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Publications about probability density function estimation
Result of the query in the list of publications :
Conference article |
1 - Multichannel SAR Image Classification by Finite Mixtures, Copula Theory and Markov Random Fields.
V. Krylov and G. Moser and S.B. Serpico and J. Zerubia. In Proc. of Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt 2010), Vol. 1305, pages 319-326, Chamonix, France, July 2010.
Keywords : 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 |
= |
{July}, |
| 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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