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Article

Challenges in UAS-Based TIR Imagery Processing: Image Alignment and Uncertainty Quantification

Geoinformation for Environmental Planning Lab, Technische Universität Berlin, Straße des 17. Juni 145, 10623 Berlin, Germany
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Remote Sens. 2020, 12(10), 1552; https://doi.org/10.3390/rs12101552
Received: 3 April 2020 / Revised: 10 May 2020 / Accepted: 11 May 2020 / Published: 13 May 2020
(This article belongs to the Section Remote Sensing Image Processing)
Thermal infrared measurements acquired with unmanned aerial systems (UAS) allow for high spatial resolution and flexibility in the time of image acquisition to assess ground surface temperature. Nevertheless, thermal infrared cameras mounted on UAS suffer from low radiometric accuracy as well as low image resolution and contrast hampering image alignment. Our analysis aims to determine the impact of the sun elevation angle (SEA), weather conditions, land cover, image contrast enhancement, geometric camera calibration, and inclusion of yaw angle information and generic and reference pre-selection methods on the point cloud and number of aligned images generated by Agisoft Metashape. We, therefore, use a total amount of 56 single data sets acquired on different days, times of day, weather conditions, and land cover types. Furthermore, we assess camera noise and the effect of temperature correction based on air temperature using features extracted by structure from motion. The study shows for the first time generalizable implications on thermal infrared image acquisitions and presents an approach to perform the analysis with a quality measure of inter-image sensor noise. Better image alignment is reached for conditions of high contrast such as clear weather conditions and high SEA. Alignment can be improved by applying a contrast enhancement and choosing both, reference and generic pre-selection. Grassland areas are best alignable, followed by cropland and forests. Geometric camera calibration hampers feature detection and matching. Temperature correction shows no effect on radiometric camera uncertainty. Based on a valid statistical analysis of the acquired data sets, we derive general suggestions for the planning of a successful field campaign as well as recommendations for a suitable preprocessing workflow. View Full-Text
Keywords: image alignment; orthomosaic; camera uncertainty quantification; image contrast enhancement; land cover; sun elevation angle image alignment; orthomosaic; camera uncertainty quantification; image contrast enhancement; land cover; sun elevation angle
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MDPI and ACS Style

Döpper, V.; Gränzig, T.; Kleinschmit, B.; Förster, M. Challenges in UAS-Based TIR Imagery Processing: Image Alignment and Uncertainty Quantification. Remote Sens. 2020, 12, 1552. https://doi.org/10.3390/rs12101552

AMA Style

Döpper V, Gränzig T, Kleinschmit B, Förster M. Challenges in UAS-Based TIR Imagery Processing: Image Alignment and Uncertainty Quantification. Remote Sensing. 2020; 12(10):1552. https://doi.org/10.3390/rs12101552

Chicago/Turabian Style

Döpper, Veronika, Tobias Gränzig, Birgit Kleinschmit, and Michael Förster. 2020. "Challenges in UAS-Based TIR Imagery Processing: Image Alignment and Uncertainty Quantification" Remote Sensing 12, no. 10: 1552. https://doi.org/10.3390/rs12101552

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