  The Rayleigh regression model was recently proposed for modeling amplitude
values of synthetic aperture radar (SAR) image pixels. However, inferences from
such model are based on the maximum likelihood estimators, which can be biased
for small signal lengths. The Rayleigh regression model for SAR images often
takes into account small pixel windows, which may lead to inaccurate results.
In this letter, we introduce bias-adjusted estimators tailored for the Rayleigh
regression model based on: (i) the Cox and Snell's method; (ii) the Firth's
scheme; and (iii) the parametric bootstrap method. We present numerical
experiments considering synthetic and actual SAR data sets. The bias-adjusted
estimators yield nearly unbiased estimates and accurate modeling results.
