We describe a novel adaptive despeckling filter for Synthetic Aperture Radar (SAR) images. In the proposed approach, the Radar Cross Section (RCS) is estimated using a maximum a posteriori (MAP) criterion. We first employ a logarithmic transformation to change the multiplicative speckle into additive noise. We model the RCS using the heavy-tailed Rayleigh distribution, which was recently proposed as an accurate model for amplitude SAR images. We estimate model parameters from noisy observations by applying the 'method-of-log-cumulants', which relies on the Mellin transform. Finally, we compare our proposed algorithm with the classical Lee filtering technique applied on an aerial image and we quantify the performance improvement.
SAR image filtering based on the heavy-tailed rayleigh model
Kuruoglu EE;
2005
Abstract
We describe a novel adaptive despeckling filter for Synthetic Aperture Radar (SAR) images. In the proposed approach, the Radar Cross Section (RCS) is estimated using a maximum a posteriori (MAP) criterion. We first employ a logarithmic transformation to change the multiplicative speckle into additive noise. We model the RCS using the heavy-tailed Rayleigh distribution, which was recently proposed as an accurate model for amplitude SAR images. We estimate model parameters from noisy observations by applying the 'method-of-log-cumulants', which relies on the Mellin transform. Finally, we compare our proposed algorithm with the classical Lee filtering technique applied on an aerial image and we quantify the performance improvement.File | Dimensione | Formato | |
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