Scheme for Characterizing Clutter Statistics in SAR Amplitude Images by Combining Two Parametric Models

Accurate knowledge of the statistical properties of synthetic aperture radar (SAR) amplitude data is essential for the processing and interpretation of SAR images. During the last few decades, parametric models have been extensively investigated for use in characterizing the statistics of SAR amplitude data. Among them, the generalized gamma distribution (
$\text{G}\Gamma \text{D}$
) has been widely applied in many fields of SAR image processing, as it has been demonstrated to be appropriate for describing the statistical behaviors of both SAR land and sea clutter. However, the
$\text{G}\Gamma \text{D}$
is not effective when the third-order sample log-cumulant of SAR data is close to zero as it cannot match the distribution of actual data. In this case, the recently proposed
${\mathcal{ G}}_{\textrm {AO}} $
model shows good performance for fitting the distribution of the actual SAR data. In this paper, we theoretically derive the analytical conditions of the
${\mathcal{ G}}_{\textrm {AO}} $
model applicability. Based on the derivations, a scheme describing the statistical behaviors of SAR pixel amplitudes is given by combining the
$\text{G}\Gamma \text{D}$
and the
${\mathcal{ G}}_{\textrm {AO}} $
model. Experiments performed on C-band RADARSAT-2 and L-band ALOS-PALSAR SAR data verify the effectiveness of the proposed scheme. The usefulness of the
${\mathcal{ G}}_{\textrm {AO}} $
model for extremely heterogeneous land clutter, for example, from urban regions, is also demonstrated.