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Proceedings Paper

An initial exploration of vicarious and in-scene calibration techniques for small unmanned aircraft systems
Author(s): Baabak G. Mamaghani; Geoffrey V. Sasaki; Ryan J. Connal; Kevin Kha; Jackson S. Knappen; Ryan A. Hartzell; Evan D. Marcellus; Timothy D. Bauch; Nina G. Raqueño; Carl Salvaggio
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Paper Abstract

The use of small unmanned aircraft systems (sUAS) for applications in the field of precision agriculture has demonstrated the need to produce temporally consistent imagery to allow for quantitative comparisons. In order for these aerial images to be used to identify actual changes on the ground, conversion of raw digital count to reflectance, or to an atmospherically normalized space, needs to be carried out. This paper will describe an experiment that compares the use of reflectance calibration panels, for use with the empirical line method (ELM), against a newly proposed ratio of the target radiance and the downwelling radiance, to predict the reflectance of known targets in the scene. We propose that the use of an on-board downwelling light sensor (DLS) may provide the sUAS remote sensing practitioner with an approach that does not require the expensive and time consuming task of placing known reflectance standards in the scene. Three calibration methods were tested in this study: 2-Point ELM, 1-Point ELM, and At-altitude Radiance Ratio (AARR). Our study indicates that the traditional 2-Point ELM produces the lowest mean error in band effective reflectance factor, 0.0165. The 1-Point ELM and AARR produce mean errors of 0.0343 and 0.0287 respectively. A modeling of the proposed AARR approach indicates that the technique has the potential to perform better than the 2-Point ELM method, with a 0.0026 mean error in band effective reflectance factor, indicating that this newly proposed technique may prove to be a viable alternative with suitable on-board sensors.

Paper Details

Date Published: 21 May 2018
PDF: 19 pages
Proc. SPIE 10664, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping III, 1066406 (21 May 2018); doi: 10.1117/12.2302744
Show Author Affiliations
Baabak G. Mamaghani, Rochester Institute of Technology (United States)
Geoffrey V. Sasaki, Rochester Institute of Technology (United States)
Ryan J. Connal, Rochester Institute of Technology (United States)
Kevin Kha, Rochester Institute of Technology (United States)
Jackson S. Knappen, Rochester Institute of Technology (United States)
Ryan A. Hartzell, Rochester Institute of Technology (United States)
Evan D. Marcellus, Rochester Institute of Technology (United States)
Timothy D. Bauch, Rochester Institute of Technology (United States)
Nina G. Raqueño, Rochester Institute of Technology (United States)
Carl Salvaggio, Rochester Institute of Technology (United States)


Published in SPIE Proceedings Vol. 10664:
Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping III
J. Alex Thomasson; Mac McKee; Robert J. Moorhead, Editor(s)

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