Parametric and Nonparametric Tests for Speckled Imagery

TL;DR

This paper introduces new parametric and nonparametric statistical tests based on divergence measures for analyzing speckle noise in SAR images, improving contrast evaluation and robustness in scene analysis.

Contribution

The paper proposes novel divergence-based tests and a nonparametric Kolmogorov-Smirnov approach tailored for G0 distributed SAR data, with performance assessments.

Findings

Triangular and arithmetic-geometric divergence measures outperform Kolmogorov-Smirnov tests.

Proposed tests show robustness against contamination in Monte Carlo simulations.

New tools enhance SAR image contrast evaluation and scene element identification.

Abstract

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.