Abstract Structure-from-motion (SfM) and multi-view stereo (MVS) algorithms coupled with the use of unmanned aerial vehicles (UAVs) have become a popular tool in the geosciences for modelling complex landscapes on-demand allowing for high-resolution topographic change-detection studies to be conducted at minimal cost. To identify the effects of UAV image orientation on the accuracy of SfM-MVS 3D surface models, we tested four different UAV image acquisition schemes that incorporated both nadir and oblique imagery of an agricultural field. The coupling of nadir and oblique imaging angles led to the highest surface model accuracy in the absence of ground control points (GCPs; vertical RMSE: 0.047 m, horizontal RMSE: 0.019 m), while with a normative distribution of GCPs the nadir-only image sets had similar accuracy metrics (vertical RMSE 0.028 m, horizontal RMSE 0.017 m) to surface models generated with nadir and oblique imaging angles (vertical RMSE 0.028 m, horizontal RMSE 0.013 m). Homologous keypoint matching between nadir and oblique imagery was poor when the survey conditions were bright and the surface texture of the field was homogeneous, leading to broad-scale vertical noise in the generated surface models. Results indicate that a nadir-only image set accompanied with a dense deployment of GCPs is the most ideal for SfM-MVS agricultural 3D surface reconstructions. The diachronic analysis of surface models generated from nadir-only image sets were able to detect surface-change >0.040 m in depth (i.e., rill and gully erosion, depositional zones) and were comparable to results obtained from a terrestrial laser scanner. Stable GCPs should be used where possible to ensure precise co-registration between subsequent UAV surveys.

中文翻译：

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绘制农业景观中的侵蚀和沉积：优化 SfM-MVS 的无人机图像采集方案

摘要 运动结构 (SfM) 和多视点立体 (MVS) 算法与无人机 (UAV) 的使用相结合，已成为地球科学中用于按需对复杂景观进行建模的流行工具，允许高分辨率地形以最低成本进行变化检测研究。为了确定无人机图像方向对 SfM-MVS 3D 表面模型精度的影响，我们测试了四种不同的无人机图像采集方案，这些方案结合了农田的最低点和倾斜图像。在没有地面控制点（GCP；垂直 RMSE：0.047 m，水平 RMSE：0.019 m）的情况下，天底和倾斜成像角的耦合导致最高的表面模型精度，而 GCP 的规范分布则是仅天底图像集合具有相似的精度指标（垂直 RMSE 0.028 m，水平 RMSE 0.017 m）到使用天底和倾斜成像角（垂直 RMSE 0.028 m，水平 RMSE 0.013 m）生成的表面模型。当测量条件明亮且场的表面纹理均匀时，天底和倾斜图像之间的同源关键点匹配较差，导致生成的表面模型中存在大尺度垂直噪声。结果表明，带有密集部署的 GCP 的仅最低点图像集是 SfM-MVS 农业 3D 表面重建的最理想选择。从仅最低点图像集生成的表面模型的历时分析能够检测深度 >0.040 m 的表面变化（即细沟和沟壑侵蚀、沉积区），并且与从地面激光扫描仪获得的结果相当。