Influence of AGL flight and off-nadir images on UAV-SfM accuracy in complex morphology terrains

Abstract

In the field of geosciences and engineering, situations arise where special attention have to be paid to the planning of the UAV-photogrammetric project, i.e., terrain with complex geometry and steep slopes. The use of off-nadir imagery and flights at a fixed height above ground level (AGL) are postulated as possible factors to be considered to achieve high accuracies. The objective of this study is to evaluate the influence of image angle, frontal and side overlaps, and type of flight (above mean sea level (AMSL) or AGL), on the accuracy of the dense 3 D point cloud yielded by UAV-SfM. The results obtained showed that imagery with an angle between 20° and 35° in two perpendicular flight line directions, 90/90, 90/70 or 70/70 overlap and AGL flight is the optimal combination for best accuracy and high precision. With nadir imagery, the combination of factors that gives the most accuracy results are AGL flights with a 90/70 overlap.

Enhancing UAV-SfM Photogrammetry for Terrain Modeling from the Perspective of Spatial Structure of Errors

“…We provide more insights into image collection strategies than traditional overall accuracy indexes, such as RMSE. Previous studies have confirmed that oblique photography with a larger angle can effectively reduce elevation error [24,25]. The experimental results presented in this paper support this finding.…”

“…For consumer UAVs, studies have shown that multiple flight lines by flying in a "double-grid" (two orthogonal blocks) or nadir images supplemented with convergent images can achieve similar results to that of using a five-lens tilt camera. Furthermore, using higher overlap and higher oblique camera angles (20-35 • ) can effectively improve the overall accuracy of terrain modeling for consumer UAVs [24,25].…”

Multitemporal Analysis of Slow-Moving Landslides and Channel Dynamics through Integrated Remote Sensing and In Situ Techniques

“…The accuracy of the 3D reconstruction was available at this step. The accuracy is expressed as the discrepancy between the measured and reconstructed coordinates of GCPs; this option is commonly accepted in the absence of validation points [54,65,102]. The values are shown in Table 3.…”

“…SfM photogrammetry requires metric references in order to georeference the obtained results; to this aim, ground control points (GCPs) or RTK UAVs can be used. The overall quality of the obtained 3D reconstructions can be affected by the overlap between the images, image resolution, the geometry of acquisition, the numerosity and distribution of GCPs, and the accuracy of their coordinates [60][61][62][63][64][65]. UAV photogrammetry can be effectively applied in small-medium extension areas (a few tenth of a Ha) with low-sparse vegetation; it is suited for volume computations and surface deformation controls [66].…”

Filtering Green Vegetation Out from Colored Point Clouds of Rocky Terrains Based on Various Vegetation Indices: Comparison of Simple Statistical Methods, Support Vector Machine, and Neural Network

“…It is worth noting that although mass data collection methods are currently very popular in many fields of research involving complex terrain [26], such as coastal monitoring [27][28][29], volcano exploration [30], underground research [31,32], tree detection [33,34], the evolution of rock glaciers [35], monitoring of rock masses [36][37][38], or their geological analysis [39], vegetation filtering in such cases is typically handled by human operators as the performance of automated algorithms proposed for this purpose, so far, is generally poor as confirmed by Blanco et al [6]. Specifically, they usually have problems when encountering highly rugged and/or sloped terrain [15].…”