Fully Polarimetric SAR Image Classification via Sparse Representation and Polarimetric Features

Feature extraction and image classification using polarimetric synthetic aperture radar (PolSAR) images are currently of great interest in SAR applications. Generally, PolSAR image classification is a high-dimensional nonlinear mapping problem. Sparse representation-based techniques have shown great potential for pattern recognition problems. Therefore, on the basis of the sparse characteristics of the features for PolSAR image classification, a supervised PolSAR image classification method based on sparse representation is proposed in this paper. First, the effective features are extracted to describe the distinction of each class. Then, the feature vectors of the training samples construct an over-complete dictionary and obtain the corresponding sparse coefficients; meanwhile, the residual error of the pending pixel with respect to each atom is evaluated and considered as the criteria for classification, and the ultimate class results can be obtained according to the atoms with the least residual error. In addition, a Simplified Matching Pursuit (SMP) algorithm is proposed to solve the optimization problem of sparse representation of PolSAR images. The verification tests are implemented using Danish EMISAR L-band fully polarimetric SAR data of Foulum area, Denmark. The preliminary experimental results confirm that the proposed method outputs an excellent result and moreover the classification process is simpler and less time consuming.

[1]  Søren Nørvang Madsen,et al.  EMISAR: an absolutely calibrated polarimetric L- and C-band SAR , 1998, IEEE Trans. Geosci. Remote. Sens..

[2]  Lamei Zhang,et al.  Classification of Polarimetric SAR Image Based on Support Vector Machine Using Multiple-Component Scattering Model and Texture Features , 2010, EURASIP J. Adv. Signal Process..

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

[4]  Mohammed Dabboor,et al.  An Unsupervised Classification Approach for Polarimetric SAR Data Based on the Chernoff Distance for Complex Wishart Distribution , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[5]  Thomas L. Ainsworth,et al.  Unsupervised classification using polarimetric decomposition and the complex Wishart classifier , 1999, IEEE Trans. Geosci. Remote. Sens..

[6]  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..

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

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

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

[10]  Avik Bhattacharya,et al.  A Generic Land-Cover Classification Framework for Polarimetric SAR Images Using the Optimum Touzi Decomposition Parameter Subset—An Insight on Mutual Information-Based Feature Selection Techniques , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[11]  Stéphane Mallat,et al.  Matching pursuits with time-frequency dictionaries , 1993, IEEE Trans. Signal Process..

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

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

[14]  Karthikeyan Natesan Ramamurthy,et al.  SAR target classification using sparse representations and spatial pyramids , 2011, 2011 IEEE RadarCon (RADAR).

[15]  Wen Hong,et al.  An Unsupervised Segmentation With an Adaptive Number of Clusters Using the $SPAN/H/\alpha/A$ Space and the Complex Wishart Clustering for Fully Polarimetric SAR Data Analysis , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[16]  Henri Maître,et al.  A new statistical model for Markovian classification of urban areas in high-resolution SAR images , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[17]  Yiming Pi,et al.  Polarimetric Contextual Classification of PolSAR Images Using Sparse Representation and Superpixels , 2014, Remote. Sens..

[18]  Tyng-Luh Liu,et al.  Sparse Representations for Image Decompositions , 1999, International Journal of Computer Vision.

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

[20]  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).

[21]  Hiroyoshi Yamada,et al.  Four-component scattering model for polarimetric SAR image decomposition , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[22]  谢鸿全.  An Unsupervised Segmentation With an Adaptive Number of Clusters Using the SPAN/H/a/A Space and the Complex Wishart Clustering for Fully Polarimetric SAR Data Analysis , 2007 .

[23]  Joel A. Tropp,et al.  Topics in sparse approximation , 2004 .

[24]  Mingsheng Liao,et al.  Learning based decomposition for polarmetric SAR images , 2011, 2011 IEEE International Geoscience and Remote Sensing Symposium.

[25]  Allen Y. Yang,et al.  Robust Face Recognition via Sparse Representation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.