基于无人机影像密集匹配点云的传统村落地面点提取及DEM生成——以湘西德夯村为例

doi:10.13474/j.cnki.11-2246.2021.362

测绘通报 ›› 2021, Vol. 0 ›› Issue (12): 1-5.doi: 10.13474/j.cnki.11-2246.2021.362

• 学术研究 • 下一篇

基于无人机影像密集匹配点云的传统村落地面点提取及DEM生成——以湘西德夯村为例

付翔翔¹, 邓运员^1,2, 郑文武^1,2, 周邵宁¹, 周佳露¹

1. 衡阳师范学院地理与旅游学院, 湖南衡阳 421002;
2. 传统村镇文化数字化保护与创意利用技术国家地方联合工程实验室, 湖南衡阳 421002

收稿日期:2021-06-07 发布日期:2021-12-30
通讯作者: 邓运员。E-mail:dyydyy@163.com
作者简介:付翔翔(1996-),男,硕士,主要从事无人机影像点云数据挖掘研究的工作。E-mail:446788632@qq.com
基金资助:
国家自然科学基金（41771150）；教育部人文社会科学研究项目（16YJAZ006）；湖南省社会科学基金重点项目（17ZDB051）；湖南省教育厅优秀青年项目（19B078）；湖南省研究生科研创新项目（CX20190982）

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

FU Xiangxiang¹, DENG Yunyuan^1,2, ZHENG Wenwu^1,2, ZHOU Shaoning¹, ZHOU Jialu¹

1. College of Geography and Tourism, Hengyang Normal University, Hengyang 421002, China;
2. National-Local Joint Engineering Laboratory on Digital Preservation and Innovative Technologies for the Culture of Traditional Villages and Towns, Hengyang 421002, China

Received:2021-06-07 Published:2021-12-30

摘要/Abstract

摘要： 目前,针对利用无人机技术在山地起伏大、山体植被密集区域,难以获取地面点及DEM等问题,本文提出了一种结合布料模拟算法和改进的局部最大值算法,利用树顶点、树高等植被信息,提取地面点,进而生成整个区域的DEM的方法。以中国传统村落德夯村为例,利用植被系数和高程信息将点云分割为植被密集区和非植被密集区两个部分。在非植被密集区,通过布料模拟算法和改进的局部最大值算法分别提取地面点和树顶点,计算平均树高;在植被密集区,通过该区域的树顶点推算得到植被密集区的近似地面点,最终将两部分的地面点云进行TIN插值得到该地区的DEM。试验结果表明,利用此方法生成的DEM均方根误差,在非植被密集区达0.037 m,植被密集区可达1.606 m,整体平均误差达1.492 m,总体精度较好,基本可以满足村落尺度空间分析的需求。

关键词: 无人机, DEM, 传统村落, 布料模拟算法, 局部最大值算法

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

中图分类号:

P237

付翔翔, 邓运员, 郑文武, 周邵宁, 周佳露. 基于无人机影像密集匹配点云的传统村落地面点提取及DEM生成——以湘西德夯村为例[J]. 测绘通报, 2021, 0(12): 1-5.

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.

参考文献

[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.

基于无人机影像密集匹配点云的传统村落地面点提取及DEM生成——以湘西德夯村为例

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

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