Information Compression and Speckle Reduction for Multifrequency Polarimetric SAR Imagery using KPCA

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 many 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. This paper presents a method of information compression and speckle reduction for multifrequency polarimetric SAR imagery based on kernel principal component analysis (KPCA). KPCA is a nonlinear generalization of linear principal component analysis using kernel trick. The NASA/JPL polarimetric SAR imagery of P, L, and C bands quadpolarizations is used for illustration. Experimental results show that KPCA has better capability in information compression and speckle reduction compared with linear PCA.