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

  • Caifeng Wang
  • LinLin Xu
  • David Clausi
  • Alexander Wong

Abstract

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.

Published
2015-10-31
How to Cite
Wang, C., Xu, L., Clausi, D., & Wong, A. (2015). A Bayesian Joint Decorrelation and Despeckling approach for speckle reduction of SAR Images. Journal of Computational Vision and Imaging Systems, 1(1). https://doi.org/10.15353/vsnl.v1i1.59
Section
Articles