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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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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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 |