Rayleigh Regression Model for Ground Type Detection in SAR Imagery

TL;DR

This paper introduces a Rayleigh regression model for ground type detection in SAR imagery, providing parameter estimation, detection techniques, and validation through simulations and real data comparisons.

Contribution

It presents a novel regression model specifically for Rayleigh-distributed signals in SAR imagery, including closed-form estimation and detection methods.

Findings

The model accurately estimates Rayleigh signal means.

Detection performance surpasses Gaussian, Gamma, and Weibull models.

Monte Carlo simulations validate the estimators' effectiveness.

Abstract

This letter proposes a regression model for nonnegative signals. The proposed regression estimates the mean of Rayleigh distributed signals by a structure which includes a set of regressors and a link function. For the proposed model, we present: (i)~parameter estimation; (ii)~large data record results; and (iii)~a detection technique. In this letter, we present closed-form expressions for the score vector and Fisher information matrix. The proposed model is submitted to extensive Monte Carlo simulations and to measured data. The Monte Carlo simulations are used to evaluate the performance of maximum likelihood estimators. Also, an application is performed comparing the detection results of the proposed model with Gaussian-, Gamma-, and Weibull-based regression models in SAR images.