ANALYSIS OF DIFFERENT METHODS FOR 3D RECONSTRUCTION OF NATURAL SURFACES FROM PARALLEL-AXES UAV IMAGES
ANALYSIS OF DIFFERENT METHODS FOR 3D RECONSTRUCTION OF NATURAL SURFACES FROM PARALLEL-AXES UAV IMAGES
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DOI：
10.1111/phor.12115
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发表时间：
2015-09-01
影响因子：
2.4
通讯作者：
Schneider, Danilo
中科院分区：
文献类型：
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作者：
Eltner, Anette;Schneider, Danilo
运动恢复结构（SfM）和密集匹配算法的最新进展使无人驾驶航空器（UAV）图像的表面重建具有高空间分辨率，允许对地球表面过程的新见解。然而，平行轴无人机图像配置固有的准确性问题。在这项研究中，数字高程模型（DEM）的质量进行评估，使用模拟无人机飞行的图像。比较了五种不同的SfM工具和三种不同的相机。如果地面控制点（GCPs）没有被整合到具有平行轴图像配置的调整过程中，则观察到显著的圆顶效应系统误差，其可以基于从UAV飞行之前或之后立即用会聚图像捕获的测试场检索的校准参数来减小。从无人机图像和地面激光扫描数据生成的土壤表面的DEM之间的比较表明，自然表面可以非常准确地从无人机图像重建，即使当GCPs丢失和简单的几何相机模型被认为是。
Recent advances in structure from motion (SfM) and dense matching algorithms enable surface reconstruction from unmanned aerial vehicle (UAV) images with high spatial resolution, allowing for new insights into earth surface processes. However, accuracy issues are inherent in parallel-axes UAV image configurations. In this study, the quality of digital elevation models (DEMs) is assessed using images from a simulated UAV flight. Five different SfM tools and three different cameras are compared. If ground control points (GCPs) are not integrated into the adjustment process with parallel-axes image configurations, significant dome-effect systematic errors are observed, which can be reduced based on calibration parameters retrieved from a testfield captured with convergent images immediately before or after the UAV flight. A comparison between DEMs of a soil surface generated from UAV images and terrestrial laser-scanning data show that natural surfaces can be very accurately reconstructed from UAV images, even when GCPs are missing and simple geometric camera models are considered.