Bayesian off-line detection of multiple change-points corrupted by multiplicative noise: application to SAR image edge detection

  • Authors:
  • Jean-Yves Tourneret;Michel Doisy;Marc Lavielle

  • Affiliations:
  • IRIT/ENSEEIHT/TéSA, 2 rue Camichel, BP 7122, 31071 Toulouse Cedex 7, France;IRIT/ENSEEIHT/TéSA, 2 rue Camichel, BP 7122, 31071 Toulouse Cedex 7, France;Université Paris Sud, 91405 Orsay Cedex, France

  • Venue:
  • Signal Processing
  • Year:
  • 2003

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Abstract

This paper addresses the problem of Bayesian off-line change-point detection in synthetic aperture radar images. The minimum mean square error and maximum a posteriori estimators of the changepoint positions are studied. Both estimators cannot be implemented because of optimization or integration problems. A practical implementation using Markov chain Monte Carlo methods is proposed. This implementation requires a priori knowledge of the so-called hyperparameters. A hyperparameter estimation procedure is proposed that alleviates the requirement of knowing the values of the hyperparameters. Simulation results on synthetic signals and synthetic aperture radar images are presented.