Publications de S.B. Serpico

Résultat de la recherche dans la liste des publications :

• 5 Articles
• 9 Articles de conférence
• 5 Rapports de recherche et Rapports techniques

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5 Articles

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} }

2 - Supervised High Resolution Dual Polarization SAR Image Classification by Finite Mixtures and Copulas. V. Krylov et G. Moser et S.B. Serpico et J. Zerubia. IEEE Journal of Selected Topics in Signal Processing, 5(3): pages 554-566, juin 2011. Mots-clés : Polarimetric synthetic aperture radar, Supervised classification, probability density function (pdf), dictionary-based pdf estimation, Markov random field, copula. Copyright : IEEE @ARTICLE{krylovJSTSP2011, author = {Krylov, V. and Moser, G. and Serpico, S.B. and Zerubia, J.}, title = {Supervised High Resolution Dual Polarization SAR Image Classification by Finite Mixtures and Copulas}, year = {2011}, month = {juin}, journal = { IEEE Journal of Selected Topics in Signal Processing}, volume = {5}, number = {3}, pages = {554-566}, url = {http://dx.doi.org/10.1109/JSTSP.2010.2103925}, pdf = {http://hal.archives-ouvertes.fr/inria-00562326/en/}, keyword = {Polarimetric synthetic aperture radar, Supervised classification, probability density function (pdf), dictionary-based pdf estimation, Markov random field, copula} } Abstract : In this paper a novel supervised classification approach is proposed for high resolution dual polarization (dualpol) amplitude satellite synthetic aperture radar (SAR) images. A novel probability density function (pdf) model of the dual-pol SAR data is developed that combines finite mixture modeling for marginal probability density functions estimation and copulas for multivariate distribution modeling. The finite mixture modeling is performed via a recently proposed SAR-specific dictionarybased stochastic expectation maximization approach to SAR amplitude pdf estimation. For modeling the joint distribution of dual-pol data the statistical concept of copulas is employed, and a novel copula-selection dictionary-based method is proposed. In order to take into account the contextual information, the developed joint pdf model is combined with a Markov random field approach for Bayesian image classification. The accuracy of the developed dual-pol supervised classification approach is validated and compared with benchmark approaches on two high resolution dual-pol TerraSAR-X scenes, acquired during an epidemiological study. A corresponding single-channel version of the classification algorithm is also developed and validated on a single polarization COSMO-SkyMed scene.

3 - Enhanced Dictionary-Based SAR Amplitude Distribution Estimation and Its Validation With Very High-Resolution Data. V. Krylov et G. Moser et S.B. Serpico et J. Zerubia. IEEE-Geoscience and Remote Sensing Letters, 8(1): pages 148-152, janvier 2011. Mots-clés : finite mixture models, parametric estimation, probability-density-function estimation, EM Stochastique (SEM), synthetic aperture radar. Copyright : IEEE @ARTICLE{krylovGRSL2011, author = {Krylov, V. and Moser, G. and Serpico, S.B. and Zerubia, J.}, title = {Enhanced Dictionary-Based SAR Amplitude Distribution Estimation and Its Validation With Very High-Resolution Data}, year = {2011}, month = {janvier}, journal = {IEEE-Geoscience and Remote Sensing Letters}, volume = {8}, number = {1}, pages = {148-152}, url = {http://dx.doi.org/10.1109/LGRS.2010.2053517}, pdf = {http://hal.archives-ouvertes.fr/inria-00503893/en/}, keyword = {finite mixture models, parametric estimation, probability-density-function estimation, EM Stochastique (SEM), synthetic aperture radar} } Abstract : In this letter, we address the problem of estimating the amplitude probability density function (pdf) of single-channel synthetic aperture radar (SAR) images. A novel flexible method is developed to solve this problem, extending the recently proposed dictionary-based stochastic expectation maximization approach (developed for a medium-resolution SAR) to very high resolution (VHR) satellite imagery, and enhanced by introduction of a novel procedure for estimating the number of mixture components, that permits to reduce appreciably its computational complexity. The specific interest is the estimation of heterogeneous statistics, and the developed method is validated in the case of the VHR SAR imagery, acquired by the last-generation satellite SAR systems, TerraSAR-X and COSMO-SkyMed. This VHR imagery allows the appreciation of various ground materials resulting in highly mixed distributions, thus posing a difficult estimation problem that has not been addressed so far. We also conduct an experimental study of the extended dictionary of state-of-the-art SAR-specific pdf models and consider the dictionary refinements.

4 - SAR amplitude probability density function estimation based on a generalized Gaussian model. G. Moser et J. Zerubia et S.B. Serpico. IEEE Trans. on Image Processing, 15(6): pages 1429-1442, juin 2006. Mots-clés : Images SAR, Gaussiennes generalisees, Transformee de Mellin. Copyright : IEEE @ARTICLE{moser_ieeeip05, author = {Moser, G. and Zerubia, J. and Serpico, S.B.}, title = {SAR amplitude probability density function estimation based on a generalized Gaussian model}, year = {2006}, month = {juin}, journal = {IEEE Trans. on Image Processing}, volume = {15}, number = {6}, pages = {1429-1442}, url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=1632197}, pdf = {http://hal.archives-ouvertes.fr/inria-00561372/en/}, keyword = {Images SAR, Gaussiennes generalisees, Transformee de Mellin} } Abstract : In the context of remotely sensed data analysis, an important problem is the development of accurate models for the statistics of the pixel intensities. Focusing on synthetic aperture radar (SAR) data, this modeling process turns out to be a crucial task, for instance, for classification or for denoising purposes. In this paper, an innovative parametric estimation methodology for SAR amplitude data is proposed that adopts a generalized Gaussian (GG) model for the complex SAR backscattered signal. A closed-form expression for the corresponding amplitude probability density function (PDF) is derived and a specific parameter estimation algorithm is developed in order to deal with the proposed model. Specifically, the recently proposed “method-of-log-cumulants” (MoLC) is applied, which stems from the adoption of the Mellin transform (instead of the usual Fourier transform) in the computation of characteristic functions and from the corresponding generalization of the concepts of moment and cumulant. For the developed GG-based amplitude model, the resulting MoLC estimates turn out to be numerically feasible and are also analytically proved to be consistent. The proposed parametric approach was validated by using several real ERS-1, XSAR, E-SAR, and NASA/JPL airborne SAR images, and the experimental results prove that the method models the amplitude PDF better than several previously proposed parametric models for backscattering phenomena.

5 - Dictionary-Based Stochastic Expectation-Maximization for SAR Amplitude Probability Density Function Estimation. G. Moser et J. Zerubia et S.B. Serpico. IEEE Trans. Geoscience and Remote Sensing, 44(1): pages 188-200, janvier 2006. Mots-clés : Images SAR, EM Stochastique (SEM), Dictionnaire. Copyright : IEEE @ARTICLE{moser_ieeetgrs_05, author = {Moser, G. and Zerubia, J. and Serpico, S.B.}, title = {Dictionary-Based Stochastic Expectation-Maximization for SAR Amplitude Probability Density Function Estimation}, year = {2006}, month = {janvier}, journal = {IEEE Trans. Geoscience and Remote Sensing}, volume = {44}, number = {1}, pages = {188-200}, url = {http://dx.doi.org/10.1109/TGRS.2005.859349}, pdf = {http://hal.archives-ouvertes.fr/inria-00561369/en/}, keyword = {Images SAR, EM Stochastique (SEM), Dictionnaire} } Abstract : In remotely sensed data analysis, a crucial problem is represented by the need to develop accurate models for the statistics of the pixel intensities. This paper deals with the problem of probability density function (pdf) estimation in the context of synthetic aperture radar (SAR) amplitude data analysis. Several theoretical and heuristic models for the pdfs of SAR data have been proposed in the literature, which have been proved to be effective for different land-cover typologies, thus making the choice of a single optimal parametric pdf a hard task, especially when dealing with heterogeneous SAR data. In this paper, an innovative estimation algorithm is described, which faces such a problem by adopting a finite mixture model for the amplitude pdf, with mixture components belonging to a given dictionary of SAR-specific pdfs. The proposed method automatically integrates the procedures of selection of the optimal model for each component, of parameter estimation, and of optimization of the number of components by combining the stochastic expectation–maximization iterative methodology with the recently developed “method-of-log-cumulants” for parametric pdf estimation in the case of nonnegative random variables. Experimental results on several real SAR images are reported, showing that the proposed method accurately models the statistics of SAR amplitude data.

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9 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 - 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.

5 - Multichannel SAR Image Classification by Finite Mixtures, Copula Theory and Markov Random Fields. V. Krylov et G. Moser et S.B. Serpico et J. Zerubia. Dans Proc. of Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt 2010), Vol. 1305, pages 319-326, Chamonix, France, juillet 2010. Mots-clés : multichannel SAR, Classification, probability density function estimation, Markov random field, copula. Copyright : AIP @INPROCEEDINGS{krylovMaxEnt10, author = {Krylov, V. and Moser, G. and Serpico, S.B. and Zerubia, J.}, title = {Multichannel SAR Image Classification by Finite Mixtures, Copula Theory and Markov Random Fields}, year = {2010}, month = {juillet}, booktitle = {Proc. of Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt 2010)}, volume = {1305}, pages = {319-326}, address = {Chamonix, France}, url = {http://hal.archives-ouvertes.fr/inria-00495557/en/}, pdf = {http://hal.archives-ouvertes.fr/docs/00/49/55/57/PDF/krylov_MaxEnt2010.pdf}, keyword = {multichannel SAR, Classification, probability density function estimation, Markov random field, copula} } Abstract : The last decades have witnessed an intensive development and a significant increase of interest to remote sensing, and, in particular, to synthetic aperture radar (SAR) imagery. In this paper we develop a supervised classification approach for medium and high resolution multichannel SAR amplitude images. The proposed technique combines finite mixture modeling for probability density function estimation, copulas for multivariate distribution modeling and the Markov random field approach to Bayesian image classification. The finite mixture modeling is done via a recently proposed SAR-specific dictionary-based stochastic expectation maximization approach to class-conditional amplitude probability density function estimation, which is applied separately to all the SAR channels. For modeling the class-conditional joint distributions of multichannel data the statistical concept of copulas is employed, and a dictionary-based copula selection method is proposed. Finally, the Markov random field approach enables to take into account the contextual information and to gain robustness against the inherent noise-like phenomenon of SAR known as speckle. The designed method is an extension and a generalization to multichannel SAR of a recently developed single-channel and Dual-pol SAR image classification technique. The accuracy of the developed multichannel SAR classification approach is validated on several multichannel Quad-pol RADARSAT-2 images and compared to benchmark classification techniques.

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