Wavelet filtering of SAR images based on non-Gaussian assumptions

Radar images are affected by multiplicative noise depending on the underlying signal (the ground reflectivity) due to the coherence of the radar wavelength. Images present a strong pixel to pixel variability considerably reducing the efficiency of target detection and classification algorithms. We propose in this study filtering this noise using image multiresolution analysis. The value of the wavelet coefficients of the radar reflectance is estimated by a Bayesian model by maximizing the a posteriori density and by modeling the different densities using the Pearson distributions system. The resulting filter combines a classical adaptive approach and wavelet decomposition using the local variance of the wavelet coefficients for segmenting and weighting the latter taking into account the multiplicative nature of the noise