Document Type
Article
Publication Date
8-9-2019
Abstract
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.
Source Publication
Remote Sensing
Recommended Citation
Westing, N., Borghetti, B., & Gross, K. C. (2019). Fast and Effective Techniques for LWIR Radiative Transfer Modeling: A Dimension-Reduction Approach. Remote Sensing, 11(16), 1866. https://doi.org/10.3390/rs11161866
Comments
This is an open access article published by MDPI and distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. CC BY 4.0
Sourced from the published version of record cited below.
This article belongs to the Special Issue of Remote Sensing, "Robust Multispectral/Hyperspectral Image Analysis and Classification"
Author marked [*] was an AFIT graduate student at the time of publication.