Quality Assessment of DSMs Produced from UAV Flights Georeferenced with On-Board RTK Positioning

Abstract

High-resolution Digital Surface Models (DSMs) from unmanned aerial vehicles (UAVs) imagery with accuracy better than 10 cm open new possibilities in geosciences and engineering. The accuracy of such DSMs depends on the number and distribution of ground control points (GCPs). Placing and measuring GCPs are often the most time-consuming on-site tasks in a UAV project. Safety or accessibility concerns may impede their proper placement, so either costlier techniques must be used, or a less accurate DSM is obtained. Photogrammetric blocks flown by drones with on-board receivers capable of RTK (real-time kinematic) positioning do not need GCPs, as camera stations at exposure time can be determined with cm-level accuracy, and used to georeference the block and control its deformations. This paper presents an experimental investigation on the repeatability of DSM generation from several blocks acquired with a RTK-enabled drone, where differential corrections were sent from a local master station or a network of Continuously Operating Reference Stations (CORS). Four different flights for each RTK mode were executed over a test field, according to the same flight plan. DSM generation was performed with three block control configurations: GCP only, camera stations only, and with camera stations and one GCP. The results show that irrespective of the RTK mode, the first and third configurations provide the best DSM inner consistency. The average range of the elevation discrepancies among the DSMs in such cases is about 6 cm (2.5 GSD, ground sampling density) for a 10-cm resolution DSM. Using camera stations only, the average range is almost twice as large (4.7 GSD). The average DSM accuracy, which was verified on checkpoints, turned out to be about 2.1 GSD with the first and third configurations, and 3.7 GSD with camera stations only.

Coastal Mapping Using DJI Phantom 4 RTK in Post-Processing Kinematic Mode

“…In order to testing the potential of direct georeferencing with DJI-P4RTK with as few GCPs as possible, projects were created varying the number and the position of GCPs [27][28][29]. In particular, as already pointed out by Taddia et al using RTK camera locations for the same datasets [22] as well as Forlani et al [30] with a fixed wing aircraft, the nadiral dataset necessarily requires the use of at least one GCP for the model to obtain good accuracy. The lack of any GCP, indeed, may cause a wrong estimation of the focal length if only nadiral images are used, generating a model with a significant vertical offset of up to meters in some situations.…”

High-resolution digital terrain modelling of a rugged alpine terrain by fusing data from terrestrial laser scanning and UAV photogrammetry

“…Therefore, there was not sufficient time for placing and measuring any GCPs within the hardly transient AOI. Placing GCPs would improve the absolute accuracy of UAV-SfM data [11]. Despite the wind conditions were not optimal (wind speed 8-12 m/s), flying the UAV was possible and safe.…”

UAV Photogrammetry and Ground Surveys as a Mapping Tool for Quickly Monitoring Shoreline and Beach Changes

“…The kinematic positioning techniques PPK, RTK and NRTK allow, in undisturbed operating conditions, better accuracies than 5 cm in planimetry and height. Applying these surveying techniques in the estimation of the positions of the shutter centres of the cameras, together with a cross-strip flight, and just a single GCP positioned in the central area of the block, it is possible to obtain accuracies comparable to those obtainable through the use of a homogeneous distribution of GCPs [43]. In case of significant offsets between the GNSS antenna and the position of the camera, the presence of a synchronized onboard strapdown inertial navigation system (SINS) mechanically connected to the camera, allows to correct the orientation up to 0.10 • -0.05 • , even if the camera is mounted within a gimbal.…”