Low power compressive sensing for hyperspectral imagery

Bibliographic Details
Main Author: Nascimento, Jose
Publication Date: 2019
Other Authors: Véstias, Mário
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10400.21/11219
Summary: Hyperspectral imaging instruments allow remote Earth exploration by measuring hundreds of spectral bands at very narrow channels of a given spatial area. The resulting hyperspectral data cube typically comprises several gigabytes. Such extremely large volumes of data introduces problems in its transmission to Earth due to limited communication bandwidth. As a result, the applicability of data compression techniques to hyperspectral images have received increasing attention. This paper, presents a study of the power and time consumption of a parallel implementation for a spectral compressive acquisition method on a Jetson TX2 platform. The conducted experiments have been performed to demonstrate the applicability of these methods for onboard processing. The results show that by using this low energy consumption GPU and integer data type is it possible to obtain real-time performance with a very limited power requirement while maintaining the methods accuracy.
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spelling Low power compressive sensing for hyperspectral imageryHyperspectral imageryCompressive sensingEmbedded systemsHyperspectral imaging instruments allow remote Earth exploration by measuring hundreds of spectral bands at very narrow channels of a given spatial area. The resulting hyperspectral data cube typically comprises several gigabytes. Such extremely large volumes of data introduces problems in its transmission to Earth due to limited communication bandwidth. As a result, the applicability of data compression techniques to hyperspectral images have received increasing attention. This paper, presents a study of the power and time consumption of a parallel implementation for a spectral compressive acquisition method on a Jetson TX2 platform. The conducted experiments have been performed to demonstrate the applicability of these methods for onboard processing. The results show that by using this low energy consumption GPU and integer data type is it possible to obtain real-time performance with a very limited power requirement while maintaining the methods accuracy.RCIPLNascimento, JoseVéstias, Mário2020-03-11T09:40:22Z2019-062019-06-01T00:00:00Zconference objectinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10400.21/11219enginfo:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2025-02-12T08:10:59Zoai:repositorio.ipl.pt:10400.21/11219Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T19:54:08.912450Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Low power compressive sensing for hyperspectral imagery
title Low power compressive sensing for hyperspectral imagery
spellingShingle Low power compressive sensing for hyperspectral imagery
Nascimento, Jose
Hyperspectral imagery
Compressive sensing
Embedded systems
title_short Low power compressive sensing for hyperspectral imagery
title_full Low power compressive sensing for hyperspectral imagery
title_fullStr Low power compressive sensing for hyperspectral imagery
title_full_unstemmed Low power compressive sensing for hyperspectral imagery
title_sort Low power compressive sensing for hyperspectral imagery
author Nascimento, Jose
author_facet Nascimento, Jose
Véstias, Mário
author_role author
author2 Véstias, Mário
author2_role author
dc.contributor.none.fl_str_mv RCIPL
dc.contributor.author.fl_str_mv Nascimento, Jose
Véstias, Mário
dc.subject.por.fl_str_mv Hyperspectral imagery
Compressive sensing
Embedded systems
topic Hyperspectral imagery
Compressive sensing
Embedded systems
description Hyperspectral imaging instruments allow remote Earth exploration by measuring hundreds of spectral bands at very narrow channels of a given spatial area. The resulting hyperspectral data cube typically comprises several gigabytes. Such extremely large volumes of data introduces problems in its transmission to Earth due to limited communication bandwidth. As a result, the applicability of data compression techniques to hyperspectral images have received increasing attention. This paper, presents a study of the power and time consumption of a parallel implementation for a spectral compressive acquisition method on a Jetson TX2 platform. The conducted experiments have been performed to demonstrate the applicability of these methods for onboard processing. The results show that by using this low energy consumption GPU and integer data type is it possible to obtain real-time performance with a very limited power requirement while maintaining the methods accuracy.
publishDate 2019
dc.date.none.fl_str_mv 2019-06
2019-06-01T00:00:00Z
2020-03-11T09:40:22Z
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dc.language.iso.fl_str_mv eng
language eng
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