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A review of dimensionality reduction techniques for processing hyper-spectral optical signal

del Águila, Ana and Efremenko, Dmitry and Trautmann, Thomas (2019) A review of dimensionality reduction techniques for processing hyper-spectral optical signal. Light and Engineering, 27 (3), pp. 85-98. Znack Publishing House. DOI: 10.33383/2019-017 ISSN 0236-2945

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Official URL: https://l-e-journal.com/en/journals/light-engineering-27-3/a-review-of-dimensionality-reduction-techniques-for-processing-hyper-spectral-optical-signal/

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.

Item URL in elib:https://elib.dlr.de/128817/
Document Type:Article
Title:A review of dimensionality reduction techniques for processing hyper-spectral optical signal
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
del Águila, AnaAna.delAguilaPerez (at) dlr.deUNSPECIFIED
Efremenko, Dmitrydmitry.efremenko (at) dlr.deUNSPECIFIED
Trautmann, ThomasThomas.Trautmann (at) dlr.deUNSPECIFIED
Date:2019
Journal or Publication Title:Light and Engineering
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:27
DOI :10.33383/2019-017
Page Range:pp. 85-98
Publisher:Znack Publishing House
ISSN:0236-2945
Status:Published
Keywords:passive remote sensing, hyper-spectral data, principal component analysis, full-physics machine learning, trace gas retrieval
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):Vorhaben Spectroscopic Methods in Remote Sensing, Vorhaben Optical Remote Sensing
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Atmospheric Processors
Deposited By: Efremenko, Dr Dmitry
Deposited On:22 Aug 2019 13:18
Last Modified:22 Aug 2019 13:18

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