Determining the Optimum Number of Ground Control Points for Obtaining High Precision Results Based on UAS Images

Abstract. Technological improvement of Unmanned Aerial Vehicles (UAVs) and computer vision algorithms, such as Structured-from-Motion (SfM) and Multi-view Stereo (MVS) have provided the possibility for high-resolution mapping and high-density point cloud generation using low-cost equipment and sensors. Orthomosaics and Digital Terrain Model (DTM) are the main digital products considering mapping purposes. Their quality is directly related to the sensors boarded on the UAV and data processing. Ground Control Points (GCPs) are used in the process of indirect georeferencing and also to model the lens distortions. The number of GCPs used in this process affects the positional accuracy of the final products. This study aims to determine the optimum number of GCPs to achieve high accuracy orthomosaics and DTM. To obtain this optimum number, an area of 3.85 ha was mapped with a low-cost UAV DJI Phantom 4 Advanced at 31 m flying height, lateral and longitudinal overlap of 90% and 80%, respectively, and using 22 checkpoints for quality assessment. For the experiments, different configuration were used both for the number of GCPs and for the use of self-calibration process or pre-calibrated camera IOP (Interior Orientation Parameters). The results show that for the flight configuration used in this work and for the mentioned UAV, a total of 5 GCPs, with pre-calibrated camera IOP, yields an accuracy of 0.023 m for X, 0.031 m for Y and 0.033 m for Z.

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Improving the Accuracy of Aerial Photography Using Ground Control Points

Agricultural machinery and technologies ◽

10.22314/2073-7599-2021-15-4-42-47 ◽

2021 ◽

Vol 15 (4) ◽

pp. 42-47

Author(s):

R. K. Kurbanov ◽

N. I. Zakharova ◽

D. M. Gorshkov

Keyword(s):

Data Processing ◽

Unmanned Aerial Vehicle ◽

High Precision ◽

Aerial Photography ◽

Ground Control ◽

Control Points ◽

Post Processing ◽

Standard Data ◽

Ground Control Points ◽

Aerial Vehicle

The authors showed that it is possible to quickly collect up-to-date information on the agricultural land condition using an unmanned aerial vehicle. It was noted that the use of ground control points increases the accuracy of project measurements, helps to compare the project post-processing results with the real measurements. (Research purpose) To compare the results of standard and high-precision post-processing of aerial survey data using ground control points. (Materials and methods) Aerial photography was carried out on a 1.1- hectare breeding field. The authors used DJI Matrice 200 v2 unmanned aerial vehicle with a GNSS L1/L2 receiver and a modified DJI X4S camera, five control points sized 50 × 50 centimeters and an EMLID Reach RS2 multi-frequency GNSS receiver. The results of scientific research into the use of ground control points during aerial photography were studied. (Results and discussion) It was found out that the error of georeferencing images obtained by an unmanned aerial vehicle without control points is significantly higher during the standard data processing compared to the high-precision one. The project error when using five control points is 3.9 times higher during the standard data processing. (Conclusions) It was shown that using ground control points it is possible to improve the project measurement accuracy, as well as compare the project post-processing results with the measurements on the ground. It was detected that the high-precision monitoring enables the use of fewer ground control points. It was found out that in order to obtain data with the accuracy of 2-4 centimeters in plan and height, at least 3 ground control points need to be used during the high-precision post-processing.

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Determining the Suitable Number of Ground Control Points for UAS Images Georeferencing by Varying Number and Spatial Distribution

Remote Sensing ◽

10.3390/rs12050876 ◽

2020 ◽

Vol 12 (5) ◽

pp. 876 ◽

Cited By ~ 5

Author(s):

Valeria-Ersilia Oniga ◽

Ana-Ioana Breaban ◽

Norbert Pfeifer ◽

Constantin Chirila

Keyword(s):

Coordinate System ◽

High Precision ◽

Random Distribution ◽

Low Cost ◽

Local Coordinate System ◽

Point Clouds ◽

Ground Control ◽

Unmanned Aerial Systems ◽

Control Points ◽

Ground Control Points

Currently, products that are obtained by Unmanned Aerial Systems (UAS) image processing based on structure-from-motion photogrammetry (SfM) are being investigated for use in high precision projects. Independent of the georeferencing process being done directly or indirectly, Ground Control Points (GCPs) are needed to increase the accuracy of the obtained products. A minimum of three GCPs is required to bring the results into a desired coordinate system through the indirect georeferencing process, but it is well known that increasing the number of GCPs will lead to a higher accuracy of the final results. The aim of this study is to find the suitable number of GCPs to derive high precision results and what is the effect of GCPs systematic or stratified random distribution on the accuracy of the georeferencing process and the final products, respectively. The case study involves an urban area of about 1 ha that was photographed with a low-cost UAS, namely, the DJI Phantom 3 Standard, at 28 m above ground. The camera was oriented in a nadiral position and 300 points were measured using a total station in a local coordinate system. The UAS images were processed using the 3DF Zephyr software performing a full BBA with a variable number of GCPs i.e., from four up to 150, while the number and the spatial location of check points (ChPs) was kept constant i.e., 150 for each independent distribution. In addition, the systematic and stratified random distribution of GCPs and ChPs spatial positions was analysed. Furthermore, the point clouds and the mesh surfaces that were automatically derived were compared with a terrestrial laser scanner (TLS) point cloud while also considering three test areas: two inside the area defined by GCPs and one outside the area. The results expressed a clear overview of the number of GCPs needed for the indirect georeferencing process with minimum influence on the final results. The RMSE can be reduced down to 50% when switching from four to 20 GCPs, whereas a higher number of GCPs only slightly improves the results.

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