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dc.contributor.author
Nemer Pelliza, Karim Alejandra
dc.contributor.author
Pucheta, Martín Alejo
dc.contributor.author
Flesia, Ana Georgina
dc.date.available
2021-03-04T16:59:50Z
dc.date.issued
2020-01
dc.identifier.citation
Nemer Pelliza, Karim Alejandra; Pucheta, Martín Alejo; Flesia, Ana Georgina; Optimal Canny's Parameters Regressions for Coastal Line Detection in Satellite-Based SAR Images; Institute of Electrical and Electronics Engineers; Ieee Geoscience and Remote Sensing Letters; 17; 1; 1-2020; 82-86
dc.identifier.issn
1545-598X
dc.identifier.uri
http://hdl.handle.net/11336/127463
dc.description.abstract
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 figure of merit (PFoM) is used to measure the performance of them, obtaining more than 0.75, on average, in the 14400 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.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Institute of Electrical and Electronics Engineers
dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
ARTIFICIAL NEURAL NETWORKS (ANNS)
dc.subject
EDGE DETECTION
dc.subject
STATISTICAL ANALYSIS
dc.subject
SYNTHETIC APERTURE RADAR (SAR) IMAGES
dc.subject.classification
Otras Ciencias de la Computación e Información
dc.subject.classification
Ciencias de la Computación e Información
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
dc.title
Optimal Canny's Parameters Regressions for Coastal Line Detection in Satellite-Based SAR Images
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2020-11-19T21:20:43Z
dc.identifier.eissn
1558-0571
dc.journal.volume
17
dc.journal.number
1
dc.journal.pagination
82-86
dc.journal.pais
Estados Unidos
dc.description.fil
Fil: Nemer Pelliza, Karim Alejandra. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Departamento de Electronica; Argentina
dc.description.fil
Fil: Pucheta, Martín Alejo. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Departamento de Electronica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.description.fil
Fil: Flesia, Ana Georgina. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomia y Física. Sección Matemática. Grupo de Probabilidad y Estadística; 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
dc.journal.title
Ieee Geoscience and Remote Sensing Letters
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://ieeexplore.ieee.org/document/8736022/
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1109/LGRS.2019.2916225
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