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SAR Image Segmentation Using Voronoi Tessellation and Bayesian Inference Applied to Dark Spot Feature Extraction
This paper presents a new segmentation-based algorithm for oil spill feature extraction from Synthetic Aperture Radar (SAR) intensity images. The proposed algorithm combines a Voronoi tessellation, Bayesian inference and Markov Chain Monte Carlo (MCMC) scheme. The shape and distribution features of...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Molecular Diversity Preservation International (MDPI)
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3871083/ https://www.ncbi.nlm.nih.gov/pubmed/24233074 http://dx.doi.org/10.3390/s131114484 |
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author | Zhao, Quanhua Li, Yu Liu, Zhenggang |
author_facet | Zhao, Quanhua Li, Yu Liu, Zhenggang |
author_sort | Zhao, Quanhua |
collection | PubMed |
description | This paper presents a new segmentation-based algorithm for oil spill feature extraction from Synthetic Aperture Radar (SAR) intensity images. The proposed algorithm combines a Voronoi tessellation, Bayesian inference and Markov Chain Monte Carlo (MCMC) scheme. The shape and distribution features of dark spots can be obtained by segmenting a scene covering an oil spill and/or look-alikes into two homogenous regions: dark spots and their marine surroundings. The proposed algorithm is applied simultaneously to several real SAR intensity images and simulated SAR intensity images which are used for accurate evaluation. The results show that the proposed algorithm can extract the shape and distribution parameters of dark spot areas, which are useful for recognizing oil spills in a further classification stage. |
format | Online Article Text |
id | pubmed-3871083 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Molecular Diversity Preservation International (MDPI) |
record_format | MEDLINE/PubMed |
spelling | pubmed-38710832013-12-26 SAR Image Segmentation Using Voronoi Tessellation and Bayesian Inference Applied to Dark Spot Feature Extraction Zhao, Quanhua Li, Yu Liu, Zhenggang Sensors (Basel) Article This paper presents a new segmentation-based algorithm for oil spill feature extraction from Synthetic Aperture Radar (SAR) intensity images. The proposed algorithm combines a Voronoi tessellation, Bayesian inference and Markov Chain Monte Carlo (MCMC) scheme. The shape and distribution features of dark spots can be obtained by segmenting a scene covering an oil spill and/or look-alikes into two homogenous regions: dark spots and their marine surroundings. The proposed algorithm is applied simultaneously to several real SAR intensity images and simulated SAR intensity images which are used for accurate evaluation. The results show that the proposed algorithm can extract the shape and distribution parameters of dark spot areas, which are useful for recognizing oil spills in a further classification stage. Molecular Diversity Preservation International (MDPI) 2013-10-25 /pmc/articles/PMC3871083/ /pubmed/24233074 http://dx.doi.org/10.3390/s131114484 Text en © 2013 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/). |
spellingShingle | Article Zhao, Quanhua Li, Yu Liu, Zhenggang SAR Image Segmentation Using Voronoi Tessellation and Bayesian Inference Applied to Dark Spot Feature Extraction |
title | SAR Image Segmentation Using Voronoi Tessellation and Bayesian Inference Applied to Dark Spot Feature Extraction |
title_full | SAR Image Segmentation Using Voronoi Tessellation and Bayesian Inference Applied to Dark Spot Feature Extraction |
title_fullStr | SAR Image Segmentation Using Voronoi Tessellation and Bayesian Inference Applied to Dark Spot Feature Extraction |
title_full_unstemmed | SAR Image Segmentation Using Voronoi Tessellation and Bayesian Inference Applied to Dark Spot Feature Extraction |
title_short | SAR Image Segmentation Using Voronoi Tessellation and Bayesian Inference Applied to Dark Spot Feature Extraction |
title_sort | sar image segmentation using voronoi tessellation and bayesian inference applied to dark spot feature extraction |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3871083/ https://www.ncbi.nlm.nih.gov/pubmed/24233074 http://dx.doi.org/10.3390/s131114484 |
work_keys_str_mv | AT zhaoquanhua sarimagesegmentationusingvoronoitessellationandbayesianinferenceappliedtodarkspotfeatureextraction AT liyu sarimagesegmentationusingvoronoitessellationandbayesianinferenceappliedtodarkspotfeatureextraction AT liuzhenggang sarimagesegmentationusingvoronoitessellationandbayesianinferenceappliedtodarkspotfeatureextraction |