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A Likelihood-Based SLIC Superpixel Algorithm for SAR Images Using Generalized Gamma Distribution

The simple linear iterative clustering (SLIC) method is a recently proposed popular superpixel algorithm. However, this method may generate bad superpixels for synthetic aperture radar (SAR) images due to effects of speckle and the large dynamic range of pixel intensity. In this paper, an improved S...

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Detalles Bibliográficos
Autores principales: Zou, Huanxin, Qin, Xianxiang, Zhou, Shilin, Ji, Kefeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4970151/
https://www.ncbi.nlm.nih.gov/pubmed/27438840
http://dx.doi.org/10.3390/s16071107
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author Zou, Huanxin
Qin, Xianxiang
Zhou, Shilin
Ji, Kefeng
author_facet Zou, Huanxin
Qin, Xianxiang
Zhou, Shilin
Ji, Kefeng
author_sort Zou, Huanxin
collection PubMed
description The simple linear iterative clustering (SLIC) method is a recently proposed popular superpixel algorithm. However, this method may generate bad superpixels for synthetic aperture radar (SAR) images due to effects of speckle and the large dynamic range of pixel intensity. In this paper, an improved SLIC algorithm for SAR images is proposed. This algorithm exploits the likelihood information of SAR image pixel clusters. Specifically, a local clustering scheme combining intensity similarity with spatial proximity is proposed. Additionally, for post-processing, a local edge-evolving scheme that combines spatial context and likelihood information is introduced as an alternative to the connected components algorithm. To estimate the likelihood information of SAR image clusters, we incorporated a generalized gamma distribution (GГD). Finally, the superiority of the proposed algorithm was validated using both simulated and real-world SAR images.
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spelling pubmed-49701512016-08-04 A Likelihood-Based SLIC Superpixel Algorithm for SAR Images Using Generalized Gamma Distribution Zou, Huanxin Qin, Xianxiang Zhou, Shilin Ji, Kefeng Sensors (Basel) Article The simple linear iterative clustering (SLIC) method is a recently proposed popular superpixel algorithm. However, this method may generate bad superpixels for synthetic aperture radar (SAR) images due to effects of speckle and the large dynamic range of pixel intensity. In this paper, an improved SLIC algorithm for SAR images is proposed. This algorithm exploits the likelihood information of SAR image pixel clusters. Specifically, a local clustering scheme combining intensity similarity with spatial proximity is proposed. Additionally, for post-processing, a local edge-evolving scheme that combines spatial context and likelihood information is introduced as an alternative to the connected components algorithm. To estimate the likelihood information of SAR image clusters, we incorporated a generalized gamma distribution (GГD). Finally, the superiority of the proposed algorithm was validated using both simulated and real-world SAR images. MDPI 2016-07-18 /pmc/articles/PMC4970151/ /pubmed/27438840 http://dx.doi.org/10.3390/s16071107 Text en © 2016 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 (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zou, Huanxin
Qin, Xianxiang
Zhou, Shilin
Ji, Kefeng
A Likelihood-Based SLIC Superpixel Algorithm for SAR Images Using Generalized Gamma Distribution
title A Likelihood-Based SLIC Superpixel Algorithm for SAR Images Using Generalized Gamma Distribution
title_full A Likelihood-Based SLIC Superpixel Algorithm for SAR Images Using Generalized Gamma Distribution
title_fullStr A Likelihood-Based SLIC Superpixel Algorithm for SAR Images Using Generalized Gamma Distribution
title_full_unstemmed A Likelihood-Based SLIC Superpixel Algorithm for SAR Images Using Generalized Gamma Distribution
title_short A Likelihood-Based SLIC Superpixel Algorithm for SAR Images Using Generalized Gamma Distribution
title_sort likelihood-based slic superpixel algorithm for sar images using generalized gamma distribution
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4970151/
https://www.ncbi.nlm.nih.gov/pubmed/27438840
http://dx.doi.org/10.3390/s16071107
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