Polarization is a property applying to transverse waves that specifies the geometrical orientation of the oscillations. This paper proposes a method for detecting small targets on the sea surface based on the combination of polarization features of two models. The scattering mechanism of sea clutter is random scattering at low glazing angle or glancing angle and the randomness is high as the angles do not have any specified shape. However, a target has a specific shape, and thus, the randomness of scattering will be less. Clutter is a term used for unwanted echoes in electronic systems, particularly in reference to radars. Such echoes typically return from ground, sea, rain, and animals/insects. In this literature, the randomness of a scattering mechanism in an echo is obtained from the probability density functions of polarization entropy using the Cloude decomposition model. Further, the proportion of scattering at spherical, dihedral, and helicoid angles from the target echoes will be different in the sea clutter. Therefore, the relative coefficient of power of these three scattering components in each echo is extracted based on Krogager polarization decomposition. Then, polarization features with good separability and complementarity are selected to form the polarization feature vector, and the characteristics are verified by Principle Component Analysis (PCA). Finally, One Class Support Vector Machine (OCSVM) is used for classification and recognition based on the polarization decomposition feature vector. Instead of single-polarization detection methods, our method uses two polarization modes to extract the decomposition features with separability and complementarity through polarization coherent decomposition and incoherent decomposition, respectively. The experimental results of the IPIX data show the effectiveness of our method. Thus, the detection performance of our model is better than those methods based on single-polarization decomposition in complex and difficult sea conditions.