| 基于密集匹配点云的DEM生产可行性研究 |
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| 引用本文： | 黄皓中,苏鹏浩,王冉,王峰,史韦韦,赵剑飞.基于密集匹配点云的DEM生产可行性研究[J].测绘通报,2022,0(9):170-174. |
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| 作者姓名： | 黄皓中 苏鹏浩 王冉 王峰 史韦韦 赵剑飞 |
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| 作者单位： | 1. 北京飞马航遥科技有限公司, 北京 100192;2. 内蒙古自治区测绘地理信息中心, 内蒙古 呼和浩特 010010 |
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| 基金项目： | 国家自然科学基金青年科学基金(41801383) |
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| 摘 要： | 无人机倾斜摄影直接生产的成果通常包括三维模型、TDOM、DSM等,然而规划设计通常不能直接利用倾斜数据输出的DEM,需要辅以人工编辑。作为倾斜摄影影像处理的过程成果,密集匹配点云未得到充分利用。其与激光雷达点云具备相似的结构,且点云密度可自由选择,在不考虑数据量的情况下,密集匹配点云的点密度可数倍于激光雷达点云。此外,密集匹配点云无需单独赋色,即具有纹理信息, 对人工目视编辑自动分类后的地面点具有一定的辅助作用。本文对比分析了同一测区的密集匹配点云与激光雷达点云,验证了密集匹配点云用于房屋建筑区及稀疏林区地面点滤波并生产DEM的可行性。
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| 关 键 词： | 激光雷达点云 密集匹配点云 地面点滤波 数字高程模型 |
| 收稿时间： | 2022-03-23 |
The feasibility study of DEM production based on dense matching point cloud |
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| HUANG Haozhong,SU Penghao,WANG Ran,WANG Feng,SHI Weiwei,ZHAO Jianfei.The feasibility study of DEM production based on dense matching point cloud[J].Bulletin of Surveying and Mapping,2022,0(9):170-174. |
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| Authors: | HUANG Haozhong SU Penghao WANG Ran WANG Feng SHI Weiwei ZHAO Jianfei |
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| Institution: | 1. Beijing Feima Hangyao Technology Co., Ltd., Beijing 100192, China;2. The Surveying and Mapping Geographic Information Center of Inner Mongolia, Hohhot 010010, China |
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| Abstract: | The direct outputs of UAV oblique photography usually include 3D mesh, TDOM, DSM and so on. However, DEM that can be used for design and planning can't be directly generated without manual edits. The dense matching point clouds produced by the oblique images processing procedure are not fully used. The dense matching point clouds, similar to the structure of LiDAR point clouds, may be freely to set the point density. Regardless of the data amount, the density of dense matching point cloud can be multiple time of that of LiDAR point cloud. Besides, the dense matching point clouds have texture information without separate color assignment, which has a certain auxiliary effect on artificial visual editing after automatic classification of ground points. This paper compares and analyzes the dense matching point cloud and LiDAR point cloud, separately based on the basis of the oblique photographed data and LiDAR data in the same survey area, and verifies the feasibility of dense matching point cloud for ground point filtering and DEM production in the housing area and sparse forest area. |
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| Keywords: | LiDAR point cloud dense matching point cloud ground cloud point filter DEM |
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