Unsupervised classification of radar images using hidden Markov chains and hidden Markov random fields
| Autor: | M. Sigelle, Yves Delignon, Florence Tupin, Wojciech Pieczynski, Roger Fjortoft |
|---|---|
| Přispěvatelé: | Norwegian Computing Center (NR), Communications, Images et Traitement de l'Information (CITI), Institut Mines-Télécom [Paris] (IMT)-Télécom SudParis (TSP), Laboratoire Traitement et Communication de l'Information (LTCI), Télécom ParisTech-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS), Département Traitement du Signal et des Images (TSI), Centre National de la Recherche Scientifique (CNRS)-Télécom ParisTech, Institut TELECOM/TELECOM Lille1, Institut Mines-Télécom [Paris] (IMT), Laboratoire d'Automatique, Génie Informatique et Signal (LAGIS), Université de Lille, Sciences et Technologies-Centrale Lille-Centre National de la Recherche Scientifique (CNRS) |
| Jazyk: | angličtina |
| Rok vydání: | 2003 |
| Předmět: |
Synthetic aperture radar
Unsupervised classification Computer science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ComputerApplications_COMPUTERSINOTHERSYSTEMS 02 engineering and technology Markov model Regularization (mathematics) law.invention law Radar imaging 0202 electrical engineering electronic engineering information engineering Computer vision Electrical and Electronic Engineering Radar Generalized mixture estimation Hidden Markov model Radar images Random field Hidden Markov random fields Contextual image classification business.industry Maximum-entropy Markov model Variable-order Markov model 020206 networking & telecommunications Pattern recognition Hidden Markov chains General Earth and Planetary Sciences 020201 artificial intelligence & image processing Artificial intelligence Hidden Markov random field business [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing |
| Zdroj: | IEEE Transactions on Geoscience and Remote Sensing IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2003, 41 (3), pp.675-686. ⟨10.1109/TGRS.2003.809940⟩ |
| ISSN: | 0196-2892 |
| DOI: | 10.1109/TGRS.2003.809940⟩ |
| Popis: | International audience; 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 |
| Databáze: | OpenAIRE |
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