Skip to main content
Article thumbnail
Location of Repository

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 1 Unsupervised Classification of Radar Images Using Hidden Markov Chains and Hidden Markov Random Fields

By Roger Fjørtoft, Yves Delignon, Wojciech Pieczynski, Marc Sigelle and Florence Tupin


Abstract—Due to the enormous quantity of radar images acquired by satellites and through shuttle missions, there is an evident need for efficient automatic analysis tools. This paper describes unsupervised classification of radar images in the framework of hidden Markov models and generalized mixture estimation. Hidden Markov chain models, applied to a Hilbert–Peano scan of the image, constitute a fast and robust alternative to hidden Markov random field models for spatial regularization of image analysis problems, even though the latter provide a finer and more intuitive modeling of spatial relationships. We here compare the two approaches and show that they can be combined in a way that conserves their respective advantages. We also describe how the distribution families and parameters of classes with constant or textured radar reflectivity can be determined through generalized mixture estimation. Sample results obtained on real and simulated radar images are presented. Index Terms—Generalized mixture estimation, hidden Markov chains, hidden Markov random fields, radar images, unsupervised classification. I

Year: 2014
OAI identifier: oai:CiteSeerX.psu:
Provided by: CiteSeerX
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • (external link)
  • (external link)
  • Suggested articles

    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.