1 - Synthetic Aperture Radar Image Classification via Mixture Approaches.
V. Krylov and J. Zerubia. In Proc. IEEE International Conference on Microwaves, Communications, Antennas and Electronic Systems (COMCAS), Tel Aviv, Israel, November 2011.
Keywords : Synthetic Aperture Radar (SAR), remote sensing, high resolution, Classification, finite mixture models, generalized gamma distribution.
Copyright : IEEE
@INPROCEEDINGS{krylovCOMCAS11,
|
| author |
= |
{Krylov, V. and Zerubia, J.}, |
| title |
= |
{Synthetic Aperture Radar Image Classification via Mixture Approaches}, |
| year |
= |
{2011}, |
| month |
= |
{November}, |
| booktitle |
= |
{Proc. IEEE International Conference on Microwaves, Communications, Antennas and Electronic Systems (COMCAS)}, |
| address |
= |
{Tel Aviv, Israel}, |
| url |
= |
{http://www.comcas.org/pages.asp?category=042_043_}, |
| pdf |
= |
{http://hal.archives-ouvertes.fr/inria-00625551/en/}, |
| keyword |
= |
{Synthetic Aperture Radar (SAR), remote sensing, high resolution, Classification, finite mixture models, generalized gamma distribution} |
| } |
Abstract :
In this paper we focus on the fundamental synthetic aperture radars (SAR) image processing problem of supervised classification. To address it we consider a statistical finite mixture approach to probability density function estimation. We develop a generalized approach to address the problem of mixture estimation and consider the use of several different classes of distributions as the base for mixture approaches. This allows performing the maximum likelihood classification which is then refined by Markov random field approach, and optimized by graph cuts. The developed method is experimentally validated on high resolution SAR imagery acquired by Cosmo-SkyMed and TerraSAR-X satellite sensors. |
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