A Bayesian Joint Decorrelation and Despeckling approach for speckle reduction of SAR Images
Caifeng Wang, Linlin Xu, David A. Clausi, Alexander K.C. Wong · Vision Letters · 2015
In this paper, we present a novel approach for joint decorrelation and despeckling of synthetic aperture radar (SAR) imagery. An iterative maximum a posterior estimation is performed to obtain the correlation and speckle-free SAR data, which incorporates a correlation model which realistically explores the physical correlated process of speckle noise on signal in SAR imaging. The correlation model is determined automatically via Bayesian estimation in the log-Fourier domain and patch-wise computation is used to account for spatial nonstationarities existing in SAR data. The proposed approach is compared to a state-of-the-art despeckling technique using both simulated and real SAR data. Experimental results illustrate its improvement in preserving the structural detail, especially the sharpness of the edges, when suppressing speckle noise.