Identifying departures from the fully developed speckle hypothesis in intensity SAR data with non-parametric estimation of the entropy

SAR Data are affected by speckle, a non-additive and non-gaussian interference noise-like pattern. The distribution these data follow is paramount for their processing and analysis. Good statistical models provide flexibility and accuracy, often at the cost of using several parameters. The $\mathcal{G}^0$ distribution is one of the most successful models for SAR data. It includes the Gamma law as a particular case which arises in the presence of fully developed speckle. Although the latter is a limit distribution of the former, using the same estimation technique for the more general model is numerically unfeasible. We developed a test statistic based on an bootstrap-improved non-parametric estimator of the Shannon entropy for assessing departures from the fully-developed speckle hypothesis. We show the adequacy of the proposal with simulated and SAR data.