Simulation of spatially correlated clutter fields

Autores
Bustos, Oscar Humberto; Flesia, Ana Georgina; Frery, Alejandro César; Lucini, María Magdalena
Año de publicación
2009
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Correlated G distributions can be used to describe the clutter seen in images obtained with coherent illumination, as is the case of B-scan ultrasound, laser, sonar, and synthetic aperture radar (SAR) imagery. These distributions are derived using the square root of the generalized inverse Gaussian distribution for the amplitude backscatter within the multiplicative model. A two-parameter particular case of the amplitude G distribution, called, constitutes a modeling improvement with respect to the widespread KA distribution when fitting urban, forested, and deforested areas in remote sensing data. This article deals with the modeling and the simulation of correlated-distributed random fields. It is accomplished by means of the Inverse Transform method, applied to Gaussian random fields with spatial correlation. The main feature of this approach is its generality, since it allows the introduction of negative correlation values in the resulting process, necessary for the proper explanation of the shadowing effect in many SAR images. © 2009 Taylor & Francis Group, LLC.
Fil: Bustos, Oscar Humberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Centro de Investigación y Estudios de Matemática. Universidad Nacional de Córdoba. Centro de Investigación y Estudios de Matemática; Argentina. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina
Fil: Flesia, Ana Georgina. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Centro de Investigación y Estudios de Matemática. Universidad Nacional de Córdoba. Centro de Investigación y Estudios de Matemática; Argentina
Fil: Frery, Alejandro César. Universidade Federal de Alagoas; Brasil
Fil: Lucini, María Magdalena. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste; Argentina. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas y Naturales y Agrimensura; Argentina
Materia
Image Modeling
Simulation
Spatial Correlation
Speckle
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:11336/61272
Acceder
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spelling Simulation of spatially correlated clutter fieldsBustos, Oscar HumbertoFlesia, Ana GeorginaFrery, Alejandro CésarLucini, María MagdalenaImage ModelingSimulationSpatial CorrelationSpecklehttps://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/1Correlated G distributions can be used to describe the clutter seen in images obtained with coherent illumination, as is the case of B-scan ultrasound, laser, sonar, and synthetic aperture radar (SAR) imagery. These distributions are derived using the square root of the generalized inverse Gaussian distribution for the amplitude backscatter within the multiplicative model. A two-parameter particular case of the amplitude G distribution, called, constitutes a modeling improvement with respect to the widespread KA distribution when fitting urban, forested, and deforested areas in remote sensing data. This article deals with the modeling and the simulation of correlated-distributed random fields. It is accomplished by means of the Inverse Transform method, applied to Gaussian random fields with spatial correlation. The main feature of this approach is its generality, since it allows the introduction of negative correlation values in the resulting process, necessary for the proper explanation of the shadowing effect in many SAR images. © 2009 Taylor & Francis Group, LLC.Fil: Bustos, Oscar Humberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Centro de Investigación y Estudios de Matemática. Universidad Nacional de Córdoba. Centro de Investigación y Estudios de Matemática; Argentina. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; ArgentinaFil: Flesia, Ana Georgina. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Centro de Investigación y Estudios de Matemática. Universidad Nacional de Córdoba. Centro de Investigación y Estudios de Matemática; ArgentinaFil: Frery, Alejandro César. Universidade Federal de Alagoas; BrasilFil: Lucini, María Magdalena. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste; Argentina. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas y Naturales y Agrimensura; ArgentinaTaylor2009-11info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/61272Bustos, Oscar Humberto; Flesia, Ana Georgina; Frery, Alejandro César; Lucini, María Magdalena; Simulation of spatially correlated clutter fields; Taylor ; Communications In Statistics-simulation And Computation; 38; 10; 11-2009; 2134-21510361-0918CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1080/03610910903249536info:eu-repo/semantics/altIdentifier/url/https://www.tandfonline.com/doi/full/10.1080/03610910903249536info:eu-repo/semantics/altIdentifier/url/https://arxiv.org/abs/0806.0582info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:37:50Zoai:ri.conicet.gov.ar:11336/61272instacron: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:34982025-09-29 09:37:50.427CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Simulation of spatially correlated clutter fields
title Simulation of spatially correlated clutter fields
spellingShingle Simulation of spatially correlated clutter fields
Bustos, Oscar Humberto
Image Modeling
Simulation
Spatial Correlation
Speckle
title_short Simulation of spatially correlated clutter fields
title_full Simulation of spatially correlated clutter fields
title_fullStr Simulation of spatially correlated clutter fields
title_full_unstemmed Simulation of spatially correlated clutter fields
title_sort Simulation of spatially correlated clutter fields
dc.creator.none.fl_str_mv Bustos, Oscar Humberto
Flesia, Ana Georgina
Frery, Alejandro César
Lucini, María Magdalena
author Bustos, Oscar Humberto
author_facet Bustos, Oscar Humberto
Flesia, Ana Georgina
Frery, Alejandro César
Lucini, María Magdalena
author_role author
author2 Flesia, Ana Georgina
Frery, Alejandro César
Lucini, María Magdalena
author2_role author
author
author
dc.subject.none.fl_str_mv Image Modeling
Simulation
Spatial Correlation
Speckle
topic Image Modeling
Simulation
Spatial Correlation
Speckle
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.1
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Correlated G distributions can be used to describe the clutter seen in images obtained with coherent illumination, as is the case of B-scan ultrasound, laser, sonar, and synthetic aperture radar (SAR) imagery. These distributions are derived using the square root of the generalized inverse Gaussian distribution for the amplitude backscatter within the multiplicative model. A two-parameter particular case of the amplitude G distribution, called, constitutes a modeling improvement with respect to the widespread KA distribution when fitting urban, forested, and deforested areas in remote sensing data. This article deals with the modeling and the simulation of correlated-distributed random fields. It is accomplished by means of the Inverse Transform method, applied to Gaussian random fields with spatial correlation. The main feature of this approach is its generality, since it allows the introduction of negative correlation values in the resulting process, necessary for the proper explanation of the shadowing effect in many SAR images. © 2009 Taylor & Francis Group, LLC.
Fil: Bustos, Oscar Humberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Centro de Investigación y Estudios de Matemática. Universidad Nacional de Córdoba. Centro de Investigación y Estudios de Matemática; Argentina. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina
Fil: Flesia, Ana Georgina. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Centro de Investigación y Estudios de Matemática. Universidad Nacional de Córdoba. Centro de Investigación y Estudios de Matemática; Argentina
Fil: Frery, Alejandro César. Universidade Federal de Alagoas; Brasil
Fil: Lucini, María Magdalena. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste; Argentina. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas y Naturales y Agrimensura; Argentina
description Correlated G distributions can be used to describe the clutter seen in images obtained with coherent illumination, as is the case of B-scan ultrasound, laser, sonar, and synthetic aperture radar (SAR) imagery. These distributions are derived using the square root of the generalized inverse Gaussian distribution for the amplitude backscatter within the multiplicative model. A two-parameter particular case of the amplitude G distribution, called, constitutes a modeling improvement with respect to the widespread KA distribution when fitting urban, forested, and deforested areas in remote sensing data. This article deals with the modeling and the simulation of correlated-distributed random fields. It is accomplished by means of the Inverse Transform method, applied to Gaussian random fields with spatial correlation. The main feature of this approach is its generality, since it allows the introduction of negative correlation values in the resulting process, necessary for the proper explanation of the shadowing effect in many SAR images. © 2009 Taylor & Francis Group, LLC.
publishDate 2009
dc.date.none.fl_str_mv 2009-11
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/11336/61272
Bustos, Oscar Humberto; Flesia, Ana Georgina; Frery, Alejandro César; Lucini, María Magdalena; Simulation of spatially correlated clutter fields; Taylor ; Communications In Statistics-simulation And Computation; 38; 10; 11-2009; 2134-2151
0361-0918
CONICET Digital
CONICET
url http://hdl.handle.net/11336/61272
identifier_str_mv Bustos, Oscar Humberto; Flesia, Ana Georgina; Frery, Alejandro César; Lucini, María Magdalena; Simulation of spatially correlated clutter fields; Taylor ; Communications In Statistics-simulation And Computation; 38; 10; 11-2009; 2134-2151
0361-0918
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1080/03610910903249536
info:eu-repo/semantics/altIdentifier/url/https://www.tandfonline.com/doi/full/10.1080/03610910903249536
info:eu-repo/semantics/altIdentifier/url/https://arxiv.org/abs/0806.0582
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
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Taylor
publisher.none.fl_str_mv Taylor
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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