Name
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. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1117/12.838594 | COMPUTATIONAL IMAGING VIII |
Keywords | Field | DocType |
SAR image classification, dictionary, amplitude probability density, stochastic expectation maximization, Markov random field, copula | Joint probability distribution,Pattern recognition,Markov model,Markov random field,Synthetic aperture radar,Expectation–maximization algorithm,Markov chain,Artificial intelligence,Contextual image classification,Probability density function,Physics | Conference |
Volume | ISSN | Citations |
7533 | 0277-786X | 2 |
PageRank | References | Authors |
0.53 | 16 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gabriele Moser | 1 | 919 | 76.92 |
| Vladimir A. Krylov | 2 | 133 | 14.81 |
| Sebastiano B. Serpico | 3 | 749 | 64.86 |
| Josiane Zerubia | 4 | 2032 | 232.91 |