Parametric and nonparametric tests for speckled imagery
Synthetic aperture radar (SAR) has a pivotal role as a remote imaging method. Obtained by means of coherent illumination, SAR images are contaminated with speckle noise. The statistical modeling of such contamination is well described according to the multiplicative model and its implied \(\fancyscript{G}^0\) distribution. The understanding of SAR imagery and scene element identification is an important objective in the field. In particular, reliable image contrast tools are sought. Aiming the proposition of new tools for evaluating SAR image contrast, we investigated new methods based on stochastic divergence. We propose several divergence measures specifically tailored for \(\fancyscript{G}^0\) distributed data. We also introduce a nonparametric approach based on the KolmogorovSmirnov distance for \(\fancyscript{G}^0\) data. We devised and assessed tests based on such measures, and their performances were quantified according to their test sizes and powers. Using Monte Carlo simulation, we present a robustness analysis of test statistics and of maximum likelihood estimators for several degrees of innovative contamination. It was identified that the proposed tests based on triangular and arithmetic-geometric measures outperformed the KolmogorovSmirnov methodology.
robust statistics information theory nonparametric methods parametric inference