基于地面控制点数量与分布的廊道测绘无人机摄影测量精度评价

Remote. Sens. Pub Date : 2020-07-30 DOI:10.3390/rs12152447
Ezequiel Ferrer-González, F. Agüera-Vega, F. Carvajal-Ramírez, P. Martínez-Carricondo
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引用次数: 56

摘要

无人机(UAV)摄影测量最近成为一种流行的解决方案,用于获得线性项目所需的某些产品,如正射影像或数字表面模型。这主要是由于它能够以快速和经济的方式提供这些地形产品。为了保证一定程度的准确性,了解需要多少地面控制点(gcp)以及如何在整个研究地点分布它们是很重要的。这项工作的目的包括确定gcp的数量以及如何以一种能够在走廊形状的研究区域产生更高精度的方式分布它们。为此,进行了几个摄影测量项目,其中用于束平差的gcp数量及其分布各不相同。不同项目的评估考虑了两个不同的参数:均方根误差(RMSE)和多尺度模型到模型云比较(M3C2)。从测试的不同配置来看,与使用类似或更多gcp的项目相比,使用9和11个gcp(分别为4.3和5.2 gcp km−1)的项目以偏移或之字形模式交替分布在道路两侧,在道路两端各有一对gcp,在实地工作成本方面产生了最佳结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
UAV Photogrammetry Accuracy Assessment for Corridor Mapping Based on the Number and Distribution of Ground Control Points
Unmanned aerial vehicle (UAV) photogrammetry has recently emerged as a popular solution to obtain certain products necessary in linear projects, such as orthoimages or digital surface models. This is mainly due to its ability to provide these topographic products in a fast and economical way. In order to guarantee a certain degree of accuracy, it is important to know how many ground control points (GCPs) are necessary and how to distribute them across the study site. The purpose of this work consists of determining the number of GCPs and how to distribute them in a way that yields higher accuracy for a corridor-shaped study area. To do so, several photogrammetric projects have been carried out in which the number of GCPs used in the bundle adjustment and their distribution vary. The different projects were assessed taking into account two different parameters: the root mean square error (RMSE) and the Multiscale Model to Model Cloud Comparison (M3C2). From the different configurations tested, the projects using 9 and 11 GCPs (4.3 and 5.2 GCPs km−1, respectively) distributed alternatively on both sides of the road in an offset or zigzagging pattern, with a pair of GCPs at each end of the road, yielded optimal results regarding fieldwork cost, compared to projects using similar or more GCPs placed according to other distributions.
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