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
- Institución
- Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturales
- OAI Identificador
- paperaa:paper_03029743_v7042LNCS_n_p89_Buemi
Ver los metadatos del registro completo
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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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13.070432 |