Improved Point Estimation for the Rayleigh Regression Model

@article{Palm2022ImprovedPE,
  title={Improved Point Estimation for the Rayleigh Regression Model},
  author={Bruna Gregory Palm and F{\'a}bio Mariano Bayer and Renato J. Cintra},
  journal={IEEE Geoscience and Remote Sensing Letters},
  year={2022},
  volume={19},
  pages={1-4}
}
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 considers 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: 1) Cox and… 

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References

SHOWING 1-10 OF 18 REFERENCES

Rayleigh Regression Model for Ground Type Detection in SAR Imagery

In this letter, closed-form expressions for the score vector and Fisher information matrix are presented and an application is performed comparing the detection results of the proposed model with Gaussian-, Gamma-, and Weibull-based regression models in synthetic aperture radar (SAR) images.

Nearly Unbiased Maximum Likelihood Estimation for the Beta Distribution

We analyze the finite-sample behavior of three second-order bias-corrected alternatives to the maximum likelihood estimator of the parameters that index the beta distribution. The three finite-sample

Linear Regression With Gaussian Model Uncertainty: Algorithms and Bounds

It is proved that the maximum-likelihood (ML) estimator is a (de)regularized least squares estimator and three alternative approaches for finding the regularization parameter that maximizes the likelihood are developed.

A Statistical Analysis for Wavelength-Resolution SAR Image Stacks

The tests results reveal that the Rician distribution is a very good candidate for modeling stack of wavelength-resolution SAR images, where 98.59% of the tested samples passed the Anderson–Darling (AD) goodness-of-fit test.

Parametric and nonparametric tests for speckled imagery

It was identified that the proposed tests based on triangular and arithmetic-geometric measures outperformed the Kolmogorov–Smirnov methodology for several degrees of innovative contamination.

Bias reduction of maximum likelihood estimates

SUMMARY It is shown how, in regular parametric problems, the first-order term is removed from the asymptotic bias of maximum likelihood estimates by a suitable modification of the score function. In

Speckle filtering of SAR images based on adaptive windowing

A new adaptive windowing algorithm is proposed for speckle noise suppression which solves the problem of window size associated with the local statistics adaptive filters and is applied to both a simulated SAR image and an ERS-1 SAR image.

SAR Image Despeckling Using a Space-Domain Filter With Alterable Window

Tests on synthetic and real SAR images show that SFAW notably smoothes speckle with unperceivable detail blurring and achieves better performances than other related methods.

Built-up Area Extraction from PolSAR Imagery with Model-Based Decomposition and Polarimetric Coherence

Experimental results demonstrate that the decomposed scattering powers and the proposed polarimetric coherence coefficient ratio are both capable of distinguishing urban areas from natural areas with accuracy about 83.1% and 80.1%, respectively.

A challenge problem for detection of targets in foliage

This paper describes a challenge problem whose scope is detection of stationary vehicles in foliage using VHF-band SAR data. The data for this challenge problem consists of images collected by the