助项目(2021-KK1)

Analysis of the effects of UAV-borne LiDAR point cloud density on DEM accuracy

XIAO Jie   

  1. Shanxi Institute of Surveying and Mapping Geographic Information, Taiyuan 030001, China
  • Received:2023-08-07 Published:2024-04-29

摘要: 无人机载LiDAR点云数据是目前生产DEM的重要数据源。为进一步提升DEM构建效率,本文选取平地和山地两类地形作为试验区,利用基于不规则三角网的点云抽稀算法,对滤波处理后的地面点云数据分别按照80%、60%、40%等7种地面点保留率进行抽稀,并用平均误差(ME)、标准差(SD)、均方根误差(RMSE)3个指标,对不同抽稀比例下生产的DEM成果进行综合精度评定。结果表明:①平地地面点云密度达2点/m2,山地地面点云密度达9点/m2时,生产的0.5 m格网间距的DEM精度优于0.05 m;②随着地面点云密度的增加,DEM精度水平逐渐趋于稳定,当地面点云密度抽稀到1点/m2时,DEM精度快速下降。针对无人机载LiDAR点云数据进行大范围工程化DEM生产任务,该研究结论对降低数据获取成本、提升DEM生产效率具有一定的指导和借鉴意义。

关键词: 点云密度, DEM, LiDAR点云, 点云抽稀, 无人机载

Abstract: UAV-borne LiDAR point cloud data is an important data source for producing DEM. In order to further improve DEM production efficiency,selecting flat terrain and mountainous terrain as test areas,the ground point cloud,which is processed by filtering method,is thinned and simplified according to the algorithm based on TIN with seven different the ground point cloud retention rate of 80%,60%,40%,and so on,and the corresponding DEM is generated and its accuracy is evaluated by mean error (ME),standard deviation (SD),and root mean square error (RMSE). The results show that: ①The accuracy of the produced 0.5 m grid-spacing DEM could exceed 0.05 m when the ground point cloud density reached 2 points/m2 for flat terrain and 9 points/m2 for mountainous terrain. ②As the density of ground point cloud increases,the DEM accuracy level gradually stabilizes,and the DEM accuracy would decrease rapidly when the ground point cloud density is thinned to 1 point/m2. For the DEM production tasks in large regions using UAV-borne LiDAR point cloud data,the conclusions of this research have a certain guiding and reference significance in reducing data acquisition costs and improving DEM production efficiency.

Key words: point cloud density, digital elevation model, LiDAR point cloud, point cloud thinning, UAV-borne

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