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Evaluation of bayesian despeckling and texture extraction methods based on Gauss–Markov and auto-binomial gibbs random fields: Application to TerraSAR-X data / D. Espinoza Molina in IEEE Transactions on geoscience and remote sensing, vol 50 n° 5 Tome 2 (May 2012)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 50 n° 5 Tome 2 (May 2012) . - pp 2001 - 2025 Titre : Evaluation of bayesian despeckling and texture extraction methods based on Gauss–Markov and auto-binomial gibbs random fields: Application to TerraSAR-X data Type de document : Article/Communication Auteurs : D. Espinoza Molina, Auteur ; D. Gleich, Auteur ; M. Dactu, Auteur Année de publication : 2012 Article en page(s) : pp 2001 - 2025 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] analyse comparative [Termes IGN] analyse texturale [Termes IGN] champ aléatoire de Markov [Termes IGN] échantillonnage de Gibbs [Termes IGN] évaluation [Termes IGN] filtre de déchatoiement [Termes IGN] image radar moirée [Termes IGN] image TerraSAR-X [Termes IGN] inférence statistique [Termes IGN] texture d'image Résumé : (Auteur) Speckle hinders information in synthetic aperture radar (SAR) images and makes automatic information extraction very difficult. The Bayesian approach allows us to perform the despeckling of an image while preserving its texture and structures. This model-based approach relies on a prior model of the scene. This paper presents an evaluation of two despeckling and texture extraction model-based methods using the two levels of Bayesian inference. The first method uses a Gauss-Markov random field as prior, and the second is based on an auto-binomial model (ABM). Both methods calculate a maximum a posteriori and determine the best model using an evidence maximization algorithm. Our evaluation approach assesses the quality of the image by means of the despeckling and texture extraction qualities. The proposed objective measures are used to quantify the despeckling performances of these methods. The accuracy of modeling and characterization of texture were determined using both supervised and unsupervised classifications, and confusion matrices. Real and simulated SAR data were used during the validation procedure. The results show that both methods enhance the image during the despeckling process. The ABM is superior regarding texture extraction and despeckling for real SAR images. Numéro de notice : A2012-190 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2011.2169679 En ligne : https://doi.org/10.1109/TGRS.2011.2169679 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31637 [article]

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