Publications de Aurélie Voisin

Résultat de la recherche dans la liste des publications :

• 1 Article
• 6 Articles de conférence
• 1 Rapport de recherche et Rapport technique

nouvelle recherche | bibtex

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Article

1 - Classification of Very High Resolution SAR Images of Urban Areas Using Copulas and Texture in a Hierarchical Markov Random Field Model. A. Voisin et V. Krylov et G. Moser et S.B. Serpico et J. Zerubia. IEEE Geoscience and Remote Sensing Letters, 2012. Note : to appear in 2013 Mots-clés : Hierarchical Markov random fields (MRFs) , Supervised classification, synthetic aperture radar (SAR), Textural features, urban areas, wavelets. @ARTICLE{Voisin13, author = {Voisin, A. and Krylov, V. and Moser, G. and Serpico, S.B. and Zerubia, J.}, title = {Classification of Very High Resolution SAR Images of Urban Areas Using Copulas and Texture in a Hierarchical Markov Random Field Model}, year = {2012}, journal = {IEEE Geoscience and Remote Sensing Letters}, note = {to appear in 2013}, url = {http://dx.doi.org/10.1109/LGRS.2012.2193869}, keyword = {Hierarchical Markov random fields (MRFs) , Supervised classification, synthetic aperture radar (SAR), Textural features, urban areas, wavelets} }

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6 Articles de conférence

1 - Change detection with synthetic aperture radar images by Wilcoxon statistic likelihood ratio test. V. Krylov et G. Moser et A. Voisin et S.B. Serpico et J. Zerubia. Dans Proc. IEEE International Conference on Image Processing (ICIP), Orlando, United States, octobre 2012. @INPROCEEDINGS{ICIP12, author = {Krylov, V. and Moser, G. and Voisin, A. and Serpico, S.B. and Zerubia, J.}, title = {Change detection with synthetic aperture radar images by Wilcoxon statistic likelihood ratio test}, year = {2012}, month = {octobre}, booktitle = {Proc. IEEE International Conference on Image Processing (ICIP)}, address = {Orlando, United States}, url = {http://hal.inria.fr/hal-00724284}, keyword = {} }

2 - Classification of multi-sensor remote sensing images using an adaptive hierarchical Markovian model. A. Voisin et V. Krylov et G. Moser et S.B. Serpico et J. Zerubia. Dans EURASIP, Bucarest, Romania, août 2012. @INPROCEEDINGS{EURASIP12, author = {Voisin, A. and Krylov, V. and Moser, G. and Serpico, S.B. and Zerubia, J.}, title = {Classification of multi-sensor remote sensing images using an adaptive hierarchical Markovian model}, year = {2012}, month = {août}, booktitle = {EURASIP}, address = {Bucarest, Romania}, url = {http://hal.inria.fr/hal-00723286}, keyword = {} }

3 - Multichannel hierarchical image classification using multivariate copulas. A. Voisin et V. Krylov et G. Moser et S.B. Serpico et J. Zerubia. Dans IS&T/SPIE Electronic Imaging – Computational Imaging X, San Francisco, United States, janvier 2012. @INPROCEEDINGS{SPIE12, author = {Voisin, A. and Krylov, V. and Moser, G. and Serpico, S.B. and Zerubia, J.}, title = {Multichannel hierarchical image classification using multivariate copulas}, year = {2012}, month = {janvier}, booktitle = {IS&T/SPIE Electronic Imaging – Computational Imaging X}, address = {San Francisco, United States}, url = {http://dx.doi.org/10.1117/12.917298}, keyword = {} }

4 - SAR image classification with non- stationary multinomial logistic mixture of amplitude and texture densities. K. Kayabol et A. Voisin et J. Zerubia. Dans Proc. IEEE International Conference on Image Processing (ICIP), pages 173-176, Brussels, Belgium, septembre 2011. Mots-clés : High resolution SAR images, Classification, Texture, Multinomial logistic, Classification EM algorithm. @INPROCEEDINGS{inria-00592252, author = {Kayabol, K. and Voisin, A. and Zerubia, J.}, title = {SAR image classification with non- stationary multinomial logistic mixture of amplitude and texture densities}, year = {2011}, month = {septembre}, booktitle = {Proc. IEEE International Conference on Image Processing (ICIP)}, pages = {173-176}, address = {Brussels, Belgium}, url = {http://hal.inria.fr/inria-00592252/en/}, keyword = {High resolution SAR images, Classification, Texture, Multinomial logistic, Classification EM algorithm} } Abstract : We combine both amplitude and texture statistics of the Synthetic Aperture Radar (SAR) images using Products of Experts (PoE) approach for classification purpose. We use Nakagami density to model the class amplitudes. To model the textures of the classes, we exploit a non-Gaussian Markov Random Field (MRF) texture model with t-distributed regression error. Non-stationary Multinomial Logistic (MnL) latent class label model is used as a mixture density to obtain spatially smooth class segments. We perform the classification Expectation-Maximization (CEM) algorithm to estimate the class parameters and classify the pixels. We obtained some classification results of water, land and urban areas in both supervised and semi-supervised cases on TerraSAR-X data.

5 - Classification bayésienne supervisée d’images RSO de zones urbaines à très haute résolution. A. Voisin et V. Krylov et J. Zerubia. Dans Proc. GRETSI Symposium on Signal and Image Processing, Bordeaux, septembre 2011. Mots-clés : Images SAR, Classification, Zones urbaines, Champs de Markov, Modeles hierarchiques. @INPROCEEDINGS{VoisinGretsi2011, author = {Voisin, A. and Krylov, V. and Zerubia, J.}, title = {Classification bayésienne supervisée d’images RSO de zones urbaines à très haute résolution}, year = {2011}, month = {septembre}, booktitle = {Proc. GRETSI Symposium on Signal and Image Processing}, address = {Bordeaux}, url = {http://hal.inria.fr/inria-00623003/fr/}, keyword = {Images SAR, Classification, Zones urbaines, Champs de Markov, Modeles hierarchiques} } Résumé : Ce papier présente un modèle de classification bayésienne supervisée d’images acquises par Radar à Synthèse d’Ouverture (RSO) très haute résolution en polarisation simple contenant des zones urbaines, particulièrement affectées par le bruit de chatoiement. Ce modèle prend en compte à la fois une représentation statistique des images RSO par modèle de mélanges finis et de copules, et une modélisation contextuelle à partir de champs de Markov hiérarchiques. Abstract : This paper deals with the Bayesian classification of single-polarized very high resolution synthetic aperture radar (SAR) images that depict urban areas. The difficulty of such a classification relies in the significant effects of speckle noise. The model considered here takes into account both statistical modeling of images via finite mixture models and copulas, and contextual modeling thanks to hierarchical Markov random fields

6 - Classification of very high resolution SAR images of urban areas by dictionary-based mixture models, copulas and Markov random fields using textural features. A. Voisin et G. Moser et V. Krylov et S.B. Serpico et J. Zerubia. Dans Proc. of SPIE (SPIE Symposium on Remote Sensing 2010), Vol. 7830, Toulouse, France, septembre 2010. Mots-clés : Images SAR, Supervised classification, Zones urbaines, Textural features, Copulas, Markov Random Fields. Copyright : SPIE @INPROCEEDINGS{7830-23, author = {Voisin, A. and Moser, G. and Krylov, V. and Serpico, S.B. and Zerubia, J.}, title = {Classification of very high resolution SAR images of urban areas by dictionary-based mixture models, copulas and Markov random fields using textural features}, year = {2010}, month = {septembre}, booktitle = {Proc. of SPIE (SPIE Symposium on Remote Sensing 2010)}, volume = {7830}, address = {Toulouse, France}, url = {http://hal.archives-ouvertes.fr/inria-00516333/en}, pdf = {http://hal.archives-ouvertes.fr/docs/00/51/63/33/PDF/Classification_of_VHR_SAR_SPIE_sept2010_Toulouse_Voisin.pdf}, keyword = {Images SAR, Supervised classification, Zones urbaines, Textural features, Copulas, Markov Random Fields} } Abstract : This paper addresses the problem of the classification of very high resolution SAR amplitude images of urban areas. The proposed supervised method combines a finite mixture technique to estimate class-conditional probability density functions, Bayesian classification, and Markov random fields (MRFs). Textural features, such as those extracted by the grey-level co-occurrency method, are also integrated in the technique, as they allow improving the discrimination of urban areas. Copula theory is applied to estimate bivariate joint class-conditional statistics, merging the marginal distributions of both textural and SAR amplitude features. The resulting joint distribution estimates are plugged into a hidden MRF model, endowed with a modified Metropolis dynamics scheme for energy minimization. Experimental results with COSMO-SkyMed images point out the accuracy of the proposed method, also as compared with previous contextual classifiers.

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Rapport de recherche et Rapport technique

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. @TECHREPORT{RR-7758, author = {Voisin, A. and Krylov, V. and Moser, G. and Serpico, S.B. and Zerubia, J.}, title = {Classification of very high resolution SAR images of urban areas}, year = {2011}, month = {octobre}, institution = {INRIA}, type = {Rapport de recherche}, number = {7758}, url = {http://hal.inria.fr/docs/00/63/10/38/PDF/RR-7758.pdf}, keyword = {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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