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A Review of Dimensionality Reduction Techniques for Processing Hyper-Spectral Optical Signal. L&E 27 (3) 2019

Light & Engineering 27 (3)

Volume 27
Date of publication 08/06/2019
Pages 85-98

PDF

A Review of Dimensionality Reduction Techniques for Processing Hyper-Spectral Optical Signal. L&E 27 (3) 2019
Articles authors:
Ana del Águila, Dmitry S. Efremenko, Thomas Trautmann

Ana del Águila graduated in Physics from the Granada University (UGR) in 2015. From 2016–2018 she worked as an early-stage researcher at the National Institute for Aerospace Technology (INTA) in Spain. At present, she is doing the Ph.D. in the German Aerospace Centre (DLR) with a DAAD/DLR scholarship. Her scientific interests are in-situ atmospheric aerosols, lidar systems, remote sensing, radiate transfer and Big Data analysis

Dmitry S. Efremenko received his Ph. D. degree from the Moscow State University in 2011 and the Habilitation degree from MPEI in 2017. Since 2011 he works as a research scientist at the German Aerospace Centre (DLR). He received the Elsevier/JQSRT Goody Award in 2017. In 2020, he obtained a title of Privatdozent from the Technical University of Munich. His scientific interests include radiative transfer, numerical methods and machine learning. He has over 70 scientific papers

Thomas Trautmann, Prof., Dr., graduated with a diploma in meteorology at the Johannes Gutenberg University at Mainz (JGU) in 1985. From the JGU he received a Ph.D. degree in 1989, a habilitation degree in 1997, and in 2003 he was appointed as applicant Prof. at the University of Leipzig. Since 2003 he works as head of department Atmospheric Processors at the German Aerospace Center (DLR). His scientific interests include radiate transfer, electromagnetic scattering and atmospheric remote sensing

Abstract:
Hyper-spectral sensors take measurements in the narrow contiguous bands across the electromagnetic spectrum. Usually, the goal is to detect a certain object or a component of the medium with unique spectral signatures. In particular, the hyper-spectral measurements are used in atmospheric remote sensing to detect trace gases. To improve the efficiency of hyper-spectral processing algorithms, data reduction methods are applied. This paper outlines the dimensionality reduction techniques in the context of hyper-spectral remote sensing of the atmosphere. The dimensionality reduction excludes redundant information from the data and currently is the integral part of high-performance radiation transfer models. In this survey, it is shown how the principal component analysis can be applied for spectral radiance modelling and retrieval of atmospheric constituents, thereby speeding up the data processing by orders of magnitude. The discussed techniques are generic and can be readily applied for solving atmospheric as well as material science problems.
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