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, 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.

[1]  Richard W. Conners,et al.  Toward a Structural Textural Analyzer Based on Statistical Methods , 1980 .

[2]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[3]  Seisuke Fukuda,et al.  Unsupervised approach for polarimetric SAR image classification using support vector machines , 2002, IEEE International Geoscience and Remote Sensing Symposium.

[4]  Stephen L. Durden,et al.  A three-component scattering model for polarimetric SAR data , 1998, IEEE Trans. Geosci. Remote. Sens..

[5]  Andrea Baraldi,et al.  An investigation of the textural characteristics associated with gray level cooccurrence matrix statistical parameters , 1995, IEEE Transactions on Geoscience and Remote Sensing.

[6]  Wang Yang,et al.  New algorithm of target classification in polarimetric SAR , 2008 .

[7]  M. Hellmann,et al.  Classification of full polarimetric SAR-data using artificial neural networks and fuzzy algorithms , 1999, IEEE 1999 International Geoscience and Remote Sensing Symposium. IGARSS'99 (Cat. No.99CH36293).

[8]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[9]  Y. Yamaguchi,et al.  CS-1-4 Four-Component Scattering Model for Polarimetric SAR Image Decomposition based on Covariance Matrix(CS-1. 電磁波計測・イメージングと波動情報処理技術, エレクトロニクス1) , 2005 .

[10]  Henning Skriver,et al.  Multitemporal C- and L-band polarimetric signatures of crops , 1999, IEEE Trans. Geosci. Remote. Sens..

[11]  Thuy Le Toan,et al.  Agriculture classification using POLSAR data , 2005 .

[12]  W. L. Cameron,et al.  Feature motivated polarization scattering matrix decomposition , 1990, IEEE International Conference on Radar.

[13]  Seiho Uratsuka,et al.  Polarimetric SAR Image Analysis Using Model Fit for Urban Structures , 2005, IEICE Trans. Commun..

[14]  Vladimir Vapnik,et al.  An overview of statistical learning theory , 1999, IEEE Trans. Neural Networks.

[15]  Eric Pottier,et al.  A review of target decomposition theorems in radar polarimetry , 1996, IEEE Trans. Geosci. Remote. Sens..

[16]  Seisuke Fukuda,et al.  Polarimetric SAR Image Classification Using Support Vector Machines , 2001 .

[17]  Fabrizio Argenti,et al.  Fast algorithms for texture analysis using co-occurrence matrices , 1990 .

[18]  Ye Zhang,et al.  Multiple-Component Scattering Model for Polarimetric SAR Image Decomposition , 2008, IEEE Geoscience and Remote Sensing Letters.

[19]  Laurent Ferro-Famil,et al.  Unsupervised terrain classification preserving polarimetric scattering characteristics , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[20]  Laurent Ferro-Famil,et al.  Unsupervised classification of multifrequency and fully polarimetric SAR images based on the H/A/Alpha-Wishart classifier , 2001, IEEE Trans. Geosci. Remote. Sens..

[21]  Eric Pottier,et al.  An entropy based classification scheme for land applications of polarimetric SAR , 1997, IEEE Trans. Geosci. Remote. Sens..