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In this paper, an efficient statistical model, called generalized Gamma distribution (GΓD), for the empirical modeling of synthetic aperture radar (SAR) images is proposed. The GΓD forms a large variety of alternative distributions (especially including Rayleigh, exponential, Nakagami, Gamma, Weibull, and log-normal distributions commonly used for the probability density function (pdf) of SAR images as special cases), and is flexible to model the SAR images with different land-cover typologies. Moreover, based on second-kind cumulants, a closed-form estimator for GΓD parameters is derived by exploiting the second-order approximation for Polygamma function. Without involving the numerical iterative process for solutions, this estimator is computationally efficient and, hence, can make the GΓD convenient for applications in the online SAR image processing. Finally, experimental results from tests carried out with actual SAR images demonstrate that the GΓD can achieve better goodness of fit than the state-of-the-art pdfs.