测绘通报 ›› 2021, Vol. 0 ›› Issue (7): 65-69.doi: 10.13474/j.cnki.11-2246.2021.0210

• 学术研究 • 上一篇    下一篇

面向复杂山地的机载LiDAR与无人机影像融合应用

高莎1, 袁希平2,3, 甘淑1, 杨亚复4, 李霞4   

  1. 1. 昆明理工大学国土资源工程学院, 云南 昆明 650093;
    2. 云南省高校高原山地空间信息测绘技术应用工程研究中心, 云南 昆明 650093;
    3. 滇西应用技术大学, 云南 大理 671006;
    4. 云南省水利水电勘测设计研究院, 云南 昆明 650093
  • 收稿日期:2020-07-31 修回日期:2020-09-22 出版日期:2021-07-25 发布日期:2021-08-04
  • 通讯作者: 袁希平。E-mail:n1480@qq.com
  • 作者简介:高莎(1993-),女,博士生,主要从事地面三维激光扫描理论与技术应用研究。E-mail:2441689096@qq.com
  • 基金资助:
    国家自然科学基金(41861054);云南省自然科学基金(2015FA016)

Application of airborne LiDAR and UAV image fusion for complicated terrain

GAO Sha1, YUAN Xiping2,3, GAN Shu1, YANG Yafu4, LI Xia4   

  1. 1. Faculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming 650093, China;
    2. Plateau Mountain Spatial Information Survey Technique Application Engineering Research Center at Yunnan Province's Universities, Kunming 650093, China;
    3. West Yunnan University of Applied Sciences, Dali 671006, China;
    4. Yunnan Institute of Water & Hydropower Engineering Investigation, Design and Research, Kunming 650093, China
  • Received:2020-07-31 Revised:2020-09-22 Online:2021-07-25 Published:2021-08-04

摘要: 本文针对LiDAR点云与无人机影像数据特征的优缺点,利用LiDAR点云与无人机DOM影像融合,将影像数据光谱信息赋给LiDAR点云数据,使其不仅具备精准的空间结构信息,还能得到清晰的纹理信息。为验证融合数据应用的可行性与数据提取的准确性,对融合前后的点云数据进行地面点提取与DEM构建。试验表明:将无人机影像的光谱信息赋给LiDAR点云数据,可以实现LiDAR点云数据从四维度表达到七维度的拓展,融合后点云数据具有清晰的纹理信息,地物类型判读更加容易,地面点分离完整;通过DEM模型的对比分析,融合后点云数据构建的DEM模型表达更加接近真实地表。研究结果为多源点云数据的深化应用提供了一定的技术方法支持作用。

关键词: 机载LiDAR, 点云数据, 无人机影像, 数据融合, 模型构建

Abstract: Aiming at the advantages and disadvantages of LiDAR point cloud and UAV image data, the LiDAR point cloud and DOM image of UAV are combined to transfer the spectral information of image data to LiDAR point cloud data, so that it not only has accurate spatial structure information, but also can obtain clear texture information. In order to verify the feasibility of fusion data application and the accuracy of data extraction, ground point extraction and DEM construction are carried out on point cloud data before and after fusion. The experiment shows that the LiDAR point cloud data can be extended from four dimensions to seven dimensions by transferring the spectral information of UAV image to the LiDAR point cloud data. After the fusion, the point cloud data has clear texture information, the ground object type is easier to interpret, and the ground points are separated completely. Through the comparative analysis of DEM model, the expression of DEM model constructed by integrating point cloud data is closer to the real surface. The research results provide some technical support for the further application of multi-source point cloud data.

Key words: airborne LiDAR, point cloud, UAV images, data fusion, model construction

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