Accuracy of Unmanned Aerial Vehicle (UAV) and SfM Photogrammetry Survey as a Function of the Number and Location of Ground Control Points Used

Abstract

The geometrical accuracy of georeferenced digital surface models (DTM) obtained from images captured by micro-UAVs and processed by using structure from motion (SfM) photogrammetry depends on several factors, including flight design, camera quality, camera calibration, SfM algorithms and georeferencing strategy. This paper focusses on the critical role of the number and location of ground control points (GCP) used during the georeferencing stage. A challenging case study involving an area of 1200+ ha, 100+ GCP and 2500+ photos was used. Three thousand, four hundred and sixty-five different combinations of control points were introduced in the bundle adjustment, whilst the accuracy of the model was evaluated using both control points and independent check points. The analysis demonstrates how much the accuracy improves as the number of GCP points increases, as well as the importance of an even distribution, how much the accuracy is overestimated when it is quantified only using control points rather than independent check points, and how the ground sample distance (GSD) of a project relates to the maximum accuracy that can be achieved.

Evaluating the potential of post-processing kinematic (PPK) georeferencing for UAV-based structure- from-motion (SfM) photogrammetry and surface change detection

“…As already indicated by many studies, a single onboard GPS provides meter-level accuracy (Turner et al, 2012a). The quality of GCP-based georeferencing depends on the number and distribution of GCPs (Sanz-Ablanedo et al, 2018). The accuracy can be improved by introducing additional and more densely distributed GCPs, which induces a trade-off between survey time and the quality of surface reconstruction (Eltner et al, 2016;Smith et al, 2016).…”

“…However, GCPs need to be placed as a network, and this comes at a cost as it is time-consuming. Furthermore, the accuracy depends on the quantity and distribution of GCPs (Sanz-Ablanedo et al, 2018). When used in a monitoring study, additional issues arise from the fact that GCPs can move (weather impact or surface deformations).…”

Measuring Change Using Quantitative Differencing of Repeat Structure-From-Motion Photogrammetry: The Effect of Storms on Coastal Boulder Deposits

“…The 2017 images required very little adjustment, but the corrections were essential for the 2015 GoPro images, which had strong barrel (or "fisheye" distortion). More complex and sophisticated lens correction techniques exist [41,54,[115][116][117][118] but Adobe's lens profiles have been tested and recommended in the photogrammetric literature [64,[119][120][121][122]. The quality of Lightroom's corrections are evident in before-and-after images ( Figure 6), and in the quality of the final model.…”

“…Although GCPs are not required for model generation, their inclusion improves positional accuracy [47,116]. GCPs acquired during the 2017 survey were brought into the model via Agisoft's import function.…”

The Use of Unmanned Aerial Systems to Map Intertidal Sediment

“…Note that not all GCPs are shown for the first flight, which covered a wider area than subsequently analyzed. Number of GCPs per 100 photos ranged from 1.8 (Appledore) to 5.9 (Neath flight on 03/05/2018); this means that all flights have a reasonable number of GCPs [32].…”