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Remote Sens. 2017, 9(5), 476;

Optimizing the Processing of UAV-Based Thermal Imagery

Laboratory of Hydrology and Water Management (LHWM), Department of Forest and Water Management, Ghent University, Coupure Links 653—Bl. A, BE-9000 Ghent, Belgium
Ecosystem Dynamics Health and Resilience, Climate Change Cluster, University of Technology, Sydney (UTS), 745 Harris Street, Broadway NSW 2007, Australia
Laboratory of Plant Ecology, Department of Applied Ecology and Environmental Biology, Ghent University, Coupure Links 653—Bl. A, BE-9000 Ghent, Belgium
Author to whom correspondence should be addressed.
Received: 3 March 2017 / Revised: 5 May 2017 / Accepted: 9 May 2017 / Published: 12 May 2017
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The current standard procedure for aligning thermal imagery with structure-from-motion (SfM) software uses GPS logger data for the initial image location. As input data, all thermal images of the flight are rescaled to cover the same dynamic scale range, but they are not corrected for changes in meteorological conditions during the flight. This standard procedure can give poor results, particularly in datasets with very low contrast between and within images or when mapping very complex 3D structures. To overcome this, three alignment procedures were introduced and tested: camera pre-calibration, correction of thermal imagery for small changes in air temperature, and improved estimation of the initial image position by making use of the alignment of RGB (visual) images. These improvements were tested and evaluated in an agricultural (low temperature contrast data) and an afforestation (complex 3D structure) dataset. In both datasets, the standard alignment procedure failed to align the images properly, either by resulting in point clouds with several gaps (images that were not aligned) or with unrealistic 3D structure. Using initial thermal camera positions derived from RGB image alignment significantly improved thermal image alignment in all datasets. Air temperature correction had a small yet positive impact on image alignment in the low-contrast agricultural dataset, but a minor effect in the afforestation area. The effect of camera calibration on the alignment was limited in both datasets. Still, in both datasets, the combination of all three procedures significantly improved the alignment, in terms of number of aligned images and of alignment quality. View Full-Text
Keywords: thermal remote sensing; thermography; drone; UAV; structure-from-motion thermal remote sensing; thermography; drone; UAV; structure-from-motion

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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. (CC BY 4.0).

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Maes, W.H.; Huete, A.R.; Steppe, K. Optimizing the Processing of UAV-Based Thermal Imagery. Remote Sens. 2017, 9, 476.

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