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

TitleA Bayesian Joint Decorrelation and Despeckling approach for speckle reduction of SAR Images
Publication TypeJournal Article
Year of Publication2015
AuthorsWang, C., L. Xu, D. A. Clausi, and A. Wong
JournalVision Letters
Volume1
Issue1
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.

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