Efficient modelling of SAR texture with a gamma‐inverse gamma distribution for MAP‐based speckle suppression

Abstract

The precise statistical modelling of synthetic aperture radar (SAR) texture is crucial while formulating maximum a posteriori (MAP) filter for speckle suppression. In this study, the authors introduce an SAR texture model considering the mixture of gamma and inverse gamma Γ I Γ distribution as an approximation to a generalised inverse Gaussian (GIG) distribution, which suitably portrays areas with a varying degree of heterogeneity. An estimator is proposed for the Γ I Γ model parameters using the expectation–maximisation (EM) algorithm. Cramer-Rao bounds are also derived for the Γ I Γ model parameters to evaluate the effectiveness of the EM estimator. Furthermore, the Γ I Γ model is experimentally established as an approximation to the GIG distribution through Monte Carlo simulation. Suitability and applicability of the Γ I Γ model is then validated through 1-look real clutter and multilook synthetic clutter data over textured areas. Utilising the above Γ I Γ model as prior density, the authors proposed an MAP filter for speckle suppression in SAR clutter data from areas of a diverse kind. Finally, the effectiveness of the Γ I Γ -MAP filter is assessed using different statistical measures and is found superior over the MMSE-based Lee, Kuan filters and MAP-based Γ -MAP, β -MAP, G 0 -MAP, CE-MAP filters in terms of speckle suppression and mean preservation.