In this paper, we proposed a new semi-supervised method for polarimetric synthetic aperture radar (PolSAR) terrain classification based on improved tri-training. This method only needs a few numbers of labeled samples to achieve the results obtained by traditional supervised classification methods. First, it uses a variety of target decomposition methods to obtain high-dimensional feature. Second, a new feature selection method based on the ratio of between-class scatter and within-class scatter is proposed to reduce the redundant feature. Finally, an improved tri-training method is executed. A real PolSAR data is used to verify the proposed method. Experimental results show that the proposed method is efficient with a few labeled samples and effectively improve the classification accuracy compared with other traditional classification methods.