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Publications sur SAR image classification
Résultat de la recherche dans la liste des publications :
Article de conférence |
1 - 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
@INPROCEEDINGS{moserSPIE2010a,
|
| author |
= |
{Moser, G. and Krylov, V. and Serpico, S.B. and Zerubia, J.}, |
| title |
= |
{High resolution SAR-image classification by Markov random fields and finite mixtures}, |
| year |
= |
{2010}, |
| month |
= |
{janvier}, |
| booktitle |
= |
{Proc. of SPIE (IS&T/SPIE Electronic Imaging 2010)}, |
| volume |
= |
{7533}, |
| pages |
= |
{753308}, |
| address |
= |
{San Jose, USA}, |
| url |
= |
{http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=776565}, |
| pdf |
= |
{http://hal.archives-ouvertes.fr/inria-00442348/en/}, |
| keyword |
= |
{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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Rapport de recherche et Rapport technique |
1 - 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
@TECHREPORT{RR-7108,
|
| author |
= |
{Krylov, V. and Zerubia, J.}, |
| title |
= |
{High resolution SAR-image classification}, |
| year |
= |
{2009}, |
| month |
= |
{novembre}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{7108}, |
| url |
= |
{http://hal.archives-ouvertes.fr/inria-00433036/en/}, |
| pdf |
= |
{http://hal.archives-ouvertes.fr/docs/00/44/81/40/PDF/RR-7108.pdf}, |
| keyword |
= |
{SAR image classification, Dictionnaire, amplitude probability density, EM Stochastique (SEM), Markov random field, copula} |
| } |
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. |
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