1Automated Non-Gaussian Clustering of Polarimetric Synthetic Aperture Radar Images

Abstract—This paper presents an automatic image segmen-tation method for Polarimetric SAR data. It utilises the full polarimetric information and incorporates texture by modelling with a non-Gaussian distribution for the complex scattering coefficients. The modelling is based upon the well known product model, with a Gamma distributed texture parameter, leading to the K-Wishart model for the covariance matrix. The automatic clustering is achieved through a finite mixture model estimated with a modified Expectation Maximisation algorithm. We include an additional goodness-of-fit test stage that allows for splitting and merging of clusters. This not only improves the model fit of the clusters, but also dynamically selects the appropriate number of clusters. The resulting image segmentation depicts the statistically significant clusters within the image. A key feature is that the degree of sub-sampling of the input image will affect the detail level of the clustering, revealing only the major classes, or a variable level of detail. Real world examples are shown to demonstrate the technique