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The Dirichlet Process Mixture Model (DPMM) is a Bayesian nonparametric approach commonly used in unsupervised learning, notable for its ability to infer the number of components in the data. In this work, DPMM is integrated with a Markov Random Field (MRF) to tackle the segmentation of Polarimetric Synthetic Aperture Radar (PolSAR) images. The MRF incorporates spatial context, enhancing segmentation accuracy. Class labels are updated using the Expectation Maximization algorithm. The proposed EM-DPMM-MRF model is evaluated on both simulated and real PolSAR images with known ground truth. The experimental results demonstrate strong and consistent performance.

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Dirichlet Process Mixture Model and Markov Random Field for PolSAR Image Segmentation

  • 会议论文

  • 发表日期：

    2026年

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  • Wassim Bdiri,
  • Nizar Bouhlel,
  • Stéphane Méric,
  • Eric Pottier,
  • Fathi Kallel

摘要

The Dirichlet Process Mixture Model (DPMM) is a Bayesian nonparametric approach commonly used in unsupervised learning, notable for its ability to infer the number of components in the data. In this work, DPMM is integrated with a Markov Random Field (MRF) to tackle the segmentation of Polarimetric Synthetic Aperture Radar (PolSAR) images. The MRF incorporates spatial context, enhancing segmentation accuracy. Class labels are updated using the Expectation Maximization algorithm. The proposed EM-DPMM-MRF model is evaluated on both simulated and real PolSAR images with known ground truth. The experimental results demonstrate strong and consistent performance.