Abstract: The multi-variant product model is widely applied in the field of PolSAR imagery, whose selection of texture component directly affects the modelwhose accuracy. Aimed at the problem of statistical model recognition for the texture component, an unsupervised method based on covariance matrix log-cumulants (MLC) is proposed. This method colors the second and third MLCs plane, then the PolSAR data are projected on the plane, and the statistical model is distinguished by the color of the pixels. The main advantage of the new method is to give a simple and macroscopic result, which can provide important support for the subsequent target detection, identification and classification of PolSAR data. Finally, experiments on the new method are made using simulated data and real PolSAR data and the results show that the new estimator is effective and robust.

Key words: polarimetric synthetic aperture radar, product model, matrix log-cumulant, model recognition