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The accurate statistical modeling of synthetic aperture radar (SAR) images is a crucial problem in the context of effective SAR image processing, interpretation and applications. In terms of the heavy-tailed or bi-/ multi-modal characteristics of histograms, it is difficult to apply a single parametric model to accurately describe the statistics of high-resolution SAR images. From the perspective of semi-parametric approach, we choose generalized Gamma distribution (GΓD) as the component of finite mixture model, then exploit generalized Gamma mixture model (GΓMM) to implement an effective statistical analysis of high-resolution SAR images in this paper. And we derive an expectation-maximization (EM) algorithm for estimating the corresponding parameters of GΓMM. Finally, experimental results, carried out on the actual high-resolution SAR images with single - and bi-modal histograms, demonstrate the validity of GΓMM.