Classification of PolSAR data with the Complex Riesz distribution - Kammoun, Rayhan; Kessentini, Sameh; Zine, Raoudha (Communications in statistics. Theory and methods, 2025)
This article deals with unsupervised classification strategies applied to polarimetric synthetic aperture radar (PolSAR) images. We discuss the performance of the Complex Riesz distribution, which is used for the first time to classify PolSAR images. Hence, we extend the maximum likelihood (ML) and expectation-maximization (EM) algorithms to the Complex Riesz distribution. Furthermore, we derive the analytic expression of five stochastic distances (Kullback-Leibler, Bhattacharyya, Rényi, Hellinger, and Chi-square) between Complex Riesz distributions. We assess the accuracy of the Complex Riesz EM algorithm on synthetic data generated by an extension of the Bartlett decomposition. Then, comparing the Complex Wishart and the Complex Riesz distributions on PolSAR data reveals that the latter performs better than the former. Finally, the EM algorithm for the Complex Riesz distribution serves to classify the actual data, and the discrimination potential of the five stochastic distances is discussed. These results in both experimental, ML, and EM algorithms suggest that most stochastic distances are significant, and mainly the 0. 7-order Rényi.