Parameter estimation in SAR imagery using stochastic distances and asymmetric kernels

Autores
Gambini, María Juliana; Cassetti, Julia Analía; Lucini, María Magdalena; Frery, Alejandro César
Año de publicación
2015
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Fil: Gambini, María Juliana. Instituto Tecnológico de Buenos Aires; Argentina.
Fil: Gambini, María Juliana. Universidad Nacional Tres de Febrero; Argentina.
Fil: Cassetti, Julia Analía. Universidad Nacional de General Sarmiento; Argentina.
Fil: Lucini, María Magdalena. Facultad de Ciencias Exactas y Naturales y Agrimensura. Universidad Nacional del Nordeste; Argentina.
Fil: Frery, Alejandro César. Laboratório de Computação Científica e Análise Numérica. Universidade Federal de Alagoas; Brasil.
The Statistical modeling of the data is essential in order to interpret synthetic aperture radar (SAR) images. Speckled data have been described under the multiplicative model using the G family of distributions, which is able to describe rough and extremely rough areas better than the K distribution. The survey article discusses in detail several statistical models for this kind of data. Under the G model, different degrees of roughness are associated with different parameter values; therefore, it is of paramount importance to have high quality estimators. Several works have been devoted to the subject of improving estimation with two main venues of research, namely, analytic and resampling procedures.
Fuente
IEEE Journal of selected topics in applied earth observations and remote sensing, 2015, vol. 8, no. 1, p. 365-375.
Materia
Feature extraction
Image texture analysis
Speckle
Statistics
Synthetic apertura radar
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
Repositorio Institucional de la Universidad Nacional del Nordeste (UNNE)
Institución
Universidad Nacional del Nordeste
OAI Identificador
oai:repositorio.unne.edu.ar:123456789/55296
Acceder
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oai_identifier_str oai:repositorio.unne.edu.ar:123456789/55296
network_acronym_str RIUNNE
repository_id_str 4871
network_name_str Repositorio Institucional de la Universidad Nacional del Nordeste (UNNE)
spelling Parameter estimation in SAR imagery using stochastic distances and asymmetric kernelsGambini, María JulianaCassetti, Julia AnalíaLucini, María MagdalenaFrery, Alejandro CésarFeature extractionImage texture analysisSpeckleStatisticsSynthetic apertura radarFil: Gambini, María Juliana. Instituto Tecnológico de Buenos Aires; Argentina.Fil: Gambini, María Juliana. Universidad Nacional Tres de Febrero; Argentina.Fil: Cassetti, Julia Analía. Universidad Nacional de General Sarmiento; Argentina.Fil: Lucini, María Magdalena. Facultad de Ciencias Exactas y Naturales y Agrimensura. Universidad Nacional del Nordeste; Argentina.Fil: Frery, Alejandro César. Laboratório de Computação Científica e Análise Numérica. Universidade Federal de Alagoas; Brasil.The Statistical modeling of the data is essential in order to interpret synthetic aperture radar (SAR) images. Speckled data have been described under the multiplicative model using the G family of distributions, which is able to describe rough and extremely rough areas better than the K distribution. The survey article discusses in detail several statistical models for this kind of data. Under the G model, different degrees of roughness are associated with different parameter values; therefore, it is of paramount importance to have high quality estimators. Several works have been devoted to the subject of improving estimation with two main venues of research, namely, analytic and resampling procedures.Institute of Electrical and Electronics Engineers Inc.2015-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfp. 365-375application/pdfGambini, María Juliana, et al., 2015. Parameter estimation in SAR imagery using stochastic distances and asymmetric kernels. IEEE Journal of selected topics in applied earth observations and remote sensing. New York: Institute of Electrical and Electronics Engineers Inc., vol. 8, no. 1, p. 365-375. ISSN 1939-1404.1939-1404http://repositorio.unne.edu.ar/handle/123456789/55296IEEE Journal of selected topics in applied earth observations and remote sensing, 2015, vol. 8, no. 1, p. 365-375.reponame:Repositorio Institucional de la Universidad Nacional del Nordeste (UNNE)instname:Universidad Nacional del Nordesteenghttp://dx.doi.org/10.1109/JSTARS.2014.2346017info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-nd/2.5/ar/Atribución-NoComercial-SinDerivadas 2.5 Argentina2025-10-16T10:07:27Zoai:repositorio.unne.edu.ar:123456789/55296instacron:UNNEInstitucionalhttp://repositorio.unne.edu.ar/Universidad públicaNo correspondehttp://repositorio.unne.edu.ar/oaiososa@bib.unne.edu.ar;sergio.alegria@unne.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:48712025-10-16 10:07:27.674Repositorio Institucional de la Universidad Nacional del Nordeste (UNNE) - Universidad Nacional del Nordestefalse
dc.title.none.fl_str_mv Parameter estimation in SAR imagery using stochastic distances and asymmetric kernels
title Parameter estimation in SAR imagery using stochastic distances and asymmetric kernels
spellingShingle Parameter estimation in SAR imagery using stochastic distances and asymmetric kernels
Gambini, María Juliana
Feature extraction
Image texture analysis
Speckle
Statistics
Synthetic apertura radar
title_short Parameter estimation in SAR imagery using stochastic distances and asymmetric kernels
title_full Parameter estimation in SAR imagery using stochastic distances and asymmetric kernels
title_fullStr Parameter estimation in SAR imagery using stochastic distances and asymmetric kernels
title_full_unstemmed Parameter estimation in SAR imagery using stochastic distances and asymmetric kernels
title_sort Parameter estimation in SAR imagery using stochastic distances and asymmetric kernels
dc.creator.none.fl_str_mv Gambini, María Juliana
Cassetti, Julia Analía
Lucini, María Magdalena
Frery, Alejandro César
author Gambini, María Juliana
author_facet Gambini, María Juliana
Cassetti, Julia Analía
Lucini, María Magdalena
Frery, Alejandro César
author_role author
author2 Cassetti, Julia Analía
Lucini, María Magdalena
Frery, Alejandro César
author2_role author
author
author
dc.subject.none.fl_str_mv Feature extraction
Image texture analysis
Speckle
Statistics
Synthetic apertura radar
topic Feature extraction
Image texture analysis
Speckle
Statistics
Synthetic apertura radar
dc.description.none.fl_txt_mv Fil: Gambini, María Juliana. Instituto Tecnológico de Buenos Aires; Argentina.
Fil: Gambini, María Juliana. Universidad Nacional Tres de Febrero; Argentina.
Fil: Cassetti, Julia Analía. Universidad Nacional de General Sarmiento; Argentina.
Fil: Lucini, María Magdalena. Facultad de Ciencias Exactas y Naturales y Agrimensura. Universidad Nacional del Nordeste; Argentina.
Fil: Frery, Alejandro César. Laboratório de Computação Científica e Análise Numérica. Universidade Federal de Alagoas; Brasil.
The Statistical modeling of the data is essential in order to interpret synthetic aperture radar (SAR) images. Speckled data have been described under the multiplicative model using the G family of distributions, which is able to describe rough and extremely rough areas better than the K distribution. The survey article discusses in detail several statistical models for this kind of data. Under the G model, different degrees of roughness are associated with different parameter values; therefore, it is of paramount importance to have high quality estimators. Several works have been devoted to the subject of improving estimation with two main venues of research, namely, analytic and resampling procedures.
description Fil: Gambini, María Juliana. Instituto Tecnológico de Buenos Aires; Argentina.
publishDate 2015
dc.date.none.fl_str_mv 2015-01
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 Gambini, María Juliana, et al., 2015. Parameter estimation in SAR imagery using stochastic distances and asymmetric kernels. IEEE Journal of selected topics in applied earth observations and remote sensing. New York: Institute of Electrical and Electronics Engineers Inc., vol. 8, no. 1, p. 365-375. ISSN 1939-1404.
1939-1404
http://repositorio.unne.edu.ar/handle/123456789/55296
identifier_str_mv Gambini, María Juliana, et al., 2015. Parameter estimation in SAR imagery using stochastic distances and asymmetric kernels. IEEE Journal of selected topics in applied earth observations and remote sensing. New York: Institute of Electrical and Electronics Engineers Inc., vol. 8, no. 1, p. 365-375. ISSN 1939-1404.
1939-1404
url http://repositorio.unne.edu.ar/handle/123456789/55296
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv http://dx.doi.org/10.1109/JSTARS.2014.2346017
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Atribución-NoComercial-SinDerivadas 2.5 Argentina
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Atribución-NoComercial-SinDerivadas 2.5 Argentina
dc.format.none.fl_str_mv application/pdf
p. 365-375
application/pdf
dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers Inc.
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers Inc.
dc.source.none.fl_str_mv IEEE Journal of selected topics in applied earth observations and remote sensing, 2015, vol. 8, no. 1, p. 365-375.
reponame:Repositorio Institucional de la Universidad Nacional del Nordeste (UNNE)
instname:Universidad Nacional del Nordeste
reponame_str Repositorio Institucional de la Universidad Nacional del Nordeste (UNNE)
collection Repositorio Institucional de la Universidad Nacional del Nordeste (UNNE)
instname_str Universidad Nacional del Nordeste
repository.name.fl_str_mv Repositorio Institucional de la Universidad Nacional del Nordeste (UNNE) - Universidad Nacional del Nordeste
repository.mail.fl_str_mv ososa@bib.unne.edu.ar;sergio.alegria@unne.edu.ar
_version_ 1846146006049095680
score 12.712165

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