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Remote Sens. 2012, 4(9), 2736-2752; doi:10.3390/rs4092736

Radiometric and Geometric Analysis of Hyperspectral Imagery Acquired from an Unmanned Aerial Vehicle

1
Idaho National Laboratory, 2525 North Fremont Ave, Idaho Falls, ID 83415, USA
2
Boise Center Aerospace Laboratory, Idaho State University, 322 E. Front St, Suite 240, Boise, ID 83702, USA
*
Author to whom correspondence should be addressed.
Received: 20 July 2012 / Revised: 6 September 2012 / Accepted: 10 September 2012 / Published: 17 September 2012
(This article belongs to the Special Issue Unmanned Aerial Vehicles (UAVs) based Remote Sensing)
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Abstract

In the summer of 2010, an Unmanned Aerial Vehicle (UAV) hyperspectral calibration and characterization experiment of the Resonon PIKA II imaging spectrometer was conducted at the US Department of Energy’s Idaho National Laboratory (INL) UAV Research Park. The purpose of the experiment was to validate the radiometric calibration of the spectrometer and determine the georegistration accuracy achievable from the on-board global positioning system (GPS) and inertial navigation sensors (INS) under operational conditions. In order for low-cost hyperspectral systems to compete with larger systems flown on manned aircraft, they must be able to collect data suitable for quantitative scientific analysis. The results of the in-flight calibration experiment indicate an absolute average agreement of 96.3%, 93.7% and 85.7% for calibration tarps of 56%, 24%, and 2.5% reflectivity, respectively. The achieved planimetric accuracy was 4.6 m (based on RMSE) with a flying height of 344 m above ground level (AGL). View Full-Text
Keywords: hyperspectral; radiometric calibration; geometric correction; UAV; imaging spectrometer hyperspectral; radiometric calibration; geometric correction; UAV; imaging spectrometer
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Hruska, R.; Mitchell, J.; Anderson, M.; Glenn, N.F. Radiometric and Geometric Analysis of Hyperspectral Imagery Acquired from an Unmanned Aerial Vehicle. Remote Sens. 2012, 4, 2736-2752.

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