Unsupervised Classification Method for Polarimetric Synthetic Aperture Radar Imagery Based on Yamaguchi Four-Component Decomposition Model

For improving the accuracy of unsupervised classification based on scattering models, the four-component Yamaguchi model is introduced, which is an improved version of the best-known three-component Freeman model. Therewith, the four-component model is combined with the Wishart distance model. The n...

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Main Authors: Sheng Sun, Renfeng Liu, Wen Wen
Format: Article
Language:English
Published: Hindawi Limited 2015-01-01
Series:Journal of Electrical and Computer Engineering
Online Access:http://dx.doi.org/10.1155/2015/680715
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spelling doaj-763c4cefdf0d4222b3ad0565eb02e9632021-07-02T13:57:36ZengHindawi LimitedJournal of Electrical and Computer Engineering2090-01472090-01552015-01-01201510.1155/2015/680715680715Unsupervised Classification Method for Polarimetric Synthetic Aperture Radar Imagery Based on Yamaguchi Four-Component Decomposition ModelSheng Sun0Renfeng Liu1Wen Wen2School of Computer Science, Guangdong University of Technology, Guangzhou 510006, ChinaInstitute for Pattern Recognition and Artificial Intelligence, Huazhong University of Science and Technology, Wuhan 430074, ChinaSchool of Computer Science, Guangdong University of Technology, Guangzhou 510006, ChinaFor improving the accuracy of unsupervised classification based on scattering models, the four-component Yamaguchi model is introduced, which is an improved version of the best-known three-component Freeman model. Therewith, the four-component model is combined with the Wishart distance model. The new proposed algorithm of clustering is rolled out thereafter and the procedure of this new method is listed. In experiments, seven areas of various homogeneities are singled out from the Flevoland sample image in AIRSAR dataset. Qualitative and quantitative experiments are performed for a comparative study. It can be easily seen that the resolution and details are remarkably upgraded by the new proposed method. The accuracy of classification in homogeneous areas has also increased significantly by adopting the new iterative algorithm.http://dx.doi.org/10.1155/2015/680715
collection DOAJ
language English
format Article
sources DOAJ
author Sheng Sun
Renfeng Liu
Wen Wen
spellingShingle Sheng Sun
Renfeng Liu
Wen Wen
Unsupervised Classification Method for Polarimetric Synthetic Aperture Radar Imagery Based on Yamaguchi Four-Component Decomposition Model
Journal of Electrical and Computer Engineering
author_facet Sheng Sun
Renfeng Liu
Wen Wen
author_sort Sheng Sun
title Unsupervised Classification Method for Polarimetric Synthetic Aperture Radar Imagery Based on Yamaguchi Four-Component Decomposition Model
title_short Unsupervised Classification Method for Polarimetric Synthetic Aperture Radar Imagery Based on Yamaguchi Four-Component Decomposition Model
title_full Unsupervised Classification Method for Polarimetric Synthetic Aperture Radar Imagery Based on Yamaguchi Four-Component Decomposition Model
title_fullStr Unsupervised Classification Method for Polarimetric Synthetic Aperture Radar Imagery Based on Yamaguchi Four-Component Decomposition Model
title_full_unstemmed Unsupervised Classification Method for Polarimetric Synthetic Aperture Radar Imagery Based on Yamaguchi Four-Component Decomposition Model
title_sort unsupervised classification method for polarimetric synthetic aperture radar imagery based on yamaguchi four-component decomposition model
publisher Hindawi Limited
series Journal of Electrical and Computer Engineering
issn 2090-0147
2090-0155
publishDate 2015-01-01
description For improving the accuracy of unsupervised classification based on scattering models, the four-component Yamaguchi model is introduced, which is an improved version of the best-known three-component Freeman model. Therewith, the four-component model is combined with the Wishart distance model. The new proposed algorithm of clustering is rolled out thereafter and the procedure of this new method is listed. In experiments, seven areas of various homogeneities are singled out from the Flevoland sample image in AIRSAR dataset. Qualitative and quantitative experiments are performed for a comparative study. It can be easily seen that the resolution and details are remarkably upgraded by the new proposed method. The accuracy of classification in homogeneous areas has also increased significantly by adopting the new iterative algorithm.
url http://dx.doi.org/10.1155/2015/680715
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AT renfengliu unsupervisedclassificationmethodforpolarimetricsyntheticapertureradarimagerybasedonyamaguchifourcomponentdecompositionmodel
AT wenwen unsupervisedclassificationmethodforpolarimetricsyntheticapertureradarimagerybasedonyamaguchifourcomponentdecompositionmodel
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