A Bayesian Joint Decorrelation and Despeckling approach for speckle reduction of SAR Images

Caifeng Wang, LinLin Xu, David Clausi, Alexander Wong


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.

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DOI: http://dx.doi.org/10.15353/vsnl.v1i1.59


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