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, María Elena, Mejail, Marta Estela
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:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03029743_v5856LNCS_n_p153_Buemi
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
id paper:paper_03029743_v5856LNCS_n_p153_Buemi
record_format dspace
spelling paper:paper_03029743_v5856LNCS_n_p153_Buemi2023-06-08T15:28:34Z SAR image segmentation using level sets and region competition under the GH model Buemi, María Elena Mejail, Marta Estela 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 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03029743_v5856LNCS_n_p153_Buemi 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)
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, María Elena
Mejail, Marta Estela
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.
author Buemi, María Elena
Mejail, Marta Estela
author_facet Buemi, María Elena
Mejail, Marta Estela
author_sort Buemi, María Elena
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 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03029743_v5856LNCS_n_p153_Buemi
http://hdl.handle.net/20.500.12110/paper_03029743_v5856LNCS_n_p153_Buemi
work_keys_str_mv AT buemimariaelena sarimagesegmentationusinglevelsetsandregioncompetitionundertheghmodel
AT mejailmartaestela sarimagesegmentationusinglevelsetsandregioncompetitionundertheghmodel
_version_ 1768542645083176960

Ejemplares similares