SAR image segmentation using level sets and region competition under the GH model

Autores
Buemi, M.E.; Goussies, N.; Jacobo, J.; Mejail, M.
Año de publicación
2009
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Synthetic Aperture Radar (SAR) images are dificult to segment due to their characteristic noise, called speckle, which is multiplicative, non-gaussian and has a low signal to noise ratio. In this work we use the GH distribution to model the SAR data from the different regions of the image. We estimate their statistical parameters and use them in a segmentation algorithm based on multiregion competition. We then apply this algorithm to segment simulated as well as real SAR images and evaluate the accuracy of the segmentation results obtained. © 2009 Springer-Verlag Berlin Heidelberg.
Fil:Buemi, M.E. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.
Fil:Mejail, M. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.
Fuente
Lect. Notes Comput. Sci. 2009;5856 LNCS:153-160
Materia
GHdistribution
Level set
Multiregion competition
SAR images
Segmentation
GHdistribution
Level Set
Multiregion competition
SAR Images
Segmentation
Algorithms
Competition
Computer applications
Computer vision
Imaging systems
Signal to noise ratio
Synthetic aperture radar
Image segmentation
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by/2.5/ar
Repositorio
Biblioteca Digital (UBA-FCEN)
Institución
Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturales
OAI Identificador
paperaa:paper_03029743_v5856LNCS_n_p153_Buemi
Acceder
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oai_identifier_str paperaa:paper_03029743_v5856LNCS_n_p153_Buemi
network_acronym_str BDUBAFCEN
repository_id_str 1896
network_name_str Biblioteca Digital (UBA-FCEN)
spelling SAR image segmentation using level sets and region competition under the GH modelBuemi, M.E.Goussies, N.Jacobo, J.Mejail, M.GHdistributionLevel setMultiregion competitionSAR imagesSegmentationGHdistributionLevel SetMultiregion competitionSAR ImagesSegmentationAlgorithmsCompetitionComputer applicationsComputer visionImaging systemsSignal to noise ratioSynthetic aperture radarImage segmentationSynthetic Aperture Radar (SAR) images are dificult to segment due to their characteristic noise, called speckle, which is multiplicative, non-gaussian and has a low signal to noise ratio. In this work we use the GH distribution to model the SAR data from the different regions of the image. We estimate their statistical parameters and use them in a segmentation algorithm based on multiregion competition. We then apply this algorithm to segment simulated as well as real SAR images and evaluate the accuracy of the segmentation results obtained. © 2009 Springer-Verlag Berlin Heidelberg.Fil:Buemi, M.E. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.Fil:Mejail, M. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.2009info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12110/paper_03029743_v5856LNCS_n_p153_BuemiLect. Notes Comput. Sci. 2009;5856 LNCS:153-160reponame:Biblioteca Digital (UBA-FCEN)instname:Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturalesinstacron:UBA-FCENenginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/2.5/ar2025-09-29T13:43:03Zpaperaa:paper_03029743_v5856LNCS_n_p153_BuemiInstitucionalhttps://digital.bl.fcen.uba.ar/Universidad públicaNo correspondehttps://digital.bl.fcen.uba.ar/cgi-bin/oaiserver.cgiana@bl.fcen.uba.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:18962025-09-29 13:43:04.45Biblioteca Digital (UBA-FCEN) - Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturalesfalse
dc.title.none.fl_str_mv SAR image segmentation using level sets and region competition under the GH model
title SAR image segmentation using level sets and region competition under the GH model
spellingShingle SAR image segmentation using level sets and region competition under the GH model
Buemi, M.E.
GHdistribution
Level set
Multiregion competition
SAR images
Segmentation
GHdistribution
Level Set
Multiregion competition
SAR Images
Segmentation
Algorithms
Competition
Computer applications
Computer vision
Imaging systems
Signal to noise ratio
Synthetic aperture radar
Image segmentation
title_short SAR image segmentation using level sets and region competition under the GH model
title_full SAR image segmentation using level sets and region competition under the GH model
title_fullStr SAR image segmentation using level sets and region competition under the GH model
title_full_unstemmed SAR image segmentation using level sets and region competition under the GH model
title_sort SAR image segmentation using level sets and region competition under the GH model
dc.creator.none.fl_str_mv Buemi, M.E.
Goussies, N.
Jacobo, J.
Mejail, M.
author Buemi, M.E.
author_facet Buemi, M.E.
Goussies, N.
Jacobo, J.
Mejail, M.
author_role author
author2 Goussies, N.
Jacobo, J.
Mejail, M.
author2_role author
author
author
dc.subject.none.fl_str_mv GHdistribution
Level set
Multiregion competition
SAR images
Segmentation
GHdistribution
Level Set
Multiregion competition
SAR Images
Segmentation
Algorithms
Competition
Computer applications
Computer vision
Imaging systems
Signal to noise ratio
Synthetic aperture radar
Image segmentation
topic GHdistribution
Level set
Multiregion competition
SAR images
Segmentation
GHdistribution
Level Set
Multiregion competition
SAR Images
Segmentation
Algorithms
Competition
Computer applications
Computer vision
Imaging systems
Signal to noise ratio
Synthetic aperture radar
Image segmentation
dc.description.none.fl_txt_mv Synthetic Aperture Radar (SAR) images are dificult to segment due to their characteristic noise, called speckle, which is multiplicative, non-gaussian and has a low signal to noise ratio. In this work we use the GH distribution to model the SAR data from the different regions of the image. We estimate their statistical parameters and use them in a segmentation algorithm based on multiregion competition. We then apply this algorithm to segment simulated as well as real SAR images and evaluate the accuracy of the segmentation results obtained. © 2009 Springer-Verlag Berlin Heidelberg.
Fil:Buemi, M.E. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.
Fil:Mejail, M. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.
description Synthetic Aperture Radar (SAR) images are dificult to segment due to their characteristic noise, called speckle, which is multiplicative, non-gaussian and has a low signal to noise ratio. In this work we use the GH distribution to model the SAR data from the different regions of the image. We estimate their statistical parameters and use them in a segmentation algorithm based on multiregion competition. We then apply this algorithm to segment simulated as well as real SAR images and evaluate the accuracy of the segmentation results obtained. © 2009 Springer-Verlag Berlin Heidelberg.
publishDate 2009
dc.date.none.fl_str_mv 2009
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.12110/paper_03029743_v5856LNCS_n_p153_Buemi
url http://hdl.handle.net/20.500.12110/paper_03029743_v5856LNCS_n_p153_Buemi
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/2.5/ar
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/2.5/ar
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv Lect. Notes Comput. Sci. 2009;5856 LNCS:153-160
reponame:Biblioteca Digital (UBA-FCEN)
instname:Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturales
instacron:UBA-FCEN
reponame_str Biblioteca Digital (UBA-FCEN)
collection Biblioteca Digital (UBA-FCEN)
instname_str Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturales
instacron_str UBA-FCEN
institution UBA-FCEN
repository.name.fl_str_mv Biblioteca Digital (UBA-FCEN) - Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturales
repository.mail.fl_str_mv ana@bl.fcen.uba.ar
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score 13.070432

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