Abstract
Unmanned aerial vehicle (UAV) photogrammetry has recently emerged as a popular solution to obtain certain products necessary in linear projects, such as orthoimages or digital surface models. This is mainly due to its ability to provide these topographic products in a fast and economical way. In order to guarantee a certain degree of accuracy, it is important to know how many ground control points (GCPs) are necessary and how to distribute them across the study site. The purpose of this work consists of determining the number of GCPs and how to distribute them in a way that yields higher accuracy for a corridor-shaped study area. To do so, several photogrammetric projects have been carried out in which the number of GCPs used in the bundle adjustment and their distribution vary. The different projects were assessed taking into account two different parameters: the root mean square error (RMSE) and the Multiscale Model to Model Cloud Comparison (M3C2). From the different configurations tested, the projects using 9 and 11 GCPs (4.3 and 5.2 GCPs km−1, respectively) distributed alternatively on both sides of the road in an offset or zigzagging pattern, with a pair of GCPs at each end of the road, yielded optimal results regarding fieldwork cost, compared to projects using similar or more GCPs placed according to other distributions.
Highlights
The availability of high-resolution topographic products, such as orthoimages and digital surface models (DSM), is of increasing importance for many different fields of engineering that require a thorough understanding of topographies
The results demonstrate that the extent to which the accuracy improves as the number of ground control points (GCPs) increases; the accuracy depends on the location of the GCPs
For all four types of distribution considered in this study, the planimetric accuracy (RMSEXY) decreases as the number of GCPs increases (Figure 9)
Summary
The availability of high-resolution topographic products, such as orthoimages and digital surface models (DSM), is of increasing importance for many different fields of engineering that require a thorough understanding of topographies These include, among many others, terrain morphology to perform reliable simulation of soil erosion, flooding phenomena, and assessment of the sediment budget [1,2,3,4,5], landslide mapping and multi-temporal study [6,7,8], road design [9], road condition surveys for road management [10], precision agriculture [11], or detection of archaeological rests [12]. Most available software applications currently used to process UAV-acquired imagery are based on the structure from motion (SfM) approach This approach, unlike traditional digital photogrammetry, resolves the collinearity equations without the need for any control point, providing a sparse point cloud in an arbitrary coordinate system and a full camera calibration [16,17]. SfM is paired with multi-view stereopsis (MVS) techniques that apply an expanding procedure of the sparse set of matched keypoints in order to obtain a dense point cloud [19]
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