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In this paper, a new method for supervised classification of terrain types in polarimetric Synthetic Aperture Radar (Pol-SAR) images is proposed. This technique is a combination of the texture classification and the maximum likelihood classification based on the complex Wishart distribution for the polarimetric covariance matrix. The texture features are first extracted from the span image based on co-occurrence matrices; and then the classifier combines the texture features with the distance measure based on polarimetric information to obtain the results. Using a NASA/JPL AIRSAR image, the effectiveness of the proposed method is demonstrated.