Abstract: Speckle reduction is a very essential part during the process of the polarimetric synthetic aperture radar (POLSAR) image, but the traditional common methods have each defects. In order to improve the accuracy of polarimetric synthetic aperture radar image speckle reduction, a polarimetric SAR image speckle reduction method using kernel independent component analysis (KICA) is presented. This method uses the polarimetric information of three channels as its input data, obtains three independent components after KICA conversion, and takes the one with the smallest speckle index as the filtered results. Due to the introduction of kernel function, the information that can not be linearly separated using independent component analysis (ICA) algorithm achieves linearly separated in the kernel high-dimensional space. For the purpose of verifying the validity of the KICA method, the AIRSAR data of San Francisco area and the PISAR data of Japan’s Niigata region were tested. The efficiency was objectively evaluated by the speckle reduction index and the edge preservation index. And the experiment results show that the image edges are retained better and the speckles are removed more effectively with the method of KICA algorithm compared with the ICA algorithm.