Unsupervised amplitude and texture classification of SAR images with multinomial latent model

International audienceWe combine both amplitude and texture statistics of the Synthetic Aperture Radar (SAR) images for modelbased classification purpose. In a finite mixture model, we bring together the Nakagami densities to model the class amplitudes and a 2D Auto-Regressive texture model with t-distributed regression error to model the textures of the classes. A nonstationary Multinomial Logistic (MnL) latent class label model is used as a mixture density to obtain spatially smooth class segments. The Classification Expectation-Maximization (CEM) algorithm is performed to estimate the class parameters and to classify the pixels. We resort to Integrated Classification Likelihood (ICL) criterion to determine the number of classes in the model. We present our results on the classification of the land covers obtained in both supervised and unsupervised cases processing TerraSAR-X, as well as COSMO-SkyMed data