SNR and Noise Variance Estimation in Polarimetric SAR Data

Thermal noise affects polarimetric measurements; but a first order noise correction can be applied if the noise variance (or power) is known. This letter deals with the estimation of the noise variance and the signal-to-noise ratio (SNR) of the cross-polarized channels in polarimetric synthetic aperture radar (SAR) data. Cramér-Rao lower bounds (CRLB) and maximum likelihood (ML) estimators are derived. The ML noise variance estimator is unbiased and efficient, while the ML SNR estimator is biased, but an unbiased SNR estimator can be derived from the biased one. It is also shown that commonly used noise variance and SNR estimators are biased. The results are finally validated using TerraSAR-X fully polarimetric data.