Information compression and speckle reduction for multifrequency polarimetric SAR images based on kernel PCA ^* * This project was supported by the Specialized Research Found for the Doctoral Program of Higher Education (20070699013); the Natural Science Foundation of Shaanxi Province (2006F05); and the Aeronautical Science Foundation (05I53076).

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.