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.
翻译:最近为模拟合成孔径雷达(SAR)图像像素的振幅值提出了Rayleigh回归模型,但这种模型的推论依据的是最大可能性估计器,这种估计器可能偏向于小信号长度。Rayleigh合成孔径雷达图像回归模型常常考虑到小像素窗口,这可能导致不准确的结果。在本信里,我们为Rayleigh回归模型引入了偏差调整估计器,其依据是:(一) Cox和Snell方法;(二) Firth的图案;和(三) 参数式靴子捕捉方法。我们介绍了考虑到合成的和实际的合成孔径雷达数据集的数值实验。偏差调整估计器得出了近乎公正的估计和准确的模型结果。