The accuracy of digital elevation models (DEMs) derived from structure-from-motion (SFM) multi-view stereo (MVS) 3D reconstruction is commonly computed for a single realization of model elevations. This approach may be adequate to estimate an overall measure of systematic error; however, it cannot provide a good estimation of measurement precision. Knowing measurement precision is crucial for measuring elevation surface changes observed by DEM comparisons. In this paper, we illustrate an approach to characterize spatial variation in the precision for SFM-MVS derived DEMs. We use a snow-covered surface of an active rock glacier located in the southern French Alps as the case study. A spatially varying precision estimate is calculated from repeated close-range aerial surveys for a single acquisition period by calculating the standard deviation per grid cell between the DEMs created for each flight repetition. Regression analysis using a generalized additive model (GAM) is performed to model the estimated precision and provide insights regarding how sensor, survey design and field site conditions may spatially influence the measurement precision. Additionally, we define how DEM error can be described differently depending on the available validation data. In our study image height above ground level and distance to ground control points had the greatest explanatory power for spatial variation in DEM precision. Image overlap, mean reprojection error and saturation were also useful for explaining spatially varying measurement precision of the DEMs. Field site characteristics, such as slope angle and shading, had the least importance in our model of precision. From a practical point of view, regression-modeled relationships between precision and image and site characteristics can be utilized to design future surveys with similar sensing platforms and site conditions for improved DEM precision.

中文翻译：

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基于重复近距离航测的运动结构多视图立体数字高程模型精度建模

从运动结构 (SFM) 多视图立体 (MVS) 3D 重建导出的数字高程模型 (DEM) 的准确性通常是针对模型高程的单一实现计算的。这种方法可能足以估计系统误差的总体度量；然而，它不能提供对测量精度的良好估计。了解测量精度对于测量 DEM 比较观察到的高程表面变化至关重要。在本文中，我们说明了一种表征 SFM-MVS 衍生 DEM 精度空间变化的方法。我们使用位于法国阿尔卑斯山南部的活动岩石冰川的积雪表面作为案例研究。通过计算为每次飞行重复创建的 DEM 之间的每个网格单元的标准偏差，从单个采集周期的重复近距离航测中计算出空间变化的精度估计。使用广义加性模型 (GAM) 进行回归分析以对估计精度进行建模，并提供有关传感器、调查设计和现场条件如何在空间上影响测量精度的见解。此外，我们定义了如何根据可用的验证数据对 DEM 错误进行不同的描述。在我们的研究中，地面以上图像高度和到地面控制点的距离对 DEM 精度的空间变化具有最大的解释力。图像重叠，平均重投影误差和饱和度也可用于解释 DEM 的空间变化测量精度。在我们的精度模型中，场地特性，如坡度角和阴影，最不重要。从实用的角度来看，精度与图像和站点特征之间的回归建模关系可用于设计具有类似传感平台和站点条件的未来调查，以提高 DEM 精度。