SAR image filtering based on the heavy-tailed Rayleigh model

AM Achim, EE Kuruoglu, J Zerubia

Research output: Contribution to journalArticle (Academic Journal)peer-review

169 Citations (Scopus)


Synthetic aperture radar (SAR) images are inherently affected by a signal dependent noise known as speckle, which is due to the radar wave coherence. In this paper, we propose a novel adaptive despeckling filter and derive a maximum a posteriori (MAP) estimator for the radar cross section (RCS). We first employ a logarithmic transformation to change the multiplicative speckle into additive noise. We model the RCS using the recently introduced heavy-tailed Rayleigh density function, which was derived based on the assumption that the real and imaginary parts of the received complex signal are best described using the alpha-stable family of distribution. We estimate model parameters from noisy observations by means of second-kind statistics theory, which relies on the Mellin transform. Finally, we compare the proposed algorithm with several classical speckle filters applied on actual SAR images. Experimental results show that the homomorphic MAP filter based on the heavy-tailed Rayleigh prior for the RCS is among the best for speckle removal.
Translated title of the contributionSAR image filtering based on the heavy-tailed Rayleigh model
Original languageEnglish
Pages (from-to)2686 - 2693
Number of pages8
JournalIEEE Transactions on Image Processing
Volume15 (9)
Publication statusPublished - Sept 2006

Bibliographical note

Publisher: IEEE


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