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Unsupervised classification of radar images using hidden Markov chains and hidden Markov random fields

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
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https://hal.archives-ouvertes.fr/hal-01347239
Contributor : Médiathèque Télécom Sudparis & Institut Mines-Télécom Business School <>
Submitted on : Wednesday, July 20, 2016 - 4:14:51 PM
Last modification on : Thursday, March 4, 2021 - 6:24:03 PM

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Roger Fjortoft, Wojciech Pieczynski, Marc Sigelle, Florence Tupin, Yves Delignon. Unsupervised classification of radar images using hidden Markov chains and hidden Markov random fields. IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2003, 41 (3), pp.675 - 686. ⟨10.1109/TGRS.2003.809940⟩. ⟨hal-01347239⟩

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