Coherent processing of synthetic aperture radar (SAR) data makes SAR images highly susceptible to signaldependent granular noise known as speckle. For accurate texture estimation, the maximum a posteriori (MAP) approa...
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Coherent processing of synthetic aperture radar (SAR) data makes SAR images highly susceptible to signaldependent 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 G-MAP and beta-MAP estimators for texture in 'amplitude format', considering clutter to be K-1/2 and W-1/2 distributed. The parameters associated with K1/2 and W1/2 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) distributedclutterdata where the texture is modelled using ce mixture model. For estimation of ce model parameters, they employ expectation-maximisation (EM) algorithm. Finally, the G-MAP, beta-MAP, and ce-MAP estimation of texture both in 'amplitude format' and 'intensity format' are performed on single look real SAR clutterdata 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'.
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