Name
Abstract | ||
|---|---|---|
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 Snellx2019;s method; 2) Firthx2019;s scheme; and 3) 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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/LGRS.2020.3019768 | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS |
Keywords | DocType | Volume |
Synthetic aperture radar, Maximum likelihood estimation, Numerical models, Maximum likelihood detection, Microsoft Windows, Analytical models, Bias correction, Rayleigh regression model, small-signal lengths inferences, synthetic aperture radar~(SAR) images | Journal | 19 |
Issue | ISSN | Citations |
99 | 1545-598X | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bruna G. Palm | 1 | 0 | 1.69 |
| Fábio M. Bayer | 2 | 126 | 12.89 |
| Renato J. Cintra | 3 | 218 | 26.82 |