High-performance lossless compression of hyperspectral remote sensing scenes based on spectral decorrelation

This article belongs to the Special Issue Remote Sensing Data Compression.

Detalles Bibliográficos
Autores: Hernández-Cabronero, Miguel, Portell, Jordi, Blanes, Ian, Serra-Sagristà, Joan
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2020
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/237000
Acceso en línea:http://hdl.handle.net/10261/237000
Access Level:acceso abierto
Palabra clave:Multispectral
Transform
Hyperspectral
Data compression
CCSDS
FAPEC
id ES_910f83033ef3559961e38bf64ff9f62b
oai_identifier_str oai:digital.csic.es:10261/237000
network_acronym_str ES
network_name_str España
repository_id_str
spelling High-performance lossless compression of hyperspectral remote sensing scenes based on spectral decorrelationHernández-Cabronero, MiguelPortell, JordiBlanes, IanSerra-Sagristà, JoanMultispectralTransformHyperspectralData compressionCCSDSFAPECThis article belongs to the Special Issue Remote Sensing Data Compression.The capacity of the downlink channel is a major bottleneck for applications based on remote sensing hyperspectral imagery (HSI). Data compression is an essential tool to maximize the amount of HSI scenes that can be retrieved on the ground. At the same time, energy and hardware constraints of spaceborne devices impose limitations on the complexity of practical compression algorithms. To avoid any distortion in the analysis of the HSI data, only lossless compression is considered in this study. This work aims at finding the most advantageous compression–complexity trade-off within the state of the art in HSI compression. To do so, a novel comparison of the most competitive spectral decorrelation approaches combined with the best performing low-complexity compressors of the state is presented. Compression performance and execution time results are obtained for a set of 47 HSI scenes produced by 14 different sensors in real remote sensing missions. Assuming only a limited amount of energy is available, obtained data suggest that the FAPEC algorithm yields the best trade-off. When compared to the CCSDS 123.0-B-2 standard, FAPEC is 5.0 times faster and its compressed data rates are on average within 16% of the CCSDS standard. In scenarios where energy constraints can be relaxed, CCSDS 123.0-B-2 yields the best average compression results of all evaluated methods.This research was funded by the Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund under grants RTI2018-095287-B-I00, TIN2015-71126-R, RTI2018-095076-B-C21 (MINECO/FEDER, UE); by the ESA Business Incubation Programme and Barcelona Activa; by the Catalan Government under grant 2017SGR-463; by the postdoctoral fellowship programme Beatriu de Pinós, reference 2018-BP-00008, funded by the Secretary of Universities and Research (Government of Catalonia); and by the Horizon 2020 programme of research and innovation of the European Union under the Marie Skłodowska-Curie grant agreement #801370.Peer reviewedMultidisciplinary Digital Publishing InstituteMinisterio de Ciencia, Innovación y Universidades (España)Agencia Estatal de Investigación (España)European Space AgencyGeneralitat de CatalunyaMinisterio de Economía y Competitividad (España)European CommissionConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202120212020info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/237000reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-095287-B-I00RTI2018-095287-B-I00/AEI/10.13039/501100011033info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2015-71126-Rinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-095076-B-C21RTI2018-095076-B-C21/AEI/10.13039/501100011033info:eu-repo/grantAgreement/EC/H2020/801370https://doi.org/10.3390/rs12182955Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/2370002026-05-22T06:33:51Z
dc.title.none.fl_str_mv High-performance lossless compression of hyperspectral remote sensing scenes based on spectral decorrelation
title High-performance lossless compression of hyperspectral remote sensing scenes based on spectral decorrelation
spellingShingle High-performance lossless compression of hyperspectral remote sensing scenes based on spectral decorrelation
Hernández-Cabronero, Miguel
Multispectral
Transform
Hyperspectral
Data compression
CCSDS
FAPEC
title_short High-performance lossless compression of hyperspectral remote sensing scenes based on spectral decorrelation
title_full High-performance lossless compression of hyperspectral remote sensing scenes based on spectral decorrelation
title_fullStr High-performance lossless compression of hyperspectral remote sensing scenes based on spectral decorrelation
title_full_unstemmed High-performance lossless compression of hyperspectral remote sensing scenes based on spectral decorrelation
title_sort High-performance lossless compression of hyperspectral remote sensing scenes based on spectral decorrelation
dc.creator.none.fl_str_mv Hernández-Cabronero, Miguel
Portell, Jordi
Blanes, Ian
Serra-Sagristà, Joan
author Hernández-Cabronero, Miguel
author_facet Hernández-Cabronero, Miguel
Portell, Jordi
Blanes, Ian
Serra-Sagristà, Joan
author_role author
author2 Portell, Jordi
Blanes, Ian
Serra-Sagristà, Joan
author2_role author
author
author
dc.contributor.none.fl_str_mv Ministerio de Ciencia, Innovación y Universidades (España)
Agencia Estatal de Investigación (España)
European Space Agency
Generalitat de Catalunya
Ministerio de Economía y Competitividad (España)
European Commission
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Multispectral
Transform
Hyperspectral
Data compression
CCSDS
FAPEC
topic Multispectral
Transform
Hyperspectral
Data compression
CCSDS
FAPEC
description This article belongs to the Special Issue Remote Sensing Data Compression.
publishDate 2020
dc.date.none.fl_str_mv 2020
2021
2021
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/237000
url http://hdl.handle.net/10261/237000
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-095287-B-I00
RTI2018-095287-B-I00/AEI/10.13039/501100011033
info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2015-71126-R
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-095076-B-C21
RTI2018-095076-B-C21/AEI/10.13039/501100011033
info:eu-repo/grantAgreement/EC/H2020/801370
https://doi.org/10.3390/rs12182955
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute
publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869413359903834112
score 15,81155

Similares en LA Referencia