Maximum a posteriori‐based texture estimation by despeckling of SAR clutter amplitude data

Abstract

Coherent processing of synthetic aperture radar (SAR) data makes SAR images highly susceptible to signal-dependent granular noise known as speckle. For accurate texture estimation, the maximum a posteriori (MAP) approach is found to be efficient among multitude of despeckling approaches reported in the available literature. However, the formulations of MAP estimator for texture are available in ‘intensity format’. The authors formulate -MAP and -MAP estimators for texture in ‘amplitude format’, considering clutter to be and distributed. The parameters associated with and distributions are estimated using Mellin transform and second-kind statistics. Furthermore, an MAP-based filter is proposed here for mixture of Cauchy–Exponential compound Gaussian (CE-CG) distributed clutter data where the texture is modelled using CE mixture model. For estimation of CE model parameters, they employ expectation–maximisation (EM) algorithm. Finally, the -MAP, -MAP, and CE-MAP estimation of texture both in ‘amplitude format’ and ‘intensity format’ are performed on single look real SAR clutter data and also on multilook synthetic data. Experimental results illustrate the superiority of CE-MAP filter and moreover that the MAP estimation in ‘amplitude format’ achieves better mean preservation and speckle suppression compared with the same in ‘intensity format’.