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Proceedings Paper

A low-cost method for collecting hyperspectral measurements from a small unmanned aircraft system
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Paper Abstract

Small unmanned aircraft systems (UAS) are a relatively new tool for collecting remote sensing data at dense spatial and temporal resolutions. This study aimed to develop a spectral measurement platform for deployment on a UAS for quantifying and delineating moisture zones within an agricultural landscape. A series of portable spectrometers covering ultraviolet (UV), visible (VIS), and near-infrared (NIR) wavelengths were instrumented using a Raspberry Pi embedded computer that was programmed to interface with the UAS autopilot for autonomous data acquisition. A second set of identical spectrometers were fitted with calibrated irradiance lenses to capture ambient light during data acquisition. Data were collected during the 2017 Great American Eclipse while observing a reflectance target to determine the ability to compensate for ambient light conditions. A calibration routine was developed that scaled raw reflectance data by sensor integration time and ambient light energy. The resulting calibrated reflectance exhibited a consistent spectral profile and average intensity across a wide range of ambient light conditions. Results indicated the potential for mitigating the effect of ambient light when passively measuring reflectance on a portable spectral measurement system. Future work will use multiple reflectance targets to test the ability to classify targets based on spectral signatures under a wide range of ambient light conditions.

Paper Details

Date Published: 21 May 2018
PDF: 15 pages
Proc. SPIE 10664, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping III, 106640H (21 May 2018); doi: 10.1117/12.2305934
Show Author Affiliations
Ali Hamidisepehr, Univ. of Kentucky (United States)
Michael P. Sama, Univ. of Kentucky (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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