Multiscale MAP filtering of SAR images

Synthetic aperture radar (SAR) images are disturbed by a multiplicative noi se depending on the signal (the ground reflectivity) due to the radar wave coherence, Images have a strong variability from one pixel to another reduc ing essentially the efficiency of the algorithms of detection and classific ation, In this study, we propose to filter this noise with a multiresolutio n analysis of the image. The wavelet coefficient of the reflectivity is est imated with a Bayesian model, maximizing the a posteriori probability densi ty function. The different probability density function are modeled with th e Pearson system of distributions, The resulting filter combines the classi cal adaptive approach with wavelet decomposition where the local variance o f high-frequency images is used in order to segment and filter wavelet coef ficients.