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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| Format: | Article |
| Language: | English |
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Wiley
2015-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2015/680715 |
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| _version_ | 1849409358316699648 |
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| author | Sheng Sun Renfeng Liu Wen Wen |
| author_facet | Sheng Sun Renfeng Liu Wen Wen |
| author_sort | Sheng Sun |
| collection | DOAJ |
| 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. |
| format | Article |
| id | doaj-art-763c4cefdf0d4222b3ad0565eb02e963 |
| institution | Kabale University |
| issn | 2090-0147 2090-0155 |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-763c4cefdf0d4222b3ad0565eb02e9632025-08-20T03:35:32ZengWileyJournal 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 |
| 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 |
| title | 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_short | 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 |
| url | http://dx.doi.org/10.1155/2015/680715 |
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