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A Bayesian filtering technique for SAR interferometric phase fields.

Giancarlo Ferraiuolo1, Giovanni Poggi

  • 1Dipartimento di Ingegneria Elettronica e delle Telecomunicazioni, Università Federico II di Napoli, 21-80125 Napoli, Italy. gferraiu@unina.it

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|October 7, 2004
PubMed
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This study introduces a Bayesian approach using Markov random fields (MRF) for filtering Synthetic Aperture Radar (SAR) interferograms. The method effectively reduces noise and residues, improving phase unwrapping accuracy.

Area of Science:

  • Geophysics
  • Signal Processing
  • Remote Sensing

Background:

  • Synthetic Aperture Radar (SAR) interferograms suffer from noise, hindering accurate phase unwrapping and reconstruction.
  • Existing filtering techniques often rely on empirical strategies and prior information.

Purpose of the Study:

  • To develop a novel phase filtering method for SAR interferograms.
  • To improve phase reconstruction accuracy and facilitate phase unwrapping.

Main Methods:

  • Recasting phase filtering as a Bayesian estimation problem.
  • Modeling the image prior using a two-component Markov Random Field (MRF).
  • Employing constrained simulated annealing for optimization.

Main Results:

Related Experiment Videos

  • The MRF-based algorithm effectively filters high-frequency noise from SAR interferograms.
  • The method progressively eliminates phase residues, enabling straightforward phase unwrapping.
  • Significant noise reduction is achieved within practical processing time limits.

Conclusions:

  • The proposed Bayesian approach offers a robust solution for SAR interferogram filtering.
  • This technique enhances the reliability of phase unwrapping for various SAR applications.
  • The MRF model provides a flexible framework for noise reduction in interferometric phase data.