Methodology for direct reflectance measurement from a drone: system description, radiometric calibration and latest results

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Title: Methodology for direct reflectance measurement from a drone: system description, radiometric calibration and latest results
Author: Markelin, Lauri; Suomalainen, Juha; Hakala, Teemu; Alves de Oliveira, Raquel; Viljanen, Niko; Näsi, Roope; Scott, Barry; Theocharous, Theo; Greenwell, Claire; Fox, Nigel; Honkavaara, Eija
Belongs to series: The International Archives fo the Photogrammetry, Remote Sensing and Spatial Information Sciences
ISSN: 1682-1750
Abstract: We study and analyse performance of a system for direct reflectance measurements from a drone. Key instruments of the system are upwards looking irradiance sensor and downwards looking imaging spectrometer. Requirement for both instruments is that they are radiometrically calibrated, the irradiance sensor has to be horizontally stabilized, and the sensors needs to be accurately synchronized. In our system, irradiance measurements are done with FGI Aerial Image Reference System (FGI AIRS), which uses novel optical levelling methodology and can compensate sensor tilting up to 15°. We performed SI-traceable spectral and radiance calibration of FPI hyperspectral camera at the National Physical Laboratory NPL (Teddington, UK). After the calibration, the radiance accuracy of different channels was between ±4% when evaluated with independent test data. Sensors response to radiance proved to be highly linear and was on average 0.9994 for all channels. The spectral response calibration showed side peaks on several channels that were due to the multiple orders of interference of the FPI and highlighted the importance of accurate calibration. The drone-based direct reflectance measurement system showed promising results with imagery collected over Jokioinen agricultural grass test site, Finland. AIRS-based image- and band wise image adjustment provided homogenous and seamless image mosaics even under varying illumination conditions and under clouds.
URI: http://hdl.handle.net/10138/253847
Date: 2018
Subject: hyperspectral
drones
reflectance
radiometric calibration
irradiance


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