Evaluation of Test Field-based Calibration and Self-calibration Models of UAV Integrated Compact Cameras

Unmanned aerial vehicles (UAVs), which have made a name for themselves in photogrammetry studies in recent years, provide users with integrated camera systems. Identifying interior orientation parameters, such as focal coordinates, focal length and distortions, is an essential requirement for camera systems used for photogrammetric purposes. This process, which is called camera calibration, is offered automatically by software from the library. Another important known calibration method is self-calibration. Calibrating cameras by creating 2D or 3D test areas is a troublesome and grueling option. However, it is the most commonly accepted way in terms of accuracy. In this study, images were taken in different test areas (2D and 3D) to perform the calibrations of the cameras integrated on two different UAVs, namely DJI Phantom 4 Pro and Parrot Anafi. The calibration parameters determined from the images taken were compared with the calibration parameters obtained by the self-calibration method, and block adjustment was performed with ground control points marked in the study area. In order to perform performance analysis, the root-mean-square error (RMSE) was determined from the control points. In conclusion, it was determined that the results of both the calibrations obtained with the test fields and those obtained with self-calibration were acceptable.