Parameter estimation in SAR imagery using stochastic distances

In this paper we analyze several strategies for the estimation of the roughness parameter of the G_I⁰ distribution. It has been shown that this distribution is able to characterize a large number of targets in monopolarized SAR imagery, deserving the denomination of “Universal Model”. It is indexed by three parameters: the number of looks (which can be estimated in the whole image), a scale parameter, and the roughness parameter. The latter is closely related to the number of elementary backscatters in each pixel, one of the reasons for receiving attention in the literature. Although there are efforts in providing improved and robust estimates for such quantity, its dependable estimation still poses numerical problems in practice. We derive a number of estimators based on the minimization of stochastic distances between empirical and theoretical densities. Some of these estimators outperform the classical alternatives (maximum likelihood, substitution-based and trimmed means).