Improved estimation of clutter properties in speckled imagery

This paper's aim is to evaluate the effectiveness of bootstrap methods in improving estimation of clutter properties in speckled imagery. Estimation is performed by standard maximum likelihood methods. We show that estimators obtained this way can be quite biased in finite samples, and develop bias correction schemes using bootstrap resampling. In particular, we propose a bootstrapping scheme which is an adaptation of that proposed by Efron (J. Amer. Statist. Assoc. 85 (1990) 79). The proposed bootstrap does not require the quantity of interest to have closed form, as does Efron's original proposal. The adaptation we suggest is particularly important since the maximum likelihood estimator of interest does not have a closed form. We show that this particular bootstrapping scheme outperforms alternative forms of bias reduction mechanisms, thus delivering more accurate inference. We also consider interval estimation using bootstrap methods, and show that a particular parametric bootstrap-based confidence interval is typically more reliable than both the asymptotic confidence interval and other bootstrap-based confidence intervals. An application to real data is presented and discussed.