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Multiscale MAP filtering of SAR images.

S Foucher¹, G B Bénié, J M Boucher

¹Centre d'Applications et de Recherche en Télédétection, Université de Sherbrooke, Sherbrooke, QC, Canada. samuel.foucher@courrier.usherb.ca

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|February 6, 2008

Summary

This summary is machine-generated.

This study introduces a new Bayesian wavelet filter to reduce multiplicative noise in Synthetic Aperture Radar (SAR) images. The method enhances image clarity for better detection and classification algorithms.

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Area of Science:

Remote Sensing
Signal Processing
Image Analysis

Background:

Synthetic Aperture Radar (SAR) images suffer from multiplicative noise due to radar wave coherence.
This noise causes significant pixel variability, hindering detection and classification algorithm efficiency.

Purpose of the Study:

To develop a novel filtering technique for noise reduction in SAR images.
To improve the performance of SAR image analysis algorithms by enhancing image quality.

Main Methods:

A multiresolution analysis using wavelet decomposition is employed for noise filtering.
A Bayesian model is used to estimate wavelet coefficients, maximizing the a posteriori probability density function.
The Pearson system of distributions models probability density functions, integrating local variance for segmentation and filtering.

Main Results:

The proposed filter effectively reduces multiplicative noise in SAR images.
The method preserves image details while smoothing noise, leading to improved signal-to-noise ratio.
The approach demonstrates enhanced performance in segmentation and classification tasks compared to traditional methods.

Conclusions:

The Bayesian wavelet filter offers a robust solution for noise reduction in SAR imagery.
This technique significantly improves the reliability and efficiency of SAR image analysis.
The method provides a valuable tool for various applications relying on high-quality SAR data.