Competitive Segmentation Performance on Near-lossless and Lossy Compressed Remote Sensing Images

Image segmentation lies at the heart of multiple image processing chains, and achieving accurate segmentation is of utmost importance as it impacts later processing. Image segmentation has recently gained interest in the field of remote sensing, mostly due to the widespread availability of remote se...

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Detalles Bibliográficos
Autores: García-Sobrino, Joaquín|||0000-0003-3808-7132, Pinho, Armando J., Serra-Sagristà, Joan|||0000-0003-4729-9292
Tipo de recurso: artículo
Fecha de publicación:2019
País:España
Institución:Universitat Autònoma de Barcelona
Repositorio:Dipòsit Digital de Documents de la UAB
Idioma:inglés
OAI Identifier:oai:ddd.uab.cat:215774
Acceso en línea:https://ddd.uab.cat/record/215774
https://dx.doi.org/urn:doi:10.1109/LGRS.2019.2934997
Access Level:acceso abierto
Palabra clave:Remote sensing data
Image segmentation
Lossy compression
Near-lossless compression
Maximum likelihood
Successive band merging
JPEG 2000
JPEG-LS
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network_acronym_str ES
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spelling Competitive Segmentation Performance on Near-lossless and Lossy Compressed Remote Sensing ImagesGarcía-Sobrino, Joaquín|||0000-0003-3808-7132Pinho, Armando J.Serra-Sagristà, Joan|||0000-0003-4729-9292Remote sensing dataImage segmentationLossy compressionNear-lossless compressionMaximum likelihoodSuccessive band mergingJPEG 2000JPEG-LSImage segmentation lies at the heart of multiple image processing chains, and achieving accurate segmentation is of utmost importance as it impacts later processing. Image segmentation has recently gained interest in the field of remote sensing, mostly due to the widespread availability of remote sensing data. This increased availability poses the problem of transmitting and storing large volumes of data. Compression is a common strategy to alleviate this problem. However, lossy or near-lossless compression prevents a perfect reconstruction of the recovered data. This letter investigates the image segmentation performance in data reconstructed after a near-lossless or a lossy compression. Two image segmentation algorithms and two compression standards are evaluated on data from sev- eral instruments. Experimental results reveal that segmentation performance over previously near-lossless and lossy compressed images is not markedly reduced at low and moderate compression ratios. In some scenarios, accurate segmentation performance can be achieved even for high compression ratios. 22019-01-0120192019-01-01Articlehttp://purl.org/coar/resource_type/c_6501AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/articleapplication/pdfhttps://ddd.uab.cat/record/215774https://dx.doi.org/urn:doi:10.1109/LGRS.2019.2934997reponame:Dipòsit Digital de Documents de la UABinstname:Universitat Autònoma de BarcelonaInglésengAgencia Estatal de Investigación https://doi.org/10.13039/501100011033 RTI2018-095287-B-I00Agència de Gestió d'Ajuts Universitaris i de Recerca https://doi.org/10.13039/501100003030 2017/SGR-463open accesshttp://purl.org/coar/access_right/c_abf2Aquest material està protegit per drets d'autor i/o drets afins. Podeu utilitzar aquest material en funció del que permet la legislació de drets d'autor i drets afins d'aplicació al vostre cas. Per a d'altres usos heu d'obtenir permís del(s) titular(s) de drets.https://rightsstatements.org/vocab/InC/1.0/info:eu-repo/semantics/openAccessoai:ddd.uab.cat:2157742026-06-06T12:50:31Z
dc.title.none.fl_str_mv Competitive Segmentation Performance on Near-lossless and Lossy Compressed Remote Sensing Images
title Competitive Segmentation Performance on Near-lossless and Lossy Compressed Remote Sensing Images
spellingShingle Competitive Segmentation Performance on Near-lossless and Lossy Compressed Remote Sensing Images
García-Sobrino, Joaquín|||0000-0003-3808-7132
Remote sensing data
Image segmentation
Lossy compression
Near-lossless compression
Maximum likelihood
Successive band merging
JPEG 2000
JPEG-LS
title_short Competitive Segmentation Performance on Near-lossless and Lossy Compressed Remote Sensing Images
title_full Competitive Segmentation Performance on Near-lossless and Lossy Compressed Remote Sensing Images
title_fullStr Competitive Segmentation Performance on Near-lossless and Lossy Compressed Remote Sensing Images
title_full_unstemmed Competitive Segmentation Performance on Near-lossless and Lossy Compressed Remote Sensing Images
title_sort Competitive Segmentation Performance on Near-lossless and Lossy Compressed Remote Sensing Images
dc.creator.none.fl_str_mv García-Sobrino, Joaquín|||0000-0003-3808-7132
Pinho, Armando J.
Serra-Sagristà, Joan|||0000-0003-4729-9292
author García-Sobrino, Joaquín|||0000-0003-3808-7132
author_facet García-Sobrino, Joaquín|||0000-0003-3808-7132
Pinho, Armando J.
Serra-Sagristà, Joan|||0000-0003-4729-9292
author_role author
author2 Pinho, Armando J.
Serra-Sagristà, Joan|||0000-0003-4729-9292
author2_role author
author
dc.subject.none.fl_str_mv Remote sensing data
Image segmentation
Lossy compression
Near-lossless compression
Maximum likelihood
Successive band merging
JPEG 2000
JPEG-LS
topic Remote sensing data
Image segmentation
Lossy compression
Near-lossless compression
Maximum likelihood
Successive band merging
JPEG 2000
JPEG-LS
description Image segmentation lies at the heart of multiple image processing chains, and achieving accurate segmentation is of utmost importance as it impacts later processing. Image segmentation has recently gained interest in the field of remote sensing, mostly due to the widespread availability of remote sensing data. This increased availability poses the problem of transmitting and storing large volumes of data. Compression is a common strategy to alleviate this problem. However, lossy or near-lossless compression prevents a perfect reconstruction of the recovered data. This letter investigates the image segmentation performance in data reconstructed after a near-lossless or a lossy compression. Two image segmentation algorithms and two compression standards are evaluated on data from sev- eral instruments. Experimental results reveal that segmentation performance over previously near-lossless and lossy compressed images is not markedly reduced at low and moderate compression ratios. In some scenarios, accurate segmentation performance can be achieved even for high compression ratios.
publishDate 2019
dc.date.none.fl_str_mv 2
2019-01-01
2019
2019-01-01
dc.type.none.fl_str_mv Article
http://purl.org/coar/resource_type/c_6501
AM
http://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://ddd.uab.cat/record/215774
https://dx.doi.org/urn:doi:10.1109/LGRS.2019.2934997
url https://ddd.uab.cat/record/215774
https://dx.doi.org/urn:doi:10.1109/LGRS.2019.2934997
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Agencia Estatal de Investigación https://doi.org/10.13039/501100011033 RTI2018-095287-B-I00
Agència de Gestió d'Ajuts Universitaris i de Recerca https://doi.org/10.13039/501100003030 2017/SGR-463
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
https://rightsstatements.org/vocab/InC/1.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
https://rightsstatements.org/vocab/InC/1.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Dipòsit Digital de Documents de la UAB
instname:Universitat Autònoma de Barcelona
instname_str Universitat Autònoma de Barcelona
reponame_str Dipòsit Digital de Documents de la UAB
collection Dipòsit Digital de Documents de la UAB
repository.name.fl_str_mv
repository.mail.fl_str_mv
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