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- Title
Neural Network Radiative Transfer for Imaging Spectroscopy.
- Authors
Bue, Brian D.; Thompson, David R.; Deshpande, Shubhankar; Eastwood, Michael; Green, Robert O.; Mullen, Terry; Natraj, Vijay; Parente, Mario
- Abstract
Visible/Shortwave InfraRed imaging spectroscopy provides valuable remote measurements of Earth's surface and atmospheric properties. These measurements generally rely on inversions of computationally-intensive Radiative Transfer Models (RTMs). RTMs' computational expense makes them difficult to use with high volume imaging spectrometers, and forces approximations such as lookup table interpolation and surface/atmosphere decoupling. These compromises limit the accuracy and flexibility of the remote retrieval; dramatic speed improvements in radiative transfer models could significantly improve the utility and interpretability of remote spectroscopy for Earth science. This study demonstrates that nonparametric function approximation with neural networks can replicate Radiative Transfer calculations over a relevant range of surface/atmosphere parameters. Incorporating physical knowledge into the network structure provides improved interpretability and model efficiency. We evaluate the approach in atmospheric correction of data from the PRISM airborne imaging spectrometer, and demonstrate accurate emulation of radiative transfer calculations which run several orders of magnitude faster than first-principles models. These results are particularly amenable to iterative spectrum fitting approaches, providing analytical benefits including statistically rigorous treatment of uncertainty and the potential to recover information on spectrally-broad signals.
- Subjects
AIRBORNE Visible/Infrared Imaging Spectrometer (AVIRIS); NONPARAMETRIC signal detection; INTERPOLATION; RADIATIVE transfer; REMOTE sensing of the atmosphere; AMBIENCE (Environment)
- Publication
Atmospheric Measurement Techniques Discussions, 2019, p1
- ISSN
1867-8610
- Publication type
Article
- DOI
10.5194/amt-2018-436