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Publications de Vladimir Krylov
Résultat de la recherche dans la liste des publications :
9 Articles de conférence |
8 - High resolution SAR-image classification by Markov random fields and finite mixtures.
G. Moser et V. Krylov et S.B. Serpico et J. Zerubia. Dans Proc. of SPIE (IS&T/SPIE Electronic Imaging 2010), Vol. 7533, pages 753308, San Jose, USA, janvier 2010.
Mots-clés : SAR image classification, Dictionnaire, amplitude probability density, EM Stochastique (SEM), Markov random field, copula.
Copyright : SPIE
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{High resolution SAR-image classification by Markov random fields and finite mixtures}, |
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{http://hal.archives-ouvertes.fr/inria-00442348/en/}, |
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{SAR image classification, Dictionnaire, amplitude probability density, EM Stochastique (SEM), Markov random field, copula} |
| } |
Abstract :
In this paper we develop a novel classification approach for high and very high resolution polarimetric synthetic aperture radar (SAR) amplitude images. This approach combines the Markov random field model to Bayesian image classification and a finite mixture technique for probability density function estimation. The finite mixture modeling is done via a recently proposed dictionary-based stochastic expectation maximization approach for SAR amplitude probability density function estimation. For modeling the joint distribution from marginals corresponding to single polarimetric channels we employ copulas. The accuracy of the developed semiautomatic supervised algorithm is validated in the application of wet soil classification on several high resolution SAR images acquired by TerraSAR-X and COSMO-SkyMed. |
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9 - Dictionary-based probability density function estimation for high-resolution SAR data.
V. Krylov et G. Moser et S.B. Serpico et J. Zerubia. Dans Proc. of SPIE (IS&T/SPIE Electronic Imaging 2009), Vol. 7246, pages 72460S, San Jose, USA, janvier 2009.
Mots-clés : SAR image, Probability density function, parametric estimation, finite mixture models, EM Stochastique (SEM).
Copyright : SPIE
@INPROCEEDINGS{KrylovSPIE09,
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{Dictionary-based probability density function estimation for high-resolution SAR data}, |
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{http://hal.archives-ouvertes.fr/inria-00361384/en/}, |
| keyword |
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{SAR image, Probability density function, parametric estimation, finite mixture models, EM Stochastique (SEM)} |
| } |
Abstract :
In the context of remotely sensed data analysis, a crucial problem is represented by the need to develop accurate models for the statistics of pixel intensities. In this work, we develop a parametric finite mixture model for the statistics of pixel intensities in high resolution synthetic aperture radar (SAR) images. This method is an extension of previously existing method for lower resolution images. The method integrates the stochastic expectation maximization (SEM) scheme and the method of log-cumulants (MoLC) with an automatic technique to select, for each mixture component, an optimal parametric model taken from a predefined dictionary of parametric probability density functions (pdf). The proposed dictionary consists of eight state-of-the-art SAR- specific pdfs: Nakagami, log-normal, generalized Gaussian Rayleigh, Heavy-tailed Rayleigh, Weibull, K-root, Fisher and generalized Gamma. The designed scheme is endowed with the novel initialization procedure and the algorithm to automatically estimate the optimal number of mixture components. The experimental results with a set of several high resolution COSMO-SkyMed images demonstrate the high accuracy of the designed algorithm, both from the viewpoint of a visual comparison of the histograms, and from the viewpoint of quantitive accuracy measures such as correlation coefficient (above 99,5%). The method proves to be effective on all the considered images, remaining accurate for multimodal and highly heterogeneous scenes. |
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4 Rapports de recherche et Rapports techniques |
1 - Classification of very high resolution SAR images of urban areas.
A. Voisin et V. Krylov et G. Moser et S.B. Serpico et J. Zerubia. Rapport de recherche 7758, INRIA, octobre 2011.
Mots-clés : Synthetic Aperture Radar (SAR) image, Supervised classification, Bayesian, finite mixture models, hierarchical Markov random fields, wavelet.
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{Classification of very high resolution SAR images of urban areas}, |
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{2011}, |
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{Synthetic Aperture Radar (SAR) image, Supervised classification, Bayesian, finite mixture models, hierarchical Markov random fields, wavelet} |
| } |
Résumé :
Dans le cadre d’une approche face aux risques environnementaux, nous proposons une nouvelle méthode de classification bayésienne supervisée. Celle-ci combine une modélisation statistique des images avec une prise en compte contextuelle via des champs de Markov hiérarchiques. Ce rapport de recherche vise à détailler plus amplement cette modélisation contextuelle, à savoir expliciter le modèle mathématique sur quad-arbre et l’obtention des observations par décomposition en ondelettes de l’image originale. Il met également en exergue certaines modifications apportées en
vue d’améliorer la classification finale. |
Abstract :
In the framework of the assessment of environmental risks, we propose herein a new supervised Bayesian classification method. It combines statistical image modeling with a contextual approach via hierarchical Markov random fields. This research report aims to further focus on this kind of contextual classification approach by detailing both the quad-tree mathematical model and the statistics of the observations, obtained by wavelet transform. We therefore introduce modifications to a classical Markovian single-scale algorithm that lead to more accurate classification results. |
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2 - On the Method of Logarithmic Cumulants for Parametric Probability Density Function Estimation.
V. Krylov et G. Moser et S.B. Serpico et J. Zerubia. Rapport de Recherche 7666, INRIA, juillet 2011.
Mots-clés : Probability density function, Parameter estimation, generalized gamma distribution, K-distribution, Radar a Ouverture Synthetique (SAR), Classification.
Copyright : INRIA/ARIANA
@TECHREPORT{RR-7666,
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{On the Method of Logarithmic Cumulants for Parametric Probability Density Function Estimation}, |
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{Probability density function, Parameter estimation, generalized gamma distribution, K-distribution, Radar a Ouverture Synthetique (SAR), Classification} |
| } |
Résumé :
L'estimation de paramètres de fonctions de densité de probabilité est une étape majeure dans le domaine du traitement statistique du signal et des images. Dans ce rapport, nous étudions les propriétés et les limites de l'estimation de paramètres par la méthode des cumulants logarithmiques (MoLC), qui est une alternative à la fois au maximum de vraisemblance (MV) classique et à la méthode des moments. Nous dérivons la condition générale suffisante de consistance forte de l'estimation par la méthode MoLC, qui représente une propriété asymptotique importante de tout estimateur statistique. Grâce à cela, nous démontrons la consistance forte de l'estimation par la méthode MoLC pour une sélection de familles de distributions particulièrement adaptées (mais non restreintes) au traitement d'images acquises par radar à synthèse d'ouverture (RSO). Nous dérivons ensuite les conditions analytiques d'applicabilité de la méthode MoLC à des échantillons générés qui suivent les lois des différentes familles de distribution de notre sélection. Enfin, nous testons la méthode MoLC sur des données synthétiques et réelles afin de comparer les différentes propriétés inhérentes aux différents types d'images, l'applicabilité de la méthode et les effets d'un nombre restreint d'échantillons. Nous avons, en particulier, considéré les distributions gamma généralisée et K. Comme exemple d'application, nous avons réalisé des classifications supervisées d'images médicales à ultrason ainsi que d'images de télédétection acquises par des capteurs RSO. Les résultats obtenus montrent que la méthode MoLC est une bonne alternative à la méthode des moments, bien qu'elle contienne certaines limitations. Elle est particulièrement utile lorsqu'une approche directe par MV n'est pas possible. |
Abstract :
Parameter estimation of probability density functions is one of the major steps in the mainframe of statistical image and signal processing. In this report we explore the properties and limitations of the recently proposed method of logarithmic cumulants (MoLC) parameter estimation approach which is an alternative to the classical maximum likelihood (ML) and method of moments (MoM) approaches. We derive the general sufficient condition of strong consistency of MoLC estimates which represents an important asymptotic property of any statistical estimator. With its help we demonstrate the strong consistency of MoLC estimates for a selection of widely used distribution families originating (but not restricted to) synthetic aperture radar (SAR) image processing. We then derive the analytical conditions of applicability of MoLC to samples generated from several distribution families in our selection. Finally, we conduct various synthetic and real data experiments to assess the comparative properties, applicability and small sample performance of MoLC notably for the generalized gamma and K family of distributions. Supervised image classification experiments are considered for medical ultrasound and remote sensing SAR imagery. The obtained results suggest MoLC to be a feasible yet not universally applicable alternative to MoM that can be considered when the direct ML approach turns out to be unfeasible. |
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3 - High resolution SAR-image classification.
V. Krylov et J. Zerubia. Research Report 7108, INRIA, novembre 2009.
Mots-clés : SAR image classification, Dictionnaire, amplitude probability density, EM Stochastique (SEM), Markov random field, copula.
Copyright : INRIA/ARIANA, 2009
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{SAR image classification, Dictionnaire, amplitude probability density, EM Stochastique (SEM), Markov random field, copula} |
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Résumé :
Dans ce rapport, nous proposons une nouvelle approche pour la classification des images de type Radar à Synthèse d’Ouverture (RSO) haute résolution. Cette approche combine la méthode des champs Markoviens (MRF) pour la classification bayésienne et un modèle de mélange fini pour l’estimation des densités de probabilité. Ce modèle de mélange fini est realisé grace à une approche fondée sur une espérance-maximisation stochastique, à partir d'un dictionnaire, pour l’estimation des densités de probabilité d’amplitude. Cette approche semi-automatique est étendue au cas important des images RSO avec plusieurs polarisations, en utilisant des copulas pour modéliser les distributions jointes. Des résultats expérimentaux, sur plusieurs images RSO réelles (Dual-Pol TerraSAR-X et Single-Pol COSMO-SkyMed), pour la classification de zones humides, sont présentés pour montrer l’efficacité de l’algorithme proposé. |
Abstract :
In this report we propose a novel classification algorithm for high and very high resolution synthetic aperture radar (SAR) amplitude images that combines the Markov random field approach to Bayesian image classification and a finite mixture technique for probability density function estimation. The finite mixture modeling is done by dictionary-based stochastic expectation maximization amplitude histogram estimation approach. The developed semiautomatic algorithm is extended to an important case of multi-polarized SAR by modeling the joint distributions of channels via copulas. The accuracy of the proposed algorithm is validated for the application of wet soil classification on several high resolution SAR images acquired by TerraSAR-X and COSMO-SkyMed. |
|
4 - Modeling the statistics of high resolution SAR images.
V. Krylov et G. Moser et S.B. Serpico et J. Zerubia. Research Report 6722, INRIA, novembre 2008.
Mots-clés : Synthetic Aperture Radar (SAR) image, Probability density function, parametric estimation, finite mixture models, EM Stochastique (SEM).
Copyright : INRIA/ARIANA, 2008
@TECHREPORT{krylovDSEM08,
|
| author |
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{Krylov, V. and Moser, G. and Serpico, S.B. and Zerubia, J.}, |
| title |
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{Modeling the statistics of high resolution SAR images}, |
| year |
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{2008}, |
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{novembre}, |
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{INRIA}, |
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{6722}, |
| url |
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{http://hal.archives-ouvertes.fr/inria-00342681/en/}, |
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{http://hal.archives-ouvertes.fr/docs/00/35/76/27/PDF/RR-6722.pdf}, |
| keyword |
= |
{Synthetic Aperture Radar (SAR) image, Probability density function, parametric estimation, finite mixture models, EM Stochastique (SEM)} |
| } |
Abstract :
In the context of remotely sensed data analysis, a crucial problem is represented by the need to develop accurate models for the statistics of pixel intensities. In this work, we develop a parametric finite mixture model for modelling the statistics of intensities in high resolution Synthetic Aperture Radar (SAR) images. Along with the models we design an efficient parameter estimation scheme by integrating the Stochastic Expectation Maximization scheme and the Method of log-cumulants with an automatic technique to select, for each mixture component, an optimal parametric model taken from a predefined dictionary of parametric probability density functions (pdf). In particular, the proposed dictionary consists of eight most efficient state-of-the-art SAR-specific pdfs: Nakagami, log-normal, generalized Gaussian Rayleigh, Heavy-tailed Rayleigh, Weibull, K-root, Fisher and generalized Gamma. The experiment results with a set of several real SAR (COSMO-SkyMed) images demonstrate the high accuracy of the designed algorithm, both from the viewpoint of a visual comparison of the histograms, and from the viewpoint of quantitive measures such as correlation coefficient (always above 99,5%) . We stress, in particular, that the method proves to be effective on all the considered images, remaining accurate for multimodal and highly heterogeneous images. |
|
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Article de collection ou Chapitre de livre |
1 - Probability Density Function Estimation for Classification of High Resolution SAR Images.
V. Krylov et G. Moser et S. Serduc et J. Zerubia. Dans Signal Processing for Remote Sensing, Second Edition, pages 339-363, Ed. C. Chen., Publ. Taylor & Francis, février 2012.
@INCOLLECTION{Taylor12,
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| author |
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| title |
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{Probability Density Function Estimation for Classification of High Resolution SAR Images}, |
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{2012}, |
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{https://hal.inria.fr/hal-00729044/document}, |
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{} |
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