Improving Log-Cumulant Based Estimation of Roughness Information in SAR imagery

TL;DR

This paper enhances the estimation of roughness in SAR imagery by improving the Log-Cumulant method with Bayesian modeling and a Trigamma function approximation, enabling faster and more reliable image analysis.

Contribution

It introduces a Bayesian-based approach and a Trigamma function approximation to improve the speed and reliability of roughness estimation in SAR images.

Findings

Reliable roughness estimates under different models

Significantly faster computation time

Enhanced SAR image understanding

Abstract

Synthetic Aperture Radar (SAR) image understanding is crucial in remote sensing applications, but it is hindered by its intrinsic noise contamination, called speckle. Sophisticated statistical models, such as the $\mathcal{G}^0$ family of distributions, have been employed to SAR data and many of the current advancements in processing this imagery have been accomplished through extracting information from these models. In this paper, we propose improvements to parameter estimation in $\mathcal{G}^0$ distributions using the Method of Log-Cumulants. First, using Bayesian modeling, we construct that regularly produce reliable roughness estimates under both $\mathcal{G}^0_A$ and $\mathcal{G}^0_I$ models. Second, we make use of an approximation of the Trigamma function to compute the estimated roughness in constant time, making it considerably faster than the existing method for this task. Finally, we show how we can use this method to achieve fast and reliable SAR image understanding based on roughness information.