Szostak, R.; Zimnoch, M.; Wachniew, P.; Jasek-Kamińska, A. Self-Calibration of UAV Thermal Imagery Using Gradient Descent Algorithm. Drones2023, 7, 683.
Szostak, R.; Zimnoch, M.; Wachniew, P.; Jasek-Kamińska, A. Self-Calibration of UAV Thermal Imagery Using Gradient Descent Algorithm. Drones 2023, 7, 683.
Szostak, R.; Zimnoch, M.; Wachniew, P.; Jasek-Kamińska, A. Self-Calibration of UAV Thermal Imagery Using Gradient Descent Algorithm. Drones2023, 7, 683.
Szostak, R.; Zimnoch, M.; Wachniew, P.; Jasek-Kamińska, A. Self-Calibration of UAV Thermal Imagery Using Gradient Descent Algorithm. Drones 2023, 7, 683.
Abstract
Unmanned aerial vehicles (UAV) thermal imagery offers several advantages in environmental monitoring, as it can provide a low-cost, high-resolution, and flexible solution to measure the temperature of the surface of the land. Limitations related to the maximum load of the drone lead to use of lightweight uncooled thermal cameras whose internal components are not stabilized to a constant temperature. Such cameras suffer from several unwanted effects that contribute to the increase in temperature measurement error from ±0.5 °C in laboratory conditions, to ±5 °C in unstable flight conditions. This article describes a post processing procedure, that reduces the above unwanted effects. It consists of following steps: i) devignetting using single image vignette correction algorithm, ii) georeferencing of images using EXIF data, scale-invariant feature transform (SIFT) stitching, and gradient descent optimisation, and iii) temperature calibration by minimisation of bias between overlapping thermal images using gradient descent optimisation. The solution was tested in several case studies of river areas, where natural water bodies were used as a reference temperature benchmark. In all tests, the precision of the measurements was increased. The root of the mean of the Square of Errors RMSE on average was reduced by 39.0% and Mean of the absolute value of Errors MAE by 40.5%. The proposed algorithm can be called self-calibrating, as in contrast to other known solutions is fully automatic, uses only field data and does not require any calibration equipment or additional operator effort. A Python implementation of the solution is available on GitHub.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
