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Publications sur probability density function (pdf)
Résultat de la recherche dans la liste des publications :
Article |
1 - 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. |
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