Abstract
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.
Highlights
In recent years, the number of possible civil applications and the number of studies using UAVs (Unmanned Aerial Vehicles) or UAS (Unmanned Aerial Systems) has rapidly increased for a number of reasons
It is clear from datasets the use of positions of initial estimates ofimage thermal image positions positions greatly improved improved thermal image alignment, alignment, both in in terms of of thermalof positions greatly improved thermal image alignment, both in terms of terms number estimates thermal image greatly thermal image both of number of aligned alignedand images and in in quality quality of the the alignment alignment
We provide an improved framework for aligning and further processing UAV-based thermal imagery
Summary
The number of possible civil applications and the number of studies using UAVs (Unmanned Aerial Vehicles) or UAS (Unmanned Aerial Systems) has rapidly increased for a number of reasons. UAVs have become more affordable and reliable and their performance (flight execution, flight time, payload, range) has significantly increased. The evolution in UAV development goes hand in hand with the miniaturisation of sensors. Processing software has been developed for UAV data processing of both snapshot imaging and line scanning systems (such as most hyperspectral cameras). Snapshot images are processed with Structure-from-Motion (SfM) photogrammetry [2]. SfM can be seen as an expansion of traditional stereoscopic photogrammetry in which matching features of Remote Sens. SfM can be seen as an expansion of traditional stereoscopic photogrammetry in which matching features of Remote Sens. 2017, 9, 476; doi:10.3390/rs9050476 www.mdpi.com/journal/remotesensing
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