A focus on marine target detection in noise corrupted fully polarimetric synthetic aperture radar (SAR) is presented. The property of the relative phase between two cross-polarized channels reveals that the relative phases evaluated within sea surface area or noise corrupted area are widely spread phase angle region [−π,π] due to decorrelation effect; however, the relative phases are concentrated to zero and ±π for real target and its first-order azimuth ambiguities (FOAAs), respectively. Exploiting this physical behavior, the reciprocal of the mean square value of the relative phase (RMSRP) is defined as a new parameter for target detection, and the experiments based on fully polarimetric Radarsat-2 SAR images show that the strong noise and the FOAAs can be effectively suppressed in RMSRP image. Meanwhile, validity of the new parameter for target detection is also verified by two typical Radarsat-2 SAR images, in which targets’ ambiguities and strong noise are present.
A qualitative relationship between the statistical behavior of cross-polarized phase difference ϕhvvh and dominant noise type is examined based on the polarimetric noise model proposed. The noise model focusing on the covariance matrix is able to separate the multiplicative noise which only affects the amplitude from the additive noise that alters both the amplitude and phase. In the case of low noise, the phase is not affected by the noise and ϕhvvh distribution is predicted to be centered at 0 deg in terms of reciprocity theorem. The case of strong noise is much more complicated as the dominant noise type plays an important role in the statistics of ϕhvvh. The phase over the area where multiplicative noise dominates is not altered, thus the ϕhvvh distribution is expected to have similar behaviors to the case of low noise. However, the dominant additive noise would significantly affect the phase so that an obvious deviation from 0 deg for ϕhvvh distribution is expected. Experiments with Radarsat-2 full polarimetric imageries further validate this qualitative relationship.
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