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Fast SAR image restoration, segmentation, and detection of high-reflectance regions / E. Bratsolis in IEEE Transactions on geoscience and remote sensing, vol 41 n° 12 (December 2003)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 41 n° 12 (December 2003) . - pp 2890 - 2899 Titre : Fast SAR image restoration, segmentation, and detection of high-reflectance regions Type de document : Article/Communication Auteurs : E. Bratsolis, Auteur ; M. Sigelle, Auteur Année de publication : 2003 Article en page(s) : pp 2890 - 2899 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] champ aléatoire de Markov [Termes IGN] chatoiement [Termes IGN] classification [Termes IGN] filtre numérique [Termes IGN] histogramme [Termes IGN] image ERS-SAR [Termes IGN] itération [Termes IGN] réflectance [Termes IGN] restauration d'image [Termes IGN] segmentation d'image Résumé : (Auteur) An iterative filter that can be used for speckle reduction and restoration of synthetic aperture radar (SAR) images is presented here. This method can be considered as a first step in the extraction of other important information. The second step is the detection of high-reflectance regions and continues with the segmentation of the total image. We have worked in three-look simulated and real European Remote Sensing 1 satellite amplitude images. The iterative filter is based on a membrane model Markov random field approximation optimized by a synchronous local iterative method. The final form of restoration gives a total sum-preserving regularization for the pixel values of our image. The high-reflectance regions are defined as the brightest regions of the restored image. After the separation of this extreme class, we give a fast segmentation method using the histogram of the restored image. Numéro de notice : A2003-383 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2003.817222 En ligne : https://doi.org/10.1109/TGRS.2003.817222 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26463 [article]

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065-03121	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Unsupervised classification of radar images using hidden Markov chains and hidden Markov random fields / R. Fjortoft in IEEE Transactions on geoscience and remote sensing, vol 41 n° 3 (March 2003)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 41 n° 3 (March 2003) . - pp 675 - 686 Titre : Unsupervised classification of radar images using hidden Markov chains and hidden Markov random fields Type de document : Article/Communication Auteurs : R. Fjortoft, Auteur ; Y. Delignon, Auteur ; W. Pieczynski, Auteur ; M. Sigelle, Auteur ; Florence Tupin, Auteur Année de publication : 2003 Article en page(s) : pp 675 - 686 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] chaîne de Markov [Termes IGN] champ aléatoire de Markov [Termes IGN] classification non dirigée [Termes IGN] estimation statistique [Termes IGN] image radar Résumé : (Auteur) 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 he determined through generalized mixture estimation. Sample results obtained on real and simulated radar images are presented. Numéro de notice : A2003-119 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2003.809940 En ligne : https://doi.org/10.1109/TGRS.2003.809940 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22415 [article]

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Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-03031	RAB	Revue	Centre de documentation	En réserve L003	Disponible