Classification of Polarimetric SAR Image Based on Support Vector Machine Using Multiple-Component Scattering Model and Texture Features
The classification of polarimetric SAR image based on Multiple-Component Scattering Model (MCSM) and Support Vector Machine (SVM) is presented in this paper. MCSM is a potential decomposition method for a general condition. SVM is a popular tool for machine learning tasks involving classification, r...
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2010-01-01
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Series: | EURASIP Journal on Advances in Signal Processing |
Online Access: | http://dx.doi.org/10.1155/2010/960831 |
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doaj-372289f64fe3405bae942a51b95514b12020-11-25T01:37:17ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61721687-61802010-01-01201010.1155/2010/960831Classification of Polarimetric SAR Image Based on Support Vector Machine Using Multiple-Component Scattering Model and Texture FeaturesLamei ZhangBin ZouJunping ZhangYe ZhangThe classification of polarimetric SAR image based on Multiple-Component Scattering Model (MCSM) and Support Vector Machine (SVM) is presented in this paper. MCSM is a potential decomposition method for a general condition. SVM is a popular tool for machine learning tasks involving classification, recognition, or detection. The scattering powers of single-bounce, double-bounce, volume, helix, and wire scattering components are extracted from fully polarimetric SAR images. Combining with the scattering powers of MCSM and the selected texture features from Gray-level cooccurrence matrix (GCM), SVM is used for the classification of polarimetric SAR image. We generate a validity test for the proposed method using Danish EMISAR L-band fully polarimetric data of Foulum Area (DK), Denmark. The preliminary result indicates that this method can classify most of the areas correctly. http://dx.doi.org/10.1155/2010/960831 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Lamei Zhang Bin Zou Junping Zhang Ye Zhang |
spellingShingle |
Lamei Zhang Bin Zou Junping Zhang Ye Zhang Classification of Polarimetric SAR Image Based on Support Vector Machine Using Multiple-Component Scattering Model and Texture Features EURASIP Journal on Advances in Signal Processing |
author_facet |
Lamei Zhang Bin Zou Junping Zhang Ye Zhang |
author_sort |
Lamei Zhang |
title |
Classification of Polarimetric SAR Image Based on Support Vector Machine Using Multiple-Component Scattering Model and Texture Features |
title_short |
Classification of Polarimetric SAR Image Based on Support Vector Machine Using Multiple-Component Scattering Model and Texture Features |
title_full |
Classification of Polarimetric SAR Image Based on Support Vector Machine Using Multiple-Component Scattering Model and Texture Features |
title_fullStr |
Classification of Polarimetric SAR Image Based on Support Vector Machine Using Multiple-Component Scattering Model and Texture Features |
title_full_unstemmed |
Classification of Polarimetric SAR Image Based on Support Vector Machine Using Multiple-Component Scattering Model and Texture Features |
title_sort |
classification of polarimetric sar image based on support vector machine using multiple-component scattering model and texture features |
publisher |
SpringerOpen |
series |
EURASIP Journal on Advances in Signal Processing |
issn |
1687-6172 1687-6180 |
publishDate |
2010-01-01 |
description |
The classification of polarimetric SAR image based on Multiple-Component Scattering Model (MCSM) and Support Vector Machine (SVM) is presented in this paper. MCSM is a potential decomposition method for a general condition. SVM is a popular tool for machine learning tasks involving classification, recognition, or detection. The scattering powers of single-bounce, double-bounce, volume, helix, and wire scattering components are extracted from fully polarimetric SAR images. Combining with the scattering powers of MCSM and the selected texture features from Gray-level cooccurrence matrix (GCM), SVM is used for the classification of polarimetric SAR image. We generate a validity test for the proposed method using Danish EMISAR L-band fully polarimetric data of Foulum Area (DK), Denmark. The preliminary result indicates that this method can classify most of the areas correctly. |
url |
http://dx.doi.org/10.1155/2010/960831 |
work_keys_str_mv |
AT lameizhang classificationofpolarimetricsarimagebasedonsupportvectormachineusingmultiplecomponentscatteringmodelandtexturefeatures AT binzou classificationofpolarimetricsarimagebasedonsupportvectormachineusingmultiplecomponentscatteringmodelandtexturefeatures AT junpingzhang classificationofpolarimetricsarimagebasedonsupportvectormachineusingmultiplecomponentscatteringmodelandtexturefeatures AT yezhang classificationofpolarimetricsarimagebasedonsupportvectormachineusingmultiplecomponentscatteringmodelandtexturefeatures |
_version_ |
1725058600264531968 |