Assessment of SAR image filtering using adaptive stack filters

Autores
Buemi, M.E.; Mejail, M.; Jacobo, J.; Frery, A.C.; Ramos, H.S.
Año de publicación
2011
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Stack filters are a special case of non-linear filters. They have a good performance for filtering images with different types of noise while preserving edges and details. A stack filter decomposes an input image into several binary images according to a set of thresholds. Each binary image is then filtered by a Boolean function, which characterizes the filter. Adaptive stack filters can be designed to be optimal; they are computed from a pair of images consisting of an ideal noiseless image and its noisy version. In this work we study the performance of adaptive stack filters when they are applied to Synthetic Aperture Radar (SAR) images. This is done by evaluating the quality of the filtered images through the use of suitable image quality indexes and by measuring the classification accuracy of the resulting images. © 2011 Springer-Verlag.
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.
Fuente
Lect. Notes Comput. Sci. 2011;7042 LNCS:89-96
Materia
Non-linear filters
SAR image filtering
speckle noise
stack filters
Classification accuracy
Filtered images
Input image
Noiseless images
Noisy versions
Nonlinear filter
Quality indices
SAR Images
speckle noise
Stack filters
Synthetic aperture radar images
Binary images
Boolean functions
Computer vision
Image quality
Nonlinear filtering
Synthetic aperture radar
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by/2.5/ar
Repositorio
Biblioteca Digital (UBA-FCEN)
Institución
Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturales
OAI Identificador
paperaa:paper_03029743_v7042LNCS_n_p89_Buemi

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oai_identifier_str paperaa:paper_03029743_v7042LNCS_n_p89_Buemi
network_acronym_str BDUBAFCEN
repository_id_str 1896
network_name_str Biblioteca Digital (UBA-FCEN)
spelling Assessment of SAR image filtering using adaptive stack filtersBuemi, M.E.Mejail, M.Jacobo, J.Frery, A.C.Ramos, H.S.Non-linear filtersSAR image filteringspeckle noisestack filtersClassification accuracyFiltered imagesInput imageNoiseless imagesNoisy versionsNonlinear filterQuality indicesSAR Imagesspeckle noiseStack filtersSynthetic aperture radar imagesBinary imagesBoolean functionsComputer visionImage qualityNonlinear filteringSynthetic aperture radarStack filters are a special case of non-linear filters. They have a good performance for filtering images with different types of noise while preserving edges and details. A stack filter decomposes an input image into several binary images according to a set of thresholds. Each binary image is then filtered by a Boolean function, which characterizes the filter. Adaptive stack filters can be designed to be optimal; they are computed from a pair of images consisting of an ideal noiseless image and its noisy version. In this work we study the performance of adaptive stack filters when they are applied to Synthetic Aperture Radar (SAR) images. This is done by evaluating the quality of the filtered images through the use of suitable image quality indexes and by measuring the classification accuracy of the resulting images. © 2011 Springer-Verlag.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.2011info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12110/paper_03029743_v7042LNCS_n_p89_BuemiLect. Notes Comput. Sci. 2011;7042 LNCS:89-96reponame:Biblioteca Digital (UBA-FCEN)instname:Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturalesinstacron:UBA-FCENenginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/2.5/ar2025-09-29T13:43:09Zpaperaa:paper_03029743_v7042LNCS_n_p89_BuemiInstitucionalhttps://digital.bl.fcen.uba.ar/Universidad públicaNo correspondehttps://digital.bl.fcen.uba.ar/cgi-bin/oaiserver.cgiana@bl.fcen.uba.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:18962025-09-29 13:43:10.573Biblioteca Digital (UBA-FCEN) - Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturalesfalse
dc.title.none.fl_str_mv Assessment of SAR image filtering using adaptive stack filters
title Assessment of SAR image filtering using adaptive stack filters
spellingShingle Assessment of SAR image filtering using adaptive stack filters
Buemi, M.E.
Non-linear filters
SAR image filtering
speckle noise
stack filters
Classification accuracy
Filtered images
Input image
Noiseless images
Noisy versions
Nonlinear filter
Quality indices
SAR Images
speckle noise
Stack filters
Synthetic aperture radar images
Binary images
Boolean functions
Computer vision
Image quality
Nonlinear filtering
Synthetic aperture radar
title_short Assessment of SAR image filtering using adaptive stack filters
title_full Assessment of SAR image filtering using adaptive stack filters
title_fullStr Assessment of SAR image filtering using adaptive stack filters
title_full_unstemmed Assessment of SAR image filtering using adaptive stack filters
title_sort Assessment of SAR image filtering using adaptive stack filters
dc.creator.none.fl_str_mv Buemi, M.E.
Mejail, M.
Jacobo, J.
Frery, A.C.
Ramos, H.S.
author Buemi, M.E.
author_facet Buemi, M.E.
Mejail, M.
Jacobo, J.
Frery, A.C.
Ramos, H.S.
author_role author
author2 Mejail, M.
Jacobo, J.
Frery, A.C.
Ramos, H.S.
author2_role author
author
author
author
dc.subject.none.fl_str_mv Non-linear filters
SAR image filtering
speckle noise
stack filters
Classification accuracy
Filtered images
Input image
Noiseless images
Noisy versions
Nonlinear filter
Quality indices
SAR Images
speckle noise
Stack filters
Synthetic aperture radar images
Binary images
Boolean functions
Computer vision
Image quality
Nonlinear filtering
Synthetic aperture radar
topic Non-linear filters
SAR image filtering
speckle noise
stack filters
Classification accuracy
Filtered images
Input image
Noiseless images
Noisy versions
Nonlinear filter
Quality indices
SAR Images
speckle noise
Stack filters
Synthetic aperture radar images
Binary images
Boolean functions
Computer vision
Image quality
Nonlinear filtering
Synthetic aperture radar
dc.description.none.fl_txt_mv Stack filters are a special case of non-linear filters. They have a good performance for filtering images with different types of noise while preserving edges and details. A stack filter decomposes an input image into several binary images according to a set of thresholds. Each binary image is then filtered by a Boolean function, which characterizes the filter. Adaptive stack filters can be designed to be optimal; they are computed from a pair of images consisting of an ideal noiseless image and its noisy version. In this work we study the performance of adaptive stack filters when they are applied to Synthetic Aperture Radar (SAR) images. This is done by evaluating the quality of the filtered images through the use of suitable image quality indexes and by measuring the classification accuracy of the resulting images. © 2011 Springer-Verlag.
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.
description Stack filters are a special case of non-linear filters. They have a good performance for filtering images with different types of noise while preserving edges and details. A stack filter decomposes an input image into several binary images according to a set of thresholds. Each binary image is then filtered by a Boolean function, which characterizes the filter. Adaptive stack filters can be designed to be optimal; they are computed from a pair of images consisting of an ideal noiseless image and its noisy version. In this work we study the performance of adaptive stack filters when they are applied to Synthetic Aperture Radar (SAR) images. This is done by evaluating the quality of the filtered images through the use of suitable image quality indexes and by measuring the classification accuracy of the resulting images. © 2011 Springer-Verlag.
publishDate 2011
dc.date.none.fl_str_mv 2011
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/20.500.12110/paper_03029743_v7042LNCS_n_p89_Buemi
url http://hdl.handle.net/20.500.12110/paper_03029743_v7042LNCS_n_p89_Buemi
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/2.5/ar
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/2.5/ar
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv Lect. Notes Comput. Sci. 2011;7042 LNCS:89-96
reponame:Biblioteca Digital (UBA-FCEN)
instname:Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturales
instacron:UBA-FCEN
reponame_str Biblioteca Digital (UBA-FCEN)
collection Biblioteca Digital (UBA-FCEN)
instname_str Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturales
instacron_str UBA-FCEN
institution UBA-FCEN
repository.name.fl_str_mv Biblioteca Digital (UBA-FCEN) - Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturales
repository.mail.fl_str_mv ana@bl.fcen.uba.ar
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