Polarimetric SAR image segmentation with B-splines and a new statistical model

We present an approach for polarimetric Synthetic Aperture Radar (SAR) image region boundary detection based on the use of B-Spline active contours and a new model for polarimetric SAR data: the GH P distribution. In order to detect the boundary of a region, initial B-Spline curves are specified, ei...

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Publicado: 2010
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Acceso en línea:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_09236082_v21_n4_p319_Frery
http://hdl.handle.net/20.500.12110/paper_09236082_v21_n4_p319_Frery
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spelling paper:paper_09236082_v21_n4_p319_Frery2023-06-08T15:51:01Z Polarimetric SAR image segmentation with B-splines and a new statistical model B-spline curves Complex-valued sensing Edge detection Estimation Polarimetric signal analysis Specklenoise Statistical model Synthetic aperture radar B spline curve Complex-valued sensing Polarimetric signal analysis Specklenoise Statistical models Algorithms Curve fitting Edge detection Image segmentation Imaging systems Polarimeters Polarographic analysis Radar Ship propellers Signal analysis Signal detection Splines Synthetic apertures Synthetic aperture radar We present an approach for polarimetric Synthetic Aperture Radar (SAR) image region boundary detection based on the use of B-Spline active contours and a new model for polarimetric SAR data: the GH P distribution. In order to detect the boundary of a region, initial B-Spline curves are specified, either automatically or manually, and the proposed algorithm uses a deformable contours technique to find the boundary. In doing this, the parameters of the polarimetric GH P model for the data are estimated, in order to find the transition points between the region being segmented and the surrounding area. This is a local algorithm since it works only on the region to be segmented. Results of its performance are presented. © 2010 Springer Science+Business Media, LLC. 2010 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_09236082_v21_n4_p319_Frery http://hdl.handle.net/20.500.12110/paper_09236082_v21_n4_p319_Frery
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic B-spline curves
Complex-valued sensing
Edge detection
Estimation
Polarimetric signal analysis
Specklenoise
Statistical model
Synthetic aperture radar
B spline curve
Complex-valued sensing
Polarimetric signal analysis
Specklenoise
Statistical models
Algorithms
Curve fitting
Edge detection
Image segmentation
Imaging systems
Polarimeters
Polarographic analysis
Radar
Ship propellers
Signal analysis
Signal detection
Splines
Synthetic apertures
Synthetic aperture radar
spellingShingle B-spline curves
Complex-valued sensing
Edge detection
Estimation
Polarimetric signal analysis
Specklenoise
Statistical model
Synthetic aperture radar
B spline curve
Complex-valued sensing
Polarimetric signal analysis
Specklenoise
Statistical models
Algorithms
Curve fitting
Edge detection
Image segmentation
Imaging systems
Polarimeters
Polarographic analysis
Radar
Ship propellers
Signal analysis
Signal detection
Splines
Synthetic apertures
Synthetic aperture radar
Polarimetric SAR image segmentation with B-splines and a new statistical model
topic_facet B-spline curves
Complex-valued sensing
Edge detection
Estimation
Polarimetric signal analysis
Specklenoise
Statistical model
Synthetic aperture radar
B spline curve
Complex-valued sensing
Polarimetric signal analysis
Specklenoise
Statistical models
Algorithms
Curve fitting
Edge detection
Image segmentation
Imaging systems
Polarimeters
Polarographic analysis
Radar
Ship propellers
Signal analysis
Signal detection
Splines
Synthetic apertures
Synthetic aperture radar
description We present an approach for polarimetric Synthetic Aperture Radar (SAR) image region boundary detection based on the use of B-Spline active contours and a new model for polarimetric SAR data: the GH P distribution. In order to detect the boundary of a region, initial B-Spline curves are specified, either automatically or manually, and the proposed algorithm uses a deformable contours technique to find the boundary. In doing this, the parameters of the polarimetric GH P model for the data are estimated, in order to find the transition points between the region being segmented and the surrounding area. This is a local algorithm since it works only on the region to be segmented. Results of its performance are presented. © 2010 Springer Science+Business Media, LLC.
title Polarimetric SAR image segmentation with B-splines and a new statistical model
title_short Polarimetric SAR image segmentation with B-splines and a new statistical model
title_full Polarimetric SAR image segmentation with B-splines and a new statistical model
title_fullStr Polarimetric SAR image segmentation with B-splines and a new statistical model
title_full_unstemmed Polarimetric SAR image segmentation with B-splines and a new statistical model
title_sort polarimetric sar image segmentation with b-splines and a new statistical model
publishDate 2010
url https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_09236082_v21_n4_p319_Frery
http://hdl.handle.net/20.500.12110/paper_09236082_v21_n4_p319_Frery
_version_ 1768544556520833024