16 July 2018 Quality assessment of radiometric calibration of UAV image mosaics
Author Affiliations +
The use of UAV (unmanned aerial vehicle) based imaging in agriculture adds the ability to incorporate vast amounts of data into analyses designed to improve efficiency in the use of agricultural inputs. One reason this ability has not yet been realized is that producing UAV based radiometrically calibrated images for the purpose of ensuring data reliability is difficult at the large scale. This paper presents an investigation of field-based image-mosaic calibration procedures using a commercial off-the-shelf fixed-wing small UAV and a five-band multispectral sensor. To determine the quality of the radiometric calibration procedure for UAV image mosaics, images were also collected with an identical camera on a manned aircraft, and ground based radiometric calibration tarps were used to produce high-quality calibrated field images. Satellite images were also collected on the same day as the aircraft images in a two-hour flight window centered on solar noon. The manned aircraft and satellite images were large enough for a single image to cover the entire field. The multispectral camera used enables two kinds of exposure settings; auto exposure allows the camera to automatically select exposure and gain settings for each image in a flight, and manual exposure allows the user to select settings preflight which are used for all the images in that flight. In this work we compare the radiometrically calibrated UAV images, collected with both auto-exposure and manual-exposure methods, to the radiometrically calibrated single-frame image generated with the manned aircraft, as well as to a satellite image.
Conference Presentation
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Cody Bagnall, Cody Bagnall, J. Alex Thomasson, J. Alex Thomasson, Chao Sima, Chao Sima, Chenghai Yang, Chenghai Yang, "Quality assessment of radiometric calibration of UAV image mosaics", Proc. SPIE 10664, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping III, 1066404 (16 July 2018); doi: 10.1117/12.2305635; https://doi.org/10.1117/12.2305635

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