Unsupervised clustering of PolSAR data using Polarimetric G Distribution and Markov Random Fields

Abstract:
In this paper an unsupervised PolSAR data clustering algorithm utilizing the flexible polarimetric G distribution is proposed for the first time. This algorithm has been demonstrated in earlier contributions using other non-Gaussian distributions like K, G⁰ , and U distributions. The K and G⁰ distributions suffer from limited modeling capability due to the presence of only one shape parameter, while the U distribution, although as flexible as the G model, has a very cumbersome probability distribution function, making its software implementation difficult and computation slow. The proposed algorithmwith the G distribution has a similar non-Gaussianmodeling accuracy to the U model, a more easily implementable probability distribution function, and a much faster computation time. The only disadvantage being that the log cumulants of the G model are only computable using numerical differentiation, and hence fractional moment estimators are used in this analysis.