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In the context of remotely sensed data analysis, a crucial problem is represented by the need to develop accurate models for the statistics of pixel intensities. In this work, we develop a parametric nite mixture model for the statistics of pixel intensities in high resolution synthetic aperture radar (SAR) images. This method,is an extension of previously existing method for lower resolution images. The method,integrates the stochastic expectation maximization (SEM) scheme and the method of log-cumulants (MoLC) with an automatic technique to select, for each mixture component, an optimal parametric model taken from a predened dictionary of parametric probability density functions (pdf). The proposed dictionary consists of eight state-of-the-art SAR- specic pdfs: Nakagami, log-normal, generalized Gaussian Rayleigh, Heavy-tailed Rayleigh, Weibull, K-root, Fisher and generalized Gamma. The designed scheme is endowed with the novel initialization procedure and the algorithm to automatically estimate the optimal number,of mixture components. The experimental results with a set of several high resolution COSMO-SkyMed images demonstrate the high accuracy of the designed algorithm, both from the viewpoint of a visual comparison of the histograms, and from the viewpoint of quantitive accuracy measures such as correlation coecient,(above 99,5%). The method proves to be eective on all the considered images, remaining accurate for multimodal and highly heterogeneous scenes. Keywords: Synthetic aperture radar (SAR) image, probability density function (pdf), parametric estimation,

Dictionary-based probability density function estimation for high-resolution SAR data