PolSAR image speckle reduction based on sparse representation and structure characteristics

This paper presents a novel speckle reduction algorithm based on sparse representation and structure characteristics of PolSAR image. First, each pixel in original image is classified into bright point or line targets, dark point or line targets and others to form a classification map. Second, sparse decomposition and reconstruction is performed on PolSAR image by OMP and K-SVD methods to filter speckle. Finally, the blurred point and line targets in filtered image are enhanced with the classification map. Experimental results with the data of Hayward area from AIRSAR system show that the proposed method is effective both on speckle reduction and scattering characteristics preservation.

[1]  Hartmut Bossel,et al.  Modeling and simulation , 1994 .

[2]  Wang Jian-ying Image Denoising Based on Its Sparse Decomposition , 2006 .

[3]  Samuel Foucher,et al.  SAR Image Filtering Via Learned Dictionaries and Sparse Representations , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.

[4]  Licheng Jiao,et al.  Sparse representation-based spectral clustering for SAR image segmentation , 2011, International Symposium on Multispectral Image Processing and Pattern Recognition.

[5]  Michael Elad,et al.  Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries , 2006, IEEE Transactions on Image Processing.

[6]  Y. C. Pati,et al.  Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition , 1993, Proceedings of 27th Asilomar Conference on Signals, Systems and Computers.

[7]  Michael Elad,et al.  Learning Multiscale Sparse Representations for Image and Video Restoration , 2007, Multiscale Model. Simul..

[8]  Jayaraman J. Thiagarajan,et al.  Sparse representations for automatic target classification in SAR images , 2010, 2010 4th International Symposium on Communications, Control and Signal Processing (ISCCSP).

[9]  Thomas S. Huang,et al.  Multi-View Automatic Target Recognition using Joint Sparse Representation , 2012, IEEE Transactions on Aerospace and Electronic Systems.

[10]  M. Elad,et al.  $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation , 2006, IEEE Transactions on Signal Processing.

[11]  Michael Elad,et al.  Image Denoising Via Learned Dictionaries and Sparse representation , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[12]  Fei Dong,et al.  PolSAR image speckle reduction based on polarimetric decomposition and classification , 2011, Proceedings of 2011 IEEE CIE International Conference on Radar.

[13]  Shen Yan-hao Clustering based sparse model for image denoising , 2011 .