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SAR image filtering based on the heavy-tailed Rayleigh model.

Alin Achim1, Ercan E Kuruoğlu, Josiane Zerubia

  • 1Signal Processing Group, Department of Electrical and Electronic Engineering, University of Bristol, BS8 1UB Bristol, UK. alin.achim@bristol.ac.uk

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|September 5, 2006
PubMed
Summary

This study introduces a new adaptive filter to reduce speckle noise in Synthetic Aperture Radar (SAR) images. The proposed homomorphic Maximum a Posteriori (MAP) filter effectively enhances radar cross-section (RCS) estimation, outperforming traditional methods.

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Area of Science:

  • Remote Sensing
  • Signal Processing
  • Image Analysis

Background:

  • Synthetic Aperture Radar (SAR) images suffer from signal-dependent speckle noise due to radar wave coherence.
  • Speckle noise degrades image quality and complicates the analysis of radar cross-section (RCS).

Purpose of the Study:

  • To develop a novel adaptive despeckling filter for SAR images.
  • To derive a Maximum a Posteriori (MAP) estimator for RCS incorporating a heavy-tailed distribution model.

Main Methods:

  • Logarithmic transformation to convert multiplicative speckle to additive noise.
  • Modeling RCS using a heavy-tailed Rayleigh density function derived from alpha-stable distributions.
  • Parameter estimation using second-kind statistics and the Mellin transform.

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Main Results:

  • The proposed homomorphic MAP filter effectively reduces speckle in SAR images.
  • The filter demonstrates superior performance compared to several classical despeckling algorithms.
  • Accurate estimation of radar cross-section (RCS) is achieved.

Conclusions:

  • The novel homomorphic MAP filter provides significant improvements in SAR image despeckling.
  • The heavy-tailed Rayleigh prior model is well-suited for RCS in the presence of speckle.
  • This approach offers a robust solution for enhancing SAR image analysis and interpretation.