Classification of Polarimetric SAR Image Based on Support Vector Machine Using Multiple-Component Scattering Model and Texture Features

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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Main Authors: Lamei Zhang, Bin Zou, Junping Zhang, Ye Zhang
Format: Article
Language:English
Published: SpringerOpen 2010-01-01
Series:EURASIP Journal on Advances in Signal Processing
Online Access:http://dx.doi.org/10.1155/2010/960831
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spelling 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
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