Challenges and Best Practices for Deriving Temperature Data from an Uncalibrated UAV Thermal Infrared Camera

TLDR

Miniaturized thermal infrared cameras for UAVs are increasingly available, yet deriving accurate temperature data is difficult because they are highly sensitive to internal temperature changes and low‑cost models are often uncalibrated. The study aimed to evaluate the temperature‑dependency of a non‑radiometric FLIR Vue 640 through laboratory and field experiments and to propose best‑practice sampling guidelines. Researchers conducted controlled lab and in‑flight tests, applying an empirical line calibration with at least three ground reference points, and analyzed how sensor temperature, wind, and drift caused a nonlinear output relationship. The calibration yielded ±0.5 °C accuracy in the lab but degraded to ±5 °C during flight, with the camera’s NUC and vignetting unable to correct the inconsistencies, underscoring the need for the recommended best practices.

Abstract

Miniaturized thermal infrared (TIR) cameras that measure surface temperature are increasingly available for use with unmanned aerial vehicles (UAVs). However, deriving accurate temperature data from these cameras is non-trivialsince they are highly sensitive to changes in their internal temperature and low-cost models are often not radiometrically calibrated. We present the results of laboratory and field experiments that tested the extent of the temperature-dependency of a non-radiometric FLIR Vue Pro 640. We found that a simple empirical line calibration using at least three ground calibration points was sufficient to convert camera digital numbers to temperature values for images captured during UAV flight. Although the camera performed well under stable laboratory conditions (accuracy ±0.5 °C), the accuracy declined to ±5 °C under the changing ambient conditions experienced during UAV flight. The poor performance resulted from the non-linear relationship between camera output and sensor temperature, which was affected by wind and temperature-drift during flight. The camera’s automated non-uniformity correction (NUC) could not sufficiently correct for these effects. Prominent vignetting was also visible in images captured under both stable and changing ambient conditions. The inconsistencies in camera output over time and across the sensor will affect camera applications based on relative temperature differences as well as user-generated radiometric calibration. Based on our findings, we present a set of best practices for UAV TIR camera sampling to minimize the impacts of the temperature dependency of these systems.