In order to make full use of the ability of of Synthetic Aperture Radar (SAR) images with different polarization features for characterizing different types of ground objects, an analysis and classification approach of polarimetric SAR feature based on multi-layer Support Vector Machine (SVM) classifier was proposed. Firstly, the optimal feature subsets suitable for different terrain types were determined through the feature analysis. Then, the method of hierarchical classification tree was used for SVM classification step by step according to the feature subset of each object type.Finally, the overall final result was obtained. The experimental results of RadarSAT-2 polarimetric SAR image classification show that, the classification accuracy of the proposed approach is approximately 85% for four kinds of ground objects such as water area, cultivated land, forest land and urban area and the overall classification accuracy is up to 86%. The proposed approach can make full use of the characteristics of the different ground object target types, improve the classification accuracy and reduce the time complexity.