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

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 estima...

Descripción completa

Detalles Bibliográficos
Autores principales: Buemi, M.E., Goussies, N., Jacobo, J., Mejail, M.
Formato: Artículo publishedVersion
Lenguaje:Inglés
Publicado: 2009
Materias:
GHdistribution
Level set
Multiregion competition
SAR images
Segmentation
Level Set
SAR Images
Algorithms
Competition
Computer applications
Computer vision
Imaging systems
Signal to noise ratio
Synthetic aperture radar
Image segmentation
Acceso en línea:http://hdl.handle.net/20.500.12110/paper_03029743_v5856LNCS_n_p153_Buemi
Aporte de:Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires Ver origen
id paperaa:paper_03029743_v5856LNCS_n_p153_Buemi
record_format dspace
spelling paperaa:paper_03029743_v5856LNCS_n_p153_Buemi2023-02-10T13:34:27Z SAR image segmentation using level sets and region competition under the GH model Lect. Notes Comput. Sci. 2009;5856 LNCS:153-160 Buemi, M.E. Goussies, N. Jacobo, J. Mejail, M. 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 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. 2009 info:eu-repo/semantics/article info:ar-repo/semantics/artículo info:eu-repo/semantics/publishedVersion application/pdf eng info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_03029743_v5856LNCS_n_p153_Buemi
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
language Inglés
orig_language_str_mv eng
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
spellingShingle 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
Buemi, M.E.
Goussies, N.
Jacobo, J.
Mejail, M.
SAR image segmentation using level sets and region competition under the GH model
topic_facet 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
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.
format Artículo
Artículo
publishedVersion
author Buemi, M.E.
Goussies, N.
Jacobo, J.
Mejail, M.
author_facet Buemi, M.E.
Goussies, N.
Jacobo, J.
Mejail, M.
author_sort Buemi, M.E.
title SAR image segmentation using level sets and region competition under the GH model
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
publishDate 2009
url http://hdl.handle.net/20.500.12110/paper_03029743_v5856LNCS_n_p153_Buemi
work_keys_str_mv AT buemime sarimagesegmentationusinglevelsetsandregioncompetitionundertheghmodel
AT goussiesn sarimagesegmentationusinglevelsetsandregioncompetitionundertheghmodel
AT jacoboj sarimagesegmentationusinglevelsetsandregioncompetitionundertheghmodel
AT mejailm sarimagesegmentationusinglevelsetsandregioncompetitionundertheghmodel
_version_ 1759062937941573632

Ejemplares similares