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Speckle Noise Suppression in SAR Images Using a Three-Step Algorithm.

Ze Yu1, Wenqi Wang2, Chunsheng Li3

  • 1School of Electronic and Information Engineering, Beihang University, Beijing 100083, China. yz613@buaa.edu.cn.

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|October 31, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces an improved algorithm for synthetic aperture radar (SAR) image processing. The novel method effectively suppresses speckle noise while preserving crucial image details like edges and textures.

Keywords:
non-local filteringprobabilistic patch-based (PPB)speckle noisesynthetic aperture radar (SAR)

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Area of Science:

  • Remote Sensing
  • Image Processing

Background:

  • Speckle noise degrades Synthetic Aperture Radar (SAR) image quality.
  • Image interpretation is complicated by speckle noise, particularly around bright structures.

Purpose of the Study:

  • To develop a novel algorithm for effective speckle noise reduction in SAR images.
  • To minimize the impact of bright structures on speckle suppression performance.
  • To enhance the preservation of image edges and textures.

Main Methods:

  • A three-step approach based on the probabilistic patch-based (PPB) algorithm.
  • Step 1: Pre-processing with a linear minimum mean-square error filter and reassessing pixel similarity.
  • Step 2: Iterative method with adaptive search window size for accurate homogeneous factor estimation.
  • Step 3: Modified bias-reduction technique to correct spreading and blurring of bright structures.

Main Results:

  • The proposed algorithm demonstrates superior speckle suppression capabilities.
  • Improved preservation of image edges and textures compared to conventional methods.
  • Quantitative evaluation using equivalent number of looks, edge preservation index, and ratio image statistics.

Conclusions:

  • The novel three-step approach effectively suppresses speckle noise in SAR images.
  • The algorithm successfully minimizes interference from bright structures.
  • Enhanced image quality is achieved, preserving essential features for better interpretation.