Information compression and speckle reduction for multifrequency polarimetric SAR images based on kernel PCA  

作　　者：Li Ying Lei Xiaogang Bai Bendu Zhang Yanning 

机构地区：[1]Dept. of Computer Science and Engineering, Northwest Polytechnical Univ., Xi'an 710072, P. R. China

出　　处：《Journal of Systems Engineering and Electronics》2008年第3期493-498,共6页系统工程与电子技术（英文版）

基　　金：the Specialized Research Found for the Doctoral Program of Higher Education (20070699013);the Natural Science Foundation of Shaanxi Province (2006F05);the Aeronautical Science Foundation (05I53076).

摘　　要：Multifrequency polarimetric SAR imagery provides a very convenient approach for signal processing and acquisition of radar image. However, the amount of information is scattered in several images, and redundancies exist between different bands and polarizations. Similar to signal-polarimetric SAR image, multifrequency polarimetric SAR image is corrupted with speckle noise at the same time. A method of information compression and speckle reduction for multifrequency polarimetric SAR imagery is presented based on kernel principal component analysis （KPCA）. KPCA is a nonlinear generalization of the linear principal component analysis using the kernel trick. The NASA/JPL polarimetric SAR imagery of P, L, and C bands quadpolarizations is used for illustration. The experimental results show that KPCA has better capability in information compression and speckle reduction as compared with linear PCA.Multifrequency polarimetric SAR imagery provides a very convenient approach for signal processing and acquisition of radar image. However, the amount of information is scattered in several images, and redundancies exist between different bands and polarizations. Similar to signal-polarimetric SAR image, multifrequency polarimetric SAR image is corrupted with speckle noise at the same time. A method of information compression and speckle reduction for multifrequency polarimetric SAR imagery is presented based on kernel principal component analysis （KPCA）. KPCA is a nonlinear generalization of the linear principal component analysis using the kernel trick. The NASA/JPL polarimetric SAR imagery of P, L, and C bands quadpolarizations is used for illustration. The experimental results show that KPCA has better capability in information compression and speckle reduction as compared with linear PCA.

关 键 词：kernel PCA multifrequency polarimetric SAR imagery information compression despeckling. 

分 类 号：TN95[电子电信—信号与信息处理]