Rudolf Urban, Martin Štroner, Tomáš Křemen, Jaroslav Braun, Michael Möser
A novel approach to estimate systematic and random error of terrain derived from UAVs
Číslo: 3/2018
Periodikum: Acta Montanistica Slovaca
Klíčová slova: digital terrain model (DTM), UAV, photogrammetry, spoil heap
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Anotace:
In recent years, there has been a major development in the field of Unmanned Aerial Vehicles (UAVs) as well as a significant increase
in the use of aerial photogrammetry, which is an affordable alternative to using LiDAR. However, the nature of the data obtained from
photogrammetry differs from LiDAR data. Photogrammetry using the Structure from Motion (SfM) method is however computationally
complicated, and results can be affected by many influences. In this paper, data from two UAVs were compared. The first one is a
commercial eBee system produced by SenseFly equipped with a Sony Cyber-shot DCS-WX220 camera. The other is a home assembled
solution consisting of EasyStar II motorised glider and 3DR Pixhawk B autopilot equipped with Nikon Coolpix A camera. The area of spoil
heap was measured by both systems in the leaf-off period. Both systems were set up identically for data acquisition (overlapping, resolution),
which made a comparison of the output quality possible. The ground control points (GCPs) were placed in the study area and their position
determined by GNSS (RTK method).
A traditional approach for point clouds accuracy validation is their comparison with data of greater accuracy. Unfortunately, the
photogrammetry is often validated using GNSS points, the position of which is determined under different conditions than GCPs (different
daytime, number, and visibility of satellites, etc.). The magnitude of photogrammetry errors is theoretically the same as that of GNSS.
Therefore, in this study, we suggest a novel approach that can be used to compare the accuracy of UAV point clouds without the need for
additional validation data (for example, GNSS survey). To exemplify this approach, we used data gathered by two UAV systems (eBee and
Easy Star II). Particularly, we statistically estimated the accuracy of the UAV point clouds; used two approaches to estimate standard
deviations (one of them using estimated dependencies between data); and investigated the influence of the vegetation cover.
To determine the systematic and random errors of the UAV systems data, three areas were selected, each with a typical example of
vegetation on the spoil heap (forest, grass, bush). A comparison of the individual data in a grassy area suggests that the accuracy of the
differences is about 0.03 m, which corresponds to the actual pixel size. Average shift (systematic error) ranged from 0.01 m to 0.08 m. In the
forest terrain, the accuracy of data differences is about 0.04 m, which is slightly worse than in the grassy area. Bushy terrain data achieves
precision values between a grassy area and a forest area.
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in the use of aerial photogrammetry, which is an affordable alternative to using LiDAR. However, the nature of the data obtained from
photogrammetry differs from LiDAR data. Photogrammetry using the Structure from Motion (SfM) method is however computationally
complicated, and results can be affected by many influences. In this paper, data from two UAVs were compared. The first one is a
commercial eBee system produced by SenseFly equipped with a Sony Cyber-shot DCS-WX220 camera. The other is a home assembled
solution consisting of EasyStar II motorised glider and 3DR Pixhawk B autopilot equipped with Nikon Coolpix A camera. The area of spoil
heap was measured by both systems in the leaf-off period. Both systems were set up identically for data acquisition (overlapping, resolution),
which made a comparison of the output quality possible. The ground control points (GCPs) were placed in the study area and their position
determined by GNSS (RTK method).
A traditional approach for point clouds accuracy validation is their comparison with data of greater accuracy. Unfortunately, the
photogrammetry is often validated using GNSS points, the position of which is determined under different conditions than GCPs (different
daytime, number, and visibility of satellites, etc.). The magnitude of photogrammetry errors is theoretically the same as that of GNSS.
Therefore, in this study, we suggest a novel approach that can be used to compare the accuracy of UAV point clouds without the need for
additional validation data (for example, GNSS survey). To exemplify this approach, we used data gathered by two UAV systems (eBee and
Easy Star II). Particularly, we statistically estimated the accuracy of the UAV point clouds; used two approaches to estimate standard
deviations (one of them using estimated dependencies between data); and investigated the influence of the vegetation cover.
To determine the systematic and random errors of the UAV systems data, three areas were selected, each with a typical example of
vegetation on the spoil heap (forest, grass, bush). A comparison of the individual data in a grassy area suggests that the accuracy of the
differences is about 0.03 m, which corresponds to the actual pixel size. Average shift (systematic error) ranged from 0.01 m to 0.08 m. In the
forest terrain, the accuracy of data differences is about 0.04 m, which is slightly worse than in the grassy area. Bushy terrain data achieves
precision values between a grassy area and a forest area.