Polarimetric SAR Image Segmentation using CEM Algorithm

In this work we perform Synthetic Aperture Radar (SAR) polarimetric images segmentation based on the Classification-Expectation-Maximization (CEM) method, with both supervised and unsupervised initialization. In the former case, the algorithm is randomly initialized with the number of classes as the only initial information, while in the unsupervised case initialization is based on a previous classification. Real EMISAR Single-Look-Complex (SLC) data are used, with Mixing Gaussian model. Results are compared with those obtained by Wishart unsupervised classification method, which is a well-known and widely used method for radar image classification. Finally, Da-vies-Bouldin index is applied for quantitative comparison be-tween the obtained segmentations, and for studying the CEM method performance.