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@ARTICLE{Aasen:830014,
author = {Aasen, Helge and Burkart, Andreas and Bolten, Andreas and
Bareth, Georg},
title = {{G}enerating 3{D} hyperspectral information with
lightweight {UAV} snapshot cameras for vegetation
monitoring: {F}rom camera calibration to quality assurance},
journal = {ISPRS journal of photogrammetry and remote sensing},
volume = {108},
issn = {0924-2716},
address = {Amsterdam [u.a.]},
publisher = {Elsevier},
reportid = {FZJ-2017-03616},
pages = {245 - 259},
year = {2015},
abstract = {This paper describes a novel method to derive 3D
hyperspectral information from lightweight snapshot cameras
for unmanned aerial vehicles for vegetation monitoring.
Snapshot cameras record an image cube with one spectral and
two spatial dimensions with every exposure. First, we
describe and apply methods to radiometrically characterize
and calibrate these cameras. Then, we introduce our
processing chain to derive 3D hyperspectral information from
the calibrated image cubes based on structure from motion.
The approach includes a novel way for quality assurance of
the data which is used to assess the quality of the
hyperspectral data for every single pixel in the final data
product. The result is a hyperspectral digital surface model
as a representation of the surface in 3D space linked with
the hyperspectral information emitted and reflected by the
objects covered by the surface. In this study we use the
hyperspectral camera Cubert UHD 185-Firefly, which collects
125 bands from 450 to 950 nm. The obtained data product has
a spatial resolution of approximately 1 cm for the spatial
and 21 cm for the hyperspectral information. The radiometric
calibration yields good results with less than $1\%$ offset
in reflectance compared to an ASD FieldSpec 3 for most of
the spectral range. The quality assurance information shows
that the radiometric precision is better than $0.13\%$ for
the derived data product. We apply the approach to data from
a flight campaign in a barley experiment with different
varieties during the growth stage heading (BBCH 52 – 59)
to demonstrate the feasibility for vegetation monitoring in
the context of precision agriculture. The plant parameters
retrieved from the data product correspond to in-field
measurements of a single date field campaign for plant
height (R2 = 0.7), chlorophyll (BGI2, R2 = 0.52), LAI (RDVI,
R2 = 0.32) and biomass (RDVI, R2 = 0.29). Our approach can
also be applied for other image-frame cameras as long as the
individual bands of the image cube are spatially
co-registered beforehand.},
cin = {IBG-2},
ddc = {550},
cid = {I:(DE-Juel1)IBG-2-20101118},
pnm = {582 - Plant Science (POF3-582)},
pid = {G:(DE-HGF)POF3-582},
typ = {PUB:(DE-HGF)16},
UT = {WOS:000363075300020},
doi = {10.1016/j.isprsjprs.2015.08.002},
url = {https://juser.fz-juelich.de/record/830014},
}