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We compare the performance of the texture and the amplitude based mixture density models for urban area extraction from high resolution Synthetic Aperture Radar (SAR) images. We use an Auto-Regressive (AR) model with t-distribution error for the textures and a Nakagami density for the amplitudes. We exploit a Multinomial Logistic (MnL) latent class label model as a mixture density to obtain spatially smooth class segments. We combine the Classification EM (CEM) algorithm with the hierarchical agglomeration strategy and a model order selection criterion called Integrated Completed Likelihood (ICL).We test our algorithm on TerraSAR-X data provided by DLR/DFD.