Assessing the Performance of a Low-Cost Thermal Camera in Proximal and Aerial Conditions

Abstract

The development of low-cost miniaturized thermal cameras has expanded the use of remotely sensed surface temperature and promoted advances in applications involving proximal and aerial data acquisition. However, deriving accurate temperature readings from these cameras is often challenging due to the sensitivity of the sensor, which changes according to the internal temperature. Moreover, the photogrammetry processing required to produce orthomosaics from aerial images can also be problematic and introduce errors to the temperature readings. In this study, we assessed the performance of the FLIR Lepton 3.5 camera in both proximal and aerial conditions based on precision and accuracy indices derived from reference temperature measurements. The aerial analysis was conducted using three flight altitudes replicated along the day, exploring the effect of the distance between the camera and the target, and the blending mode configuration used to create orthomosaics. During the tests, the camera was able to deliver results within the accuracy reported by the manufacturer when using factory calibration, with a root mean square error (RMSE) of 1.08 °C for proximal condition and ≤3.18 °C during aerial missions. Results among different flight altitudes revealed that the overall precision remained stable (R² = 0.94–0.96), contrasting with the accuracy results, decreasing towards higher flight altitudes due to atmospheric attenuation, which is not accounted by factory calibration (RMSE = 2.63–3.18 °C). The blending modes tested also influenced the final accuracy, with the best results obtained with the average (RMSE = 3.14 °C) and disabled mode (RMSE = 3.08 °C). Furthermore, empirical line calibration models using ground reference targets were tested, reducing the errors on temperature measurements by up to 1.83 °C, with a final accuracy better than 2 °C. Other important results include a simplified co-registering method developed to overcome alignment issues encountered during orthomosaic creation using non-geotagged thermal images, and a set of insights and recommendations to reduce errors when deriving temperature readings from aerial thermal imaging.

Mapping the Groundwater Level and Soil Moisture of a Montane Peat Bog Using UAV Monitoring and Machine Learning

“…Concerning the thermal orthomosaics, we created a point shapefile with Tbutton localities with a 1 m buffer centered on the object used to extract the mean DN of the ground object. We performed linear regression between the sampled temperatures and the corresponding DNs [52]. The regression equation was then used to convert the thermal orthomosaics to the relative temperature maps that were used in the raster calculator in Metashape.…”

Overcoming the Challenges of Thermal Infrared Orthomosaics Using a Swath-Based Approach to Correct for Dynamic Temperature and Wind Effects

“…To investigate the influence of blending modes on the orthomosaic generation, the three Agisoft PhotoScan methods were tested and compared by subtracting the vignetting corrected temperature values of one orthomosaic from the other (Figure 3). Agisoft Photo-Scan defines the blending methods as [38]:…”

“…To solve this contrast issue, Turner et al [37] applied a linear stretch using a minimum and maximum thresholds set based on all pixels in all images to improve identification of features within overlapping images. Despite the number of recent improvements to enhance the accuracy of UAV-based thermal data, relatively few evaluations of thermal mosaicking approaches and the impact of using potentially inaccurate/uncorrected thermal images on the final orthomosaic have been undertaken [31,38].…”

“…The "mosaic" method applies a weighted average resulting in higher weights for pixels closer to the image center. Importantly, for thermal data, the underlying physical processes occurring within the imagery are not considered, which hinders the handling of rapid changes in surface temperature and influences of in-flight effects, e.g., wind [38]. Furthermore, commercial software do not compute final error maps associated with inconsistencies of individual thermal images used to produce the final orthomosaic, making uncertainty assessments difficult to complete.…”

Transformations in the Photogrammetric Co-Processing of Thermal Infrared Images and RGB Images

“…In this paper, an experiment examines if the transformation key is dependent on the scene, and how the scene influences the accuracy of the transformation). In the literature, the images are captured in most of the cases from larger distance, oftentimes from UAV, e.g., [ 2 , 3 , 4 , 6 , 7 , 9 , 11 , 12 , 13 ]. However, interest from the authors of this article involves a close-range application.…”