Abstract
Miniaturized hyperspectral imaging sensors are becoming
available to small unmanned airborne vehicle (UAV)
platforms. Imaging concepts based on frame format offer
an attractive alternative to conventional hyperspectral
pushbroom scanners because they enable enhanced
processing and interpretation potential by allowing for
acquisition of the 3-D geometry of the object and
multiple object views together with the hyperspectral
reflectance signatures. The objective of this
investigation was to study the performance of novel
visible and near-infrared (VNIR) and short-wave infrared
(SWIR) hyperspectral frame cameras based on a tunable
Fabry-Pérot interferometer (FPI) in measuring a 3-D
digital surface model and the surface moisture of a peat
production area. UAV image blocks were captured with
ground sample distances (GSDs) of 15, 9.5, and 2.5 cm
with the SWIR, VNIR, and consumer RGB cameras,
respectively. Georeferencing showed consistent behavior,
with accuracy levels better than GSD for the FPI cameras.
The best accuracy in moisture estimation was obtained
when using the reflectance difference of the SWIR band at
1246 nm and of the VNIR band at 859 nm, which gave a root
mean square error (rmse) of 5.21 pp (pp is the mass
fraction in percentage points) and a normalized rmse of
7.61%. The results are encouraging, indicating that
UAV-based remote sensing could significantly improve the
efficiency and environmental safety aspects of peat
production.
Original language | English |
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Pages (from-to) | 5440-5454 |
Journal | IEEE Transactions on Geoscience and Remote Sensing |
Volume | 54 |
Issue number | 9 |
DOIs | |
Publication status | Published - 2016 |
MoE publication type | A1 Journal article-refereed |
Keywords
- calibration
- geographic information system
- geometry
- image classification
- radiometry
- remote sensing
- remotely piloted aircraft
- spectroscopy
- stereo vision
- OtaNano