The classification of polarimetric SAR images by computer has become a research hotspot in remote sensing. In this study, the fully polarimetric SAR data is used to extract characteristics by different algorithms, and the classification of tidal flat of Jiangsu coastal is realized. Firstly, the polarimetric scattering characteristics are extracted by H/α and Freeman decompositions, and the texture features are extracted by gray level co-occurrence matrix. Then, all the extracted features are combined to form different feature sets. Finally, the random forest model is used to classify and accurately evaluate with different feature sets. The study shows that using only texture features to classification achieves a poor performance. The classifications using the scattering features extracted by polarimetric decompositions are better than that of matrix element features. The combination of polarimetric scattering and texture characteristics can obtain best classification in coastal tidal flat, and the overall accuracy and Kappa coefficient are 94.44% and 0.9305, respectively. It indicates that the characteristics of different aspects contained in fully polarimetric SAR image have certain complementarity in the classification of coastal area.