Urban SAR image filtering exploiting Bayesian estimation theory

In this manuscript, a technique based on Bayesian estimation theory for filtering Synthetic Aperture Radar images is presented. The technique applies a Wiener filter to the available data, after an homomorphic transformation. With respect to classical a Wiener filter approach, the algorithm has two main advantages: it is able to take into account the spatial correlation among noise samples and it is able to automatically adapt the filter behavior to the image characteristics. This allows an improvement of the filter accuracy, without increasing the algorithm complexity: the good performances in terms of time consuming of Wiener filter are well preserved. Results on real datasets are reported and compared to the ones achievable using other filters existing in literature, showing the effectiveness of the approach.