发表机构
AGH University of Krakow; The Mærsk Mc-Kinney Møller Institute, University of Southern Denmark（克拉科夫AGH大学; 南丹麦大学马士基·麦克-凯尼·穆勒研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过全因子评估768种处理变体，揭示光束法区域网平差参数对无人机摄影测量3D精度的影响，最佳RMSE为16毫米，并显著降低系统误差，为高精度监测流程优化提供指导。
AI 中文摘要
无人机（UAV）摄影测量越来越多地用于需要高精度的应用，例如确定由滑坡、采矿或微地形变化引起的地表变化。虽然采集策略已被广泛研究，但处理流程（特别是光束法区域网平差参数设置）的影响仍未得到充分探索。本研究通过系统、全因子评估768种处理变体来填补这一空白，这些变体应用于在220公顷研究区域内1.5年间采集的十个无人机数据集。分析了八个关键参数。结果显示最终3D精度存在显著变异性：表现最佳的变体实现了16毫米的均方根误差（RMSE），而最弱的变体达到了303毫米。最有影响力的因素是地面控制点的数量、附加相机校准校正的应用，以及使用后处理动态GNSS方法确定相机投影中心坐标。研究还评估了流程优化如何影响位移、倾斜变化和水平应变确定的精度。虽然随机位移误差保持稳定（RMSE约为6-7毫米），但系统误差在所有轴向上显著减少了一半以上，与作者先前使用的基线相比，优化配置中垂直中位数绝对误差从14毫米降至7毫米。本研究首次提供了大规模、面向实践的评估，说明处理参数选择如何影响摄影测量产品和变形指数确定的精度。结果为开发更稳健、可重复的、针对高精度监测的无人机摄影测量流程提供了可操作的指导。
英文摘要
Unmanned aerial vehicle (UAV) photogrammetry is increasingly used in applications requiring high accuracy, such as determining ground surface changes caused by landslides, mining, or microrelief transformation. While acquisition strategies have been widely studied, the influence of the processing workflow-particularly Bundle Block Adjustment parameter settings-remains insufficiently explored. This study addresses this gap through a systematic, full-factorial evaluation of 768 processing variants applied to ten UAV datasets collected over 1.5 years in a 220 ha study area. Eight key parameters were analysed. The results show substantial variability in final 3D accuracy: the best performing variant achieved a root mean square error (RMSE) of 16 mm, whereas the weakest reached 303 mm. The most influential factors were the number of ground control points, the application of additional camera calibration corrections, and the use of the Post-Processing Kinematic GNSS method for determining camera projection center coordinates. The study also evaluates how workflow optimization affects the accuracy of displacement, tilt changes, and horizontal strain determination. While random displacement errors remained stable (RMSE of ~6-7 mm), systematic errors were significantly reduced by over half in all axes, with vertical median absolute error decreasing from 14 mm to 7 mm in the optimized configuration compared to the baseline previously used by the authors. This study provides the first large-scale, practice-oriented assessment of how processing parameter selection shapes the accuracy of both photogrammetric products and deformation indices determination. The results offer actionable guidance for developing more robust and repeatable UAV photogrammetry workflows tailored to high-precision monitoring.
Journal refMeasurement, Volume 265, 2026, 120315, ISSN 0263-2241
DOI：10.1016/j.measurement.2026.120315
