Ground point extraction and DEM generation of traditional village landing surface points based on dense matching point cloud of UAV image: taking Dehang village in western Hunan as an example

doi:10.13474/j.cnki.11-2246.2021.362

Abstract

Abstract: For the problems of using UAV technology to obtain ground points and DEM in mountainous area with large fluctuation and dense vegetation, a method combining cloth simulation filtering algorithm and improved local maximum algorithm is proposed.This method uses vegetation information such as tree vertices and tree heights to extract ground points, and then generates DEM for the entire area.In this paper, Dehang village, a traditional village, is taken as an example. The point cloud is divided into two parts:dense vegetation area and non-dense vegetation area by vegetation coefficient and elevation information.In non-dense vegetation area, the cloth simulation filtering algorithm and improved local maximum algorithm are used to extract ground points,tree vertices and calculate the average tree height; in dense vegetation area, the approximate ground point of the dense vegetation area is calculated from the vertex of the tree in the area, and finally perform TIN interpolation on the two parts of the ground point cloud to get the DEM of the area. The experimental results show that the root mean square error of DEM generated by this method can reach 0.037 m in non-dense vegetation areas, 1.606 m in dense vegetation areas, and the overall average error can reach 1.492 m. The overall accuracy is good, which can basically meet the needs of spatial analysis at the village scale.

Key words: UAV, DEM, traditional villiage, cloth simulation filtering algorithm, local maximum algorithm

CLC Number:

P237

FU Xiangxiang, DENG Yunyuan, ZHENG Wenwu, ZHOU Shaoning, ZHOU Jialu. Ground point extraction and DEM generation of traditional village landing surface points based on dense matching point cloud of UAV image: taking Dehang village in western Hunan as an example[J]. Bulletin of Surveying and Mapping, 2021, 0(12): 1-5.

References

[1] 魏国忠. 农村区域房地一体化高精度测绘方法研究[J]. 测绘科学, 2020, 45(2):98-103.
[2] 于东海, 冯仲科. 基于无人机倾斜航空影像的树冠体积测算方法[J]. 农业工程学报, 2019, 35(1):90-97.
[3] 马东岭, 王晓坤, 李广云. 利用图割算法进行城市密集点云表面模型重建[J]. 测绘通报, 2019(2):45-48.
[4] 刘斌, 唐雅玲, 马晨燕, 等. 无人机倾斜摄影三维模型在城市雨洪风险评估中的应用[J]. 测绘通报, 2019(10):46-50.
[5] VOSSELMAN G. Slope based filtering of laser altimetry data[J]. International Archives of Photogrammetry and Remote Sensing, 2000, 33(B3):935-942.
[6] LIU J P, SHEN J, ZHAO R, et al. Extraction of individual tree crowns from airborne LiDAR data in human settlements[J]. Mathematical and Computer Modelling, 2013, 58(3/4):524-535.
[7] CHEN Q, GONG P, BALDOCCHI D, et al. Filtering airborne laser scanning data with morphological methods[J]. Photogrammetric Engineering & Remote Sensing, 2007, 73(2):175-185.
[8] YANG B S, HUANG R G, DONG Z, et al. Two-step adaptive extraction method for ground points and breaklines from lidar point clouds[J]. ISPRS Journal of Photo-grammetry and Remote Sensing, 2016, 119:373-389.
[9] AXELSSON P. DEM generation from laser scanner data using adaptive TIN models[J]. International Archives of Photogrammetry and Remote Sensing, 2000, 23(B4):110-117.
[10] 林鑫, 庞勇, 李春干. 无人机密集匹配点云与机载激光雷达点云的差异分析[J]. 林业资源管理, 2020(3):58-62.
[11] ZHANG Z, GERKE M, VOSSELMAN G, et al. Filtering photogrammetric point clouds using standard lidar filters towards dtm generation[J]. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2018, IV-2:319-326.
[12] 马瑞峰, 张力, 杜全叶, 等. 多源数据融合的DIM点云滤波及DEM生成[J]. 测绘科学, 2020, 45(1):131-137.
[13] RESSL C, BROCKMANN H, MANDLBURGER G, et al. Dense image matching vs. airborne laser scanning-comparison of two methods for deriving terrain models[J]. Photogrammetrie-Fernerkundung-Geoinformation, 2016(2):57-73.
[14] 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, 2016, 8(6):501.
[15] 张继贤, 林祥国, 梁欣廉. 点云信息提取研究进展和展望[J]. 测绘学报, 2017, 46(10):1460-1469.
[16] 张昌赛, 刘正军, 杨树文, 等. 基于LiDAR数据的布料模拟滤波算法的适用性分析[J]. 激光技术, 2018, 42(3):410-416.