Extracting Atmospheric Profiles from Hyperspectral Data Using Particle Filters

Extracting Atmospheric Profiles from Hyperspectral Data Using Particle Filters

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Removing the effects of the atmosphere from remote sensing data requires accurate knowledge of the physical properties of the atmosphere during the time of measurement. There is a nonlinear relationship that maps atmospheric composition to emitted spectra, but it cannot be easily inverted. The time...

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Main Author: Rawlings, Dustin
Format: Others
Published: DigitalCommons@USU 2013
Subjects:
Atmosphere
Hyperspectral
Particle Filter
Electrical and Computer Engineering
Online Access:http://digitalcommons.usu.edu/etd/1533
http://digitalcommons.usu.edu/cgi/viewcontent.cgi?article=2515&context=etd
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spelling ndltd-UTAHS-oai-http---digitalcommons.usu.edu-do-oai--etd-25152013-05-15T03:56:40Z Extracting Atmospheric Profiles from Hyperspectral Data Using Particle Filters Rawlings, Dustin Removing the effects of the atmosphere from remote sensing data requires accurate knowledge of the physical properties of the atmosphere during the time of measurement. There is a nonlinear relationship that maps atmospheric composition to emitted spectra, but it cannot be easily inverted. The time evolution of atmospheric composition is approximately Markovian, and can be estimated using hyperspectral measurements of the atmosphere with particle filters. The difficulties associated with particle filtering high-dimension data can be mitigated by incorporating future measurement data with the proposal density. 2013-05-01T07:00:00Z text application/pdf http://digitalcommons.usu.edu/etd/1533 http://digitalcommons.usu.edu/cgi/viewcontent.cgi?article=2515&context=etd Copyright for this work is held by the author. Transmission or reproduction of materials protected by copyright beyond that allowed by fair use requires the written permission of the copyright owners. Works not in the public domain cannot be commercially exploited without permission of the copyright owner. Responsibility for any use rests exclusively with the user. For more information contact Andrew Wesolek (andrew.wesolek@usu.edu). All Graduate Theses and Dissertations DigitalCommons@USU Atmosphere Hyperspectral Particle Filter Electrical and Computer Engineering
collection NDLTD
format Others
sources NDLTD
topic Atmosphere
Hyperspectral
Particle Filter
Electrical and Computer Engineering
spellingShingle Atmosphere
Hyperspectral
Particle Filter
Electrical and Computer Engineering
Rawlings, Dustin
Extracting Atmospheric Profiles from Hyperspectral Data Using Particle Filters
description Removing the effects of the atmosphere from remote sensing data requires accurate knowledge of the physical properties of the atmosphere during the time of measurement. There is a nonlinear relationship that maps atmospheric composition to emitted spectra, but it cannot be easily inverted. The time evolution of atmospheric composition is approximately Markovian, and can be estimated using hyperspectral measurements of the atmosphere with particle filters. The difficulties associated with particle filtering high-dimension data can be mitigated by incorporating future measurement data with the proposal density.
author Rawlings, Dustin
author_facet Rawlings, Dustin
author_sort Rawlings, Dustin
title Extracting Atmospheric Profiles from Hyperspectral Data Using Particle Filters
title_short Extracting Atmospheric Profiles from Hyperspectral Data Using Particle Filters
title_full Extracting Atmospheric Profiles from Hyperspectral Data Using Particle Filters
title_fullStr Extracting Atmospheric Profiles from Hyperspectral Data Using Particle Filters
title_full_unstemmed Extracting Atmospheric Profiles from Hyperspectral Data Using Particle Filters
title_sort extracting atmospheric profiles from hyperspectral data using particle filters
publisher DigitalCommons@USU
publishDate 2013
url http://digitalcommons.usu.edu/etd/1533
http://digitalcommons.usu.edu/cgi/viewcontent.cgi?article=2515&context=etd
work_keys_str_mv AT rawlingsdustin extractingatmosphericprofilesfromhyperspectraldatausingparticlefilters
_version_ 1716585848587157504

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