  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 with the multiplicative model and its implied G0 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 G0 distributed data. We also
introduce a nonparametric approach based on the Kolmogorov-Smirnov distance for
G0 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 Kolmogorov-Smirnov
methodology.
