Polarimetric SAR Image Segmentation using CEM Algorithm

Autores
Fernández Michelli, Juan Ignacio; Hurtado, Martin; Areta, Javier Alberto; Muravchik, Carlos Horacio
Año de publicación
2014
Idioma
español castellano
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In this work we perform Synthetic Aperture Radar (SAR) polarimetric images segmentation based on the Classification-Expectation-Maximization (CEM) method, with both supervised and unsupervised initialization. In the former case, the algorithm is randomly initialized with the number of classes as the only initial information, while in the unsupervised case initialization is based on a previous classification. Real EMISAR Single-Look-Complex (SLC) data are used, with Mixing Gaussian model. Results are compared with those obtained by Wishart unsupervised classification method, which is a well-known and widely used method for radar image classification. Finally, Da-vies-Bouldin index is applied for quantitative comparison be-tween the obtained segmentations, and for studying the CEM method performance.
Fil: Fernández Michelli, Juan Ignacio. Universidad Nacional de la Plata. Facultad de Ingeniería. Departamento de Electrotecnia; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Hurtado, Martin. Universidad Nacional de la Plata. Facultad de Ingeniería. Departamento de Electrotecnia; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Areta, Javier Alberto. Universidad Nacional de Rio Negro. Sede Andina. Departamento de Ciencias Exactas, Naturales y de Ingenieria; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Muravchik, Carlos Horacio. Universidad Nacional de la Plata. Facultad de Ingeniería. Departamento de Electrotecnia; Argentina
Materia
CEM
CLASSIFICATION
EXPECTATION MAXIMIZATION
SAR
SEGMENTATION
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/35841
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Polarimetric SAR Image Segmentation using CEM AlgorithmFernández Michelli, Juan IgnacioHurtado, MartinAreta, Javier AlbertoMuravchik, Carlos HoracioCEMCLASSIFICATIONEXPECTATION MAXIMIZATIONSARSEGMENTATIONhttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2In this work we perform Synthetic Aperture Radar (SAR) polarimetric images segmentation based on the Classification-Expectation-Maximization (CEM) method, with both supervised and unsupervised initialization. In the former case, the algorithm is randomly initialized with the number of classes as the only initial information, while in the unsupervised case initialization is based on a previous classification. Real EMISAR Single-Look-Complex (SLC) data are used, with Mixing Gaussian model. Results are compared with those obtained by Wishart unsupervised classification method, which is a well-known and widely used method for radar image classification. Finally, Da-vies-Bouldin index is applied for quantitative comparison be-tween the obtained segmentations, and for studying the CEM method performance.Fil: Fernández Michelli, Juan Ignacio. Universidad Nacional de la Plata. Facultad de Ingeniería. Departamento de Electrotecnia; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Hurtado, Martin. Universidad Nacional de la Plata. Facultad de Ingeniería. Departamento de Electrotecnia; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Areta, Javier Alberto. Universidad Nacional de Rio Negro. Sede Andina. Departamento de Ciencias Exactas, Naturales y de Ingenieria; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Muravchik, Carlos Horacio. Universidad Nacional de la Plata. Facultad de Ingeniería. Departamento de Electrotecnia; ArgentinaInstitute of Electrical and Electronics Engineers2014-08info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/35841Fernández Michelli, Juan Ignacio; Hurtado, Martin; Areta, Javier Alberto; Muravchik, Carlos Horacio; Polarimetric SAR Image Segmentation using CEM Algorithm; Institute of Electrical and Electronics Engineers; IEEE Latin America Transactions; 12; 5; 8-2014; 910-9141548-0992CONICET DigitalCONICETspainfo:eu-repo/semantics/altIdentifier/doi/10.1109/TLA.2014.6872905info:eu-repo/semantics/altIdentifier/url/http://ieeexplore.ieee.org/document/6872905/info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-10T13:18:28Zoai:ri.conicet.gov.ar:11336/35841instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-10 13:18:29.059CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Polarimetric SAR Image Segmentation using CEM Algorithm
title Polarimetric SAR Image Segmentation using CEM Algorithm
spellingShingle Polarimetric SAR Image Segmentation using CEM Algorithm
Fernández Michelli, Juan Ignacio
CEM
CLASSIFICATION
EXPECTATION MAXIMIZATION
SAR
SEGMENTATION
title_short Polarimetric SAR Image Segmentation using CEM Algorithm
title_full Polarimetric SAR Image Segmentation using CEM Algorithm
title_fullStr Polarimetric SAR Image Segmentation using CEM Algorithm
title_full_unstemmed Polarimetric SAR Image Segmentation using CEM Algorithm
title_sort Polarimetric SAR Image Segmentation using CEM Algorithm
dc.creator.none.fl_str_mv Fernández Michelli, Juan Ignacio
Hurtado, Martin
Areta, Javier Alberto
Muravchik, Carlos Horacio
author Fernández Michelli, Juan Ignacio
author_facet Fernández Michelli, Juan Ignacio
Hurtado, Martin
Areta, Javier Alberto
Muravchik, Carlos Horacio
author_role author
author2 Hurtado, Martin
Areta, Javier Alberto
Muravchik, Carlos Horacio
author2_role author
author
author
dc.subject.none.fl_str_mv CEM
CLASSIFICATION
EXPECTATION MAXIMIZATION
SAR
SEGMENTATION
topic CEM
CLASSIFICATION
EXPECTATION MAXIMIZATION
SAR
SEGMENTATION
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv In this work we perform Synthetic Aperture Radar (SAR) polarimetric images segmentation based on the Classification-Expectation-Maximization (CEM) method, with both supervised and unsupervised initialization. In the former case, the algorithm is randomly initialized with the number of classes as the only initial information, while in the unsupervised case initialization is based on a previous classification. Real EMISAR Single-Look-Complex (SLC) data are used, with Mixing Gaussian model. Results are compared with those obtained by Wishart unsupervised classification method, which is a well-known and widely used method for radar image classification. Finally, Da-vies-Bouldin index is applied for quantitative comparison be-tween the obtained segmentations, and for studying the CEM method performance.
Fil: Fernández Michelli, Juan Ignacio. Universidad Nacional de la Plata. Facultad de Ingeniería. Departamento de Electrotecnia; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Hurtado, Martin. Universidad Nacional de la Plata. Facultad de Ingeniería. Departamento de Electrotecnia; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Areta, Javier Alberto. Universidad Nacional de Rio Negro. Sede Andina. Departamento de Ciencias Exactas, Naturales y de Ingenieria; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Muravchik, Carlos Horacio. Universidad Nacional de la Plata. Facultad de Ingeniería. Departamento de Electrotecnia; Argentina
description In this work we perform Synthetic Aperture Radar (SAR) polarimetric images segmentation based on the Classification-Expectation-Maximization (CEM) method, with both supervised and unsupervised initialization. In the former case, the algorithm is randomly initialized with the number of classes as the only initial information, while in the unsupervised case initialization is based on a previous classification. Real EMISAR Single-Look-Complex (SLC) data are used, with Mixing Gaussian model. Results are compared with those obtained by Wishart unsupervised classification method, which is a well-known and widely used method for radar image classification. Finally, Da-vies-Bouldin index is applied for quantitative comparison be-tween the obtained segmentations, and for studying the CEM method performance.
publishDate 2014
dc.date.none.fl_str_mv 2014-08
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/11336/35841
Fernández Michelli, Juan Ignacio; Hurtado, Martin; Areta, Javier Alberto; Muravchik, Carlos Horacio; Polarimetric SAR Image Segmentation using CEM Algorithm; Institute of Electrical and Electronics Engineers; IEEE Latin America Transactions; 12; 5; 8-2014; 910-914
1548-0992
CONICET Digital
CONICET
url http://hdl.handle.net/11336/35841
identifier_str_mv Fernández Michelli, Juan Ignacio; Hurtado, Martin; Areta, Javier Alberto; Muravchik, Carlos Horacio; Polarimetric SAR Image Segmentation using CEM Algorithm; Institute of Electrical and Electronics Engineers; IEEE Latin America Transactions; 12; 5; 8-2014; 910-914
1548-0992
CONICET Digital
CONICET
dc.language.none.fl_str_mv spa
language spa
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1109/TLA.2014.6872905
info:eu-repo/semantics/altIdentifier/url/http://ieeexplore.ieee.org/document/6872905/
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 12.993085

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