In recent years, Full Polarimetric-Ground Penetrating Radar (FP-GPR) has been developed to enhance the recognition capabilities of GPR. In order to extract the polarimetric properties of the targets, many polarimetric decomposition techniques in Synthetic Aperture Radar (SAR) are applied to FP-GPR. H-Alpha decomposition can obtain two parameters, H and α, for classification. However, due to the differences in measurements between FP-GPR and SAR, the classic H-Alpha classification template in SAR may not be suitable for FP-GPR. This paper proposes to use the Support Vector Machine (SVM) and Particle Center Supported Plane (PCSP) to analyze and obtain the rules from the FP-GPR data and establish new classification criterion suitable for FP-GPR. The training and testing of four kinds of targets verified the feasibility of this two methods. Furthermore, the comparison about the accuracy of the two method was performed. Finally, the SVM and PCSP were applied to the landmine detection, and two new methods of landmine identification was proposed. The results of the two methods are compared.