Unsupervised classification algorithm based on EM method for polarimetric SAR images

Abstract In this work we develop an iterative classification algorithm using complex Gaussian mixture models for the polarimetric complex SAR data. It is a non supervised algorithm which does not require training data or an initial set of classes. Additionally, it determines the model order from data, which allows representing data structure with minimum complexity. The algorithm consists of four steps: initialization, model selection, refinement and smoothing. After a simple initialization stage, the EM algorithm is iteratively applied in the model selection step to compute the model order and an initial classification for the refinement step. The refinement step uses Classification EM (CEM) to reach the final classification and the smoothing stage improves the results by means of non-linear filtering. The algorithm is applied to both simulated and real Single Look Complex data of the EMISAR mission and compared with the Wishart classification method. We use confusion matrix and kappa statistic to make the comparison for simulated data whose ground-truth is known. We apply Davies–Bouldin index to compare both classifications for real data. The results obtained for both types of data validate our algorithm and show that its performance is comparable to Wishart’s in terms of classification quality.