Parameter Estimation for the Single-Look $\mathcal{G}^0$ Distribution

TL;DR

This paper compares various parameter estimation methods for the $ ext{G}^0$ distribution in SAR images, focusing on single-look data, to identify the most accurate and efficient techniques under noise and contamination.

Contribution

It provides a comprehensive evaluation of estimation techniques for the $ ext{G}^0$ distribution, highlighting their performance in the noisiest single-look SAR data.

Findings

Estimation methods vary in bias and MSE under contamination.

Some techniques show faster convergence and lower computational cost.

Results validated with simulated and real SAR images.

Abstract

The statistical properties of Synthetic Aperture Radar (SAR) image texture reveals useful target characteristics. It is well-known that these images are affected by speckle, and prone to contamination as double bounce and corner reflectors. The $\mathcal{G}^0$ distribution is flexible enough to model different degrees of texture in speckled data. It is indexed by three parameters: $\alpha$, related to the texture, $\gamma$, a scale parameter, and $L$, the number of looks which is related to the signal-to-noise ratio. Quality estimation of $\alpha$ is essential due to its immediate interpretability. In this article, we compare the behavior of a number of parameter estimation techniques in the noisiest case, namely single look data. We evaluate them using Monte Carlo methods for non-contaminated and contaminated data, considering convergence rate, bias, mean squared error (MSE) and computational cost. The results are verified with simulated and actual SAR images.