H/α Unsupervised Classification for Highly Textured Polinsar Images using Information Geometry of Covariance Matrices

We discuss in the paper the use of the Riemannian mean given by the differential geometric tools. This geometric mean is used in this paper for computing the class centers in the polarimetric H/α unsupervised classification process. We show that the class centers remain more stable during the iteration process, leading to a different interpretation of the H/α/A classification. This technique can be applied both on classical Sample Covariance Matrix and on Fixed Point covariance matrices. Used jointly with the Fixed Point covariance matrix estimate, this technique can give more robust results when dealing with high resolution and highly textured polarimetric SAR images classification.