Abstract
A qualitative relationship between the statistical behavior of cross-polarized phase difference ϕ hvvh 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 ϕ hvvh 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 ϕ hvvh . The phase over the area where multiplicative noise dominates is not altered, thus the ϕ hvvh 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 ϕ hvvh distribution is expected. Experiments with Radarsat-2 full polarimetric imageries further validate this qualitative relationship.
Highlights
Synthetic aperture radar (SAR) has demonstrated its advantages in oceanic applications since it is independent of weather condition and capable of monitoring natural surface in full day and night
In order to examine the relationship between the dominant noise type and the statistical behavior of φhvvh, we have tested a series of Radarsat-2 quad-pol datasets among which three typical imageries are included in this experiment
The five subareas are chosen with the rule that strong and low noise levels should both be taken into account
Summary
Synthetic aperture radar (SAR) has demonstrated its advantages in oceanic applications since it is independent of weather condition and capable of monitoring natural surface in full day and night. A complete speckle noise model for single-look PolSAR data has been presented, which is focused on the noise characteristics of all covariance matrix elements,[10] hereinafter referred to the LM noise model This model proposes that the noise can be divided into two types: multiplicative noise which only introduces noise in amplitude and additive noise which introduces noise both in amplitude and phase. The contributions of these two noise types to the total speckle noise depend on the complex correlation coefficient,[10] which determines the characteristics of PPD PDF as introduced in Ref. 5.
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