A robust estimator of parameters for GI0-modeled SAR imagery based on random weighting method

In mono-polarized synthetic aperture radar (SAR) imagery, GI distribution often is assumed as the universal model to characterize a large number of targets, which is indexed by three parameters: the number of looks, the scale parameter, and the roughness parameter. The latter is closely related to the number of elementary backscatters in each pixel, and it is the reason why so many researchers focus on it. Although many efforts have been paid on providing many estimates, numerical problems often exist in dependable estimation, such as ‘outlier’ and small samples and so on. Thus, a robust estimation scheme of two unknown parameters in GI distribution based on random weighting method is proposed in this paper where the relationship between moments and parameters are utilized. Experimental results on SAR computational simulations data and real SAR images show that the particular scheme outperforms alternative forms of bias reduction mechanisms, and we can obtain more accurate estimation than that of other state-of-the-art algorithms.