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Open AccessArticle

Fast and Effective Techniques for LWIR Radiative Transfer Modeling: A Dimension-Reduction Approach

by 1,*,†,‡, 1,‡ and 2,‡
1
Department of Electrical and Computer Engineering, Air Force Institute of Technology, Wright-Patterson AFB, OH 45433, USA
2
Department of Engineering Physics, Air Force Institute of Technology, Wright-Patterson AFB, OH 45433, USA
*
Author to whom correspondence should be addressed.
Current address: Air Force Institute of Technology, Wright-Patterson AFB, OH 45433, USA.
These authors contributed equally to this work.
Remote Sens. 2019, 11(16), 1866; https://doi.org/10.3390/rs11161866
Received: 3 June 2019 / Revised: 31 July 2019 / Accepted: 2 August 2019 / Published: 9 August 2019
(This article belongs to the Special Issue Robust Multispectral/Hyperspectral Image Analysis and Classification)
The increasing spatial and spectral resolution of hyperspectral imagers yields detailed spectroscopy measurements from both space-based and airborne platforms. These detailed measurements allow for material classification, with many recent advancements from the fields of machine learning and deep learning. In many scenarios, the hyperspectral image must first be corrected or compensated for atmospheric effects. Radiative Transfer (RT) computations can provide look up tables (LUTs) to support these corrections. This research investigates a dimension-reduction approach using machine learning methods to create an effective sensor-specific long-wave infrared (LWIR) RT model. The utility of this approach is investigated emulating the Mako LWIR hyperspectral sensor ( Δ λ 0.044   μ m , Δ ν ˜ 3.9 cm 1 ). This study employs physics-based metrics and loss functions to identify promising dimension-reduction techniques and reduce at-sensor radiance reconstruction error. The derived RT model shows an overall root mean square error (RMSE) of less than 1 K across reflective to emissive grey-body emissivity profiles. View Full-Text
Keywords: hyperspectral imagery; machine learning; autoencoders; radiative transfer modeling hyperspectral imagery; machine learning; autoencoders; radiative transfer modeling
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MDPI and ACS Style

Westing, N.; Borghetti, B.; Gross, K.C. Fast and Effective Techniques for LWIR Radiative Transfer Modeling: A Dimension-Reduction Approach. Remote Sens. 2019, 11, 1866.

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