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Bayesian Wavelet Shrinkage With Heterogeneity-Adaptive Threshold for SAR Image Despeckling Based on Generalized Gamma Distribution | IEEE Journals & Magazine | IEEE Xplore

Bayesian Wavelet Shrinkage With Heterogeneity-Adaptive Threshold for SAR Image Despeckling Based on Generalized Gamma Distribution


Abstract:

Synthetic aperture radar (SAR) images are inherently affected by multiplicative speckle noise, which will degrade the human interpretation and computer-aided scene analys...Show More

Abstract:

Synthetic aperture radar (SAR) images are inherently affected by multiplicative speckle noise, which will degrade the human interpretation and computer-aided scene analysis. In this paper, we propose a novel Bayesian multiscale method for SAR image despeckling in the non-homomorphic framework. To address the multiplicative nature, we first make the speckle contribution additive by a linear decomposition. Then, in the stationary wavelet transform domain, a two-sided generalized Gamma distribution (GTD) is introduced as a prior to capture the heavy-tailed nature of wavelet coefficients of the noise-free reflectivity. By exploiting this prior together with a Gaussian likelihood, an analytical wavelet shrinkage function is derived based on maximum a posteriori criteria, which further adopts heterogeneity-adaptive thresholding technique to achieve better estimates of noise-free wavelet coefficients. Moreover, a pilot-signal-assisted strategy is proposed to estimate the parameters of two-sided GTD with the estimator based on second-kind cumulants. Finally, experimental results, carried out on the synthetic and actual SAR images, are given to demonstrate the validity of the proposed despeckling method.
Published in: IEEE Transactions on Geoscience and Remote Sensing ( Volume: 51, Issue: 4, April 2013)
Page(s): 2388 - 2402
Date of Publication: 14 September 2012

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