Robust Rayleigh Regression Method for SAR Image Processing in Presence of Outliers

The presence of outliers (anomalous values) in synthetic aperture radar (SAR) data and the misspecification in statistical image models may result in inaccurate inferences. To avoid such issues, the Rayleigh regression model based on a robust estimation process is proposed as a more realistic approa...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2022, Vol.60, p.1-12
Main Authors: Palm, Bruna G., Bayer, Fabio M., Machado, Renato, Pettersson, Mats I., Vu, Viet T., Cintra, Renato J.
Format: Article
Language:English
Subjects:
Anomalies
CLASSIFICATION
Data models
Estimators
Image processing
Mathematical models
Maximum likelihood detection
Maximum likelihood estimation
Maximum likelihood method
Monte Carlo simulation
Numerical models
Outliers
Outliers (statistics)
Parameter estimation
Parameter robustness
Radar polarimetry
Rayleigh regression model
Regression models
robust estimation
Robustness (mathematics)
SAR (radar)
Sensitivity analysis
Statistical analysis
Statistical methods
Synthetic aperture radar
synthetic aperture radar (SAR) images
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Summary:The presence of outliers (anomalous values) in synthetic aperture radar (SAR) data and the misspecification in statistical image models may result in inaccurate inferences. To avoid such issues, the Rayleigh regression model based on a robust estimation process is proposed as a more realistic approach to model this type of data. This article aims at obtaining Rayleigh regression model parameter estimators robust to the presence of outliers. The proposed approach considered the weighted maximum likelihood method and was submitted to numerical experiments using simulated and measured SAR images. Monte Carlo simulations were employed for the numerical assessment of the proposed robust estimator performance in finite signal lengths, their sensitivity to outliers, and the breakdown point. For instance, the nonrobust estimators show a relative bias value 65-fold larger than the results provided by the robust approach in corrupted signals. In terms of sensitivity analysis and break down point, the robust scheme resulted in a reduction of about 96% and 10%, respectively, in the mean absolute value of both measures, in compassion to the nonrobust estimators. Moreover, two SAR datasets were used to compare the ground type and anomaly detection results of the proposed robust scheme with competing methods in the literature.
ISSN:0196-2892
1558-0644
1558-0644
DOI:10.1109/TGRS.2021.3105694