Parameter Estimation in SAR Imagery using Stochastic Distances and Asymmetric Kernels

TL;DR

This paper investigates robust methods for estimating the roughness parameter of the al distribution in SAR imagery, proposing stochastic distance-based estimators with asymmetric kernels, and analyzing the distribution's heavy-tailed properties.

Contribution

It introduces a new estimator based on the Triangular distance and asymmetric kernels, improving robustness in roughness parameter estimation for SAR data.

Findings

The proposed estimator outperforms traditional methods in robustness.

The distribution al is confirmed to be heavy-tailed.

New insights into the heavy-tailedness of the al distribution.

Abstract

In this paper we analyze several strategies for the estimation of the roughness parameter of the $\mathcal G_I^0$ distribution. It has been shown that this distribution is able to characterize a large number of targets in monopolarized SAR imagery, deserving the denomination of "Universal Model" It is indexed by three parameters: the number of looks (which can be estimated in the whole image), a scale parameter, and the roughness or texture parameter. The latter is closely related to the number of elementary backscatters in each pixel, one of the reasons for receiving attention in the literature. Although there are efforts in providing improved and robust estimates for such quantity, its dependable estimation still poses numerical problems in practice. We discuss estimators based on the minimization of stochastic distances between empirical and theoretical densities, and argue in favor of using an estimator based on the Triangular distance and asymmetric kernels built with Inverse Gaussian densities. We also provide new results regarding the heavytailedness of this distribution.