Robust Rayleigh Regression Method for SAR Image Processing in Presence of Outliers
This work addresses outlier robustness in SAR image processing for remote sensing applications, representing an incremental improvement over existing methods.
The paper tackles the problem of inaccurate inferences in synthetic aperture radar (SAR) image processing due to outliers and model misspecification by proposing a robust Rayleigh regression method, which reduces relative bias by 65-fold and improves sensitivity and breakdown point by about 96% and 10%, respectively, compared to non-robust estimators.
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 paper 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 non-robust 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 non-robust estimators. Moreover, two SAR data sets were used to compare the ground type and anomaly detection results of the proposed robust scheme with competing methods in the literature.