On the Model of Polarimetric SAR Calibration Using Distributed Targets

To date, several different methods for polarimetric synthetic aperture radar (SAR) calibration with distributed targets have been proposed in the literature. The basic assumptions on the distributed target that are used by these methods are almost identical. Their difference is about the assumptions on noise. In this article, the research shows that the subtle difference between the assumptions on noise leads to two different calibration models (CMs), which is the primary cause of the differences between various methods. According to the used CM, the methods in the literature can be categorized into two groups. Because this article focuses on the CMs, thus the optimal estimator in each group is used for comparison so as to exclude the impacts of different parameter estimation algorithms. The results suggest that neither of the optimal estimators is always superior to the other. In practice, we cannot determine which estimator is better, so we recommend using the mean value of the two optimal estimators (i.e., $ { \widehat {\boldsymbol \varphi }}^{\star } $ ) for calibration because it was proved to be (at least) better than the worse one. In the research, the signal-to-noise ratio (SNR) was proved to be a proper indicator for assessing whether ${ \widehat {\boldsymbol \varphi }}^{\star } $ is reliable. Hence, an estimator for the mean SNR is proposed. In this article, some simulation experiments are used to verify some critical conclusions that we have drawn. The practical use of ${ \widehat {\boldsymbol \varphi }}^{\star } $ and an assessment of its reliability with the estimated mean SNR are illustrated with DLR E-SAR data from the 2006 AgriSAR campaign.