A polarimetric SAR image classification method based on the improved threecomponent scattering model［1］ is proposed. First, the method performs a deorientation operation on the polarization coherent matrix, and the orientation angle of each pixel is rotated to 0 degrees. Then, the improved threecomponent scattering model is used to decompose the target into surface scattering, doublebounce scattering, and volume scattering. Then the power scatter entropy is calculated by using the three scattering powers. According to the scattering entropy and the power of the three scattering components, an initial classification is made. Then, using Wishart iterative clustering to optimize the classification results. Finally, the results of Wishart clustering are reassessed to achieve unsupervised classification of polarimetric SAR images. The results show that the algorithm has a clear physical meaning and the classification result is easy to be combined with the actual features. The overall classification accuracy is 98.6% and the Kappa coefficient is 0.973.