SAR Image segmentation based on multifractal features

"Synthetic Aperture Radar (SAR) imaging is based on airborne or satellite active microwave sensors that can capture the earth surface by emitting a signal and receiving the backscattered signal that forms the resulting image. Since microwave radiation is not interfered by sunlight and can pass...

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Detalles Bibliográficos
Autores principales: Pacheco, Cristian, Gambini, Juliana, Delrieux, Claudio
Formato: Ponencias en Congresos acceptedVersion
Lenguaje:Inglés
Publicado: 2020
Materias:
RADAR DE APERTURA SINTETICA
PROCESAMIENTO DE IMAGENES
ANALISIS ESPECTRAL
SEGMENTACION DE IMAGENES
Acceso en línea:http://ri.itba.edu.ar/handle/123456789/1918
Aporte de:
Repositorio Institucional Instituto Tecnológico de Buenos Aires (ITBA) de Instituto Tecnológico de Buenos Aires (ITBA)
id I32-R138-123456789-1918
record_format dspace
spelling I32-R138-123456789-19182022-12-07T14:13:37Z SAR Image segmentation based on multifractal features Pacheco, Cristian Gambini, Juliana Delrieux, Claudio RADAR DE APERTURA SINTETICA PROCESAMIENTO DE IMAGENES ANALISIS ESPECTRAL SEGMENTACION DE IMAGENES "Synthetic Aperture Radar (SAR) imaging is based on airborne or satellite active microwave sensors that can capture the earth surface by emitting a signal and receiving the backscattered signal that forms the resulting image. Since microwave radiation is not interfered by sunlight and can pass through clouds, SAR imagery can be generated oblivious to weather and daylight conditions. However, the active nature of the imaging process determines that SAR images are contaminated by an inherent speckle noise that may degrade significantly the quality and usefulness of the images, and specific noise-removal processes may also filter out relevant textural information. In this article, we propose a texture-based method that can be applied for region segmentation in SAR imagery. The method is based on local analysis of the multifractal spectrum and a clustering procedure. The outcomes obtained both with synthetic and real SAR images show better region segmentation results than with state-of-the-art proposals." 2020-03-26T15:14:54Z 2020-03-26T15:14:54Z 2019-09 Ponencias en Congresos info:eu-repo/semantics/acceptedVersion 978-1728-123-63-9 http://ri.itba.edu.ar/handle/123456789/1918 en info:eu-repo/semantics/altIdentifier/doi/10.1109/RPIC.2019.8882173 info:eu-repo/grantAgreement/CONAE/AO-SAOCOM/AR. Ciudad Autónoma de Buenos Aires application/pdf
institution Instituto Tecnológico de Buenos Aires (ITBA)
institution_str I-32
repository_str R-138
collection Repositorio Institucional Instituto Tecnológico de Buenos Aires (ITBA)
language Inglés
topic RADAR DE APERTURA SINTETICA
PROCESAMIENTO DE IMAGENES
ANALISIS ESPECTRAL
SEGMENTACION DE IMAGENES
spellingShingle RADAR DE APERTURA SINTETICA
PROCESAMIENTO DE IMAGENES
ANALISIS ESPECTRAL
SEGMENTACION DE IMAGENES
Pacheco, Cristian
Gambini, Juliana
Delrieux, Claudio
SAR Image segmentation based on multifractal features
topic_facet RADAR DE APERTURA SINTETICA
PROCESAMIENTO DE IMAGENES
ANALISIS ESPECTRAL
SEGMENTACION DE IMAGENES
description "Synthetic Aperture Radar (SAR) imaging is based on airborne or satellite active microwave sensors that can capture the earth surface by emitting a signal and receiving the backscattered signal that forms the resulting image. Since microwave radiation is not interfered by sunlight and can pass through clouds, SAR imagery can be generated oblivious to weather and daylight conditions. However, the active nature of the imaging process determines that SAR images are contaminated by an inherent speckle noise that may degrade significantly the quality and usefulness of the images, and specific noise-removal processes may also filter out relevant textural information. In this article, we propose a texture-based method that can be applied for region segmentation in SAR imagery. The method is based on local analysis of the multifractal spectrum and a clustering procedure. The outcomes obtained both with synthetic and real SAR images show better region segmentation results than with state-of-the-art proposals."
format Ponencias en Congresos
acceptedVersion
author Pacheco, Cristian
Gambini, Juliana
Delrieux, Claudio
author_facet Pacheco, Cristian
Gambini, Juliana
Delrieux, Claudio
author_sort Pacheco, Cristian
title SAR Image segmentation based on multifractal features
title_short SAR Image segmentation based on multifractal features
title_full SAR Image segmentation based on multifractal features
title_fullStr SAR Image segmentation based on multifractal features
title_full_unstemmed SAR Image segmentation based on multifractal features
title_sort sar image segmentation based on multifractal features
publishDate 2020
url http://ri.itba.edu.ar/handle/123456789/1918
work_keys_str_mv AT pachecocristian sarimagesegmentationbasedonmultifractalfeatures
AT gambinijuliana sarimagesegmentationbasedonmultifractalfeatures
AT delrieuxclaudio sarimagesegmentationbasedonmultifractalfeatures
_version_ 1765660977037049856

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