Speckle Filtering Algorithm of PolSAR Imagery Based on Two-Dimensional Polarimetric Subspace ICA
To improve the performance of speckle filter on polarimetric synthetic aperture radar (PolSAR) imagery, an ICA algorithm based on two-dimensional polarization subspace is proposed. In this method, the PolSAR data of three channels were divided into three two-dimensional subspaces, and then the speckle noise component and texture component can be separated by the ICA algorithm. The equivalent number of looks (ENL) can be used to evaluate the effect of speckle filter. And an automatic ENL estimation algorithm is introduced to avoid the manual selection of a region with fully developed speckle and no texture. Performance of the novel speckle filter is tested through real data and the results show that the proposed filter is effective and robust.
KeywordsIndependent component analysis Polarimetric subspace Polarimetric SAR Speckle
This work was supported by the National Natural Science Foundation of China under Grant 61372165. The authors are also grateful to European Space Agency for providing the PolSAR data.
- 3.Chen HY, Li LM (2006) Speckle reduction in polarimetric SAR images based on independent component analysis. J Univ Electron Sci Technol China 35(4):480–483 (in Chinese)Google Scholar
- 5.Chen HY, Fu YS, Pi YM (2007) “Speckle reduction of polarimetric SAR image based on ICA-SCS algorithm. J Electron Inf Technol 29(4):819–821 (in Chinese)Google Scholar
- 6.Chen PS, Tong QX (1998) Remote sensing information mechanism research. Science press, Beijing, pp 43–50 (in Chinese)Google Scholar
- 10.Silverman BW (1986) Density estimation for statistics and data analysis. Chapman & Hall/CRC, Boca Raton, pp 103–108Google Scholar
- 11.Wand MP, Jones MC (1995) Kernel smoothing, Chapman & Hall/CRC, Boca Raton, pp 239–250Google Scholar
- 15.Chitroub S, Amrane H, Boualem S (2000) Compound PCA-ICA neural network model for enhancement and feature extraction of multi-frequency polarimetric SAR imagery. In: Proceedings the 2000 International Conference on Image Processing, Vancouver, British Columbia, Canada, pp 328–331Google Scholar