Optimal canny’s parameters regressions for coastal line detection in satellite-based SAR images

Autores
Nemer Pelliza, Karim Alejandra; Pucheta, Martín Alejo; Flesia, Ana Georgina
Año de publicación
2020
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Canny’s algorithm is a very well-known and widely implemented multistage edge detector. The extraction of coastal lines in space-borne-based synthetic aperture radar (SAR) images using this algorithm is particularly complicated because of the multiplicative speckle noise present in them and can only be used if Canny’s parameters (CaPP) are chosen appropriately. This letter introduces a methodology for computing functional forms for the CaPP, using functions of the image characteristics through a system that combines artificial neural networks (ANN) with statistical regression. A set of CaPP functional forms is obtained by applying this method on synthetic SAR images. Pratt’s fig- ure of merit (PFoM) is used to measure the performance of them, obtaining more than 0.75, on average, in the 14 400 synthetic SAR images analyzed. Finally, this set of formulas has been tested for extracting coastal edges from real polynyas SAR images, acquired from Sentinel-1.
Fil: Nemer Pelliza, Karim Alejandra. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Centro de Investigación en Informatica para la Ingeniería. Córdoba; Argentina.
Fil: Pucheta, Martín Alejo. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Centro de Investigación en Informatica para la Ingeniería. Córdoba; Argentina.
Fil: Flesia, Ana Georgina. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Centro de Investigación en Informatica para la Ingeniería. Córdoba; Argentina.
Peer Reviewed
Materia
Artificial neural networks (ANNs)
edge detection
statistical analysis
synthetic aperture radar (SAR) images
Nivel de accesibilidad
acceso abierto
Condiciones de uso
2024-04-11T21:11:08Z
Repositorio
Repositorio Institucional Abierto (UTN)
Institución
Universidad Tecnológica Nacional
OAI Identificador
oai:ria.utn.edu.ar:20.500.12272/10467
Acceder
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network_acronym_str RIAUTN
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network_name_str Repositorio Institucional Abierto (UTN)
spelling Optimal canny’s parameters regressions for coastal line detection in satellite-based SAR imagesNemer Pelliza, Karim AlejandraPucheta, Martín AlejoFlesia, Ana GeorginaArtificial neural networks (ANNs)edge detectionstatistical analysissynthetic aperture radar (SAR) imagesCanny’s algorithm is a very well-known and widely implemented multistage edge detector. The extraction of coastal lines in space-borne-based synthetic aperture radar (SAR) images using this algorithm is particularly complicated because of the multiplicative speckle noise present in them and can only be used if Canny’s parameters (CaPP) are chosen appropriately. This letter introduces a methodology for computing functional forms for the CaPP, using functions of the image characteristics through a system that combines artificial neural networks (ANN) with statistical regression. A set of CaPP functional forms is obtained by applying this method on synthetic SAR images. Pratt’s fig- ure of merit (PFoM) is used to measure the performance of them, obtaining more than 0.75, on average, in the 14 400 synthetic SAR images analyzed. Finally, this set of formulas has been tested for extracting coastal edges from real polynyas SAR images, acquired from Sentinel-1.Fil: Nemer Pelliza, Karim Alejandra. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Centro de Investigación en Informatica para la Ingeniería. Córdoba; Argentina.Fil: Pucheta, Martín Alejo. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Centro de Investigación en Informatica para la Ingeniería. Córdoba; Argentina.Fil: Flesia, Ana Georgina. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Centro de Investigación en Informatica para la Ingeniería. Córdoba; Argentina.Peer Reviewed2024-04-11T21:11:08Z2024-04-11T21:11:08Z2020info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articulopdfapplication/pdfhttp://hdl.handle.net/20.500.12272/10467-engenginfo:eu-repo/semantics/openAccess2024-04-11T21:11:08Zhttp://creativecommons.org/licenses/by-nc-nd/4.0/Attribution-NonCommercial-NoDerivatives 4.0 InternacionalNemer Pelliza, Karim Alejandrahttps://creativecommons.org/licenses/by-nc-sa/4.0/reponame:Repositorio Institucional Abierto (UTN)instname:Universidad Tecnológica Nacional2026-09-24T12:45:29Zoai:ria.utn.edu.ar:20.500.12272/10467instacron:UTNInstitucionalhttp://ria.utn.edu.ar/Universidad públicaNo correspondehttp://ria.utn.edu.ar/oaigestionria@rec.utn.edu.ar; fsuarez@rec.utn.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:a2026-09-24 12:45:30.818Repositorio Institucional Abierto (UTN) - Universidad Tecnológica Nacionalfalse
dc.title.none.fl_str_mv Optimal canny’s parameters regressions for coastal line detection in satellite-based SAR images
title Optimal canny’s parameters regressions for coastal line detection in satellite-based SAR images
spellingShingle Optimal canny’s parameters regressions for coastal line detection in satellite-based SAR images
Nemer Pelliza, Karim Alejandra
Artificial neural networks (ANNs)
edge detection
statistical analysis
synthetic aperture radar (SAR) images
title_short Optimal canny’s parameters regressions for coastal line detection in satellite-based SAR images
title_full Optimal canny’s parameters regressions for coastal line detection in satellite-based SAR images
title_fullStr Optimal canny’s parameters regressions for coastal line detection in satellite-based SAR images
title_full_unstemmed Optimal canny’s parameters regressions for coastal line detection in satellite-based SAR images
title_sort Optimal canny’s parameters regressions for coastal line detection in satellite-based SAR images
dc.creator.none.fl_str_mv Nemer Pelliza, Karim Alejandra
Pucheta, Martín Alejo
Flesia, Ana Georgina
author Nemer Pelliza, Karim Alejandra
author_facet Nemer Pelliza, Karim Alejandra
Pucheta, Martín Alejo
Flesia, Ana Georgina
author_role author
author2 Pucheta, Martín Alejo
Flesia, Ana Georgina
author2_role author
author
dc.subject.none.fl_str_mv Artificial neural networks (ANNs)
edge detection
statistical analysis
synthetic aperture radar (SAR) images
topic Artificial neural networks (ANNs)
edge detection
statistical analysis
synthetic aperture radar (SAR) images
dc.description.none.fl_txt_mv Canny’s algorithm is a very well-known and widely implemented multistage edge detector. The extraction of coastal lines in space-borne-based synthetic aperture radar (SAR) images using this algorithm is particularly complicated because of the multiplicative speckle noise present in them and can only be used if Canny’s parameters (CaPP) are chosen appropriately. This letter introduces a methodology for computing functional forms for the CaPP, using functions of the image characteristics through a system that combines artificial neural networks (ANN) with statistical regression. A set of CaPP functional forms is obtained by applying this method on synthetic SAR images. Pratt’s fig- ure of merit (PFoM) is used to measure the performance of them, obtaining more than 0.75, on average, in the 14 400 synthetic SAR images analyzed. Finally, this set of formulas has been tested for extracting coastal edges from real polynyas SAR images, acquired from Sentinel-1.
Fil: Nemer Pelliza, Karim Alejandra. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Centro de Investigación en Informatica para la Ingeniería. Córdoba; Argentina.
Fil: Pucheta, Martín Alejo. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Centro de Investigación en Informatica para la Ingeniería. Córdoba; Argentina.
Fil: Flesia, Ana Georgina. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Centro de Investigación en Informatica para la Ingeniería. Córdoba; Argentina.
Peer Reviewed
description Canny’s algorithm is a very well-known and widely implemented multistage edge detector. The extraction of coastal lines in space-borne-based synthetic aperture radar (SAR) images using this algorithm is particularly complicated because of the multiplicative speckle noise present in them and can only be used if Canny’s parameters (CaPP) are chosen appropriately. This letter introduces a methodology for computing functional forms for the CaPP, using functions of the image characteristics through a system that combines artificial neural networks (ANN) with statistical regression. A set of CaPP functional forms is obtained by applying this method on synthetic SAR images. Pratt’s fig- ure of merit (PFoM) is used to measure the performance of them, obtaining more than 0.75, on average, in the 14 400 synthetic SAR images analyzed. Finally, this set of formulas has been tested for extracting coastal edges from real polynyas SAR images, acquired from Sentinel-1.
publishDate 2020
dc.date.none.fl_str_mv 2020
2024-04-11T21:11:08Z
2024-04-11T21:11:08Z
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/20.500.12272/10467
-
url http://hdl.handle.net/20.500.12272/10467
identifier_str_mv -
dc.language.none.fl_str_mv eng
eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
2024-04-11T21:11:08Z
http://creativecommons.org/licenses/by-nc-nd/4.0/
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Nemer Pelliza, Karim Alejandra
https://creativecommons.org/licenses/by-nc-sa/4.0/
eu_rights_str_mv openAccess
rights_invalid_str_mv 2024-04-11T21:11:08Z
http://creativecommons.org/licenses/by-nc-nd/4.0/
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Nemer Pelliza, Karim Alejandra
https://creativecommons.org/licenses/by-nc-sa/4.0/
dc.format.none.fl_str_mv pdf
application/pdf
dc.source.none.fl_str_mv reponame:Repositorio Institucional Abierto (UTN)
instname:Universidad Tecnológica Nacional
reponame_str Repositorio Institucional Abierto (UTN)
collection Repositorio Institucional Abierto (UTN)
instname_str Universidad Tecnológica Nacional
repository.name.fl_str_mv Repositorio Institucional Abierto (UTN) - Universidad Tecnológica Nacional
repository.mail.fl_str_mv gestionria@rec.utn.edu.ar; fsuarez@rec.utn.edu.ar
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score 13.24418

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