Paper Info
Title
Supervised High-Resolution Dual-Polarization SAR Image Classification by Finite Mixtures and Copulas
Abstract
In this paper, a novel supervised classification approach is proposed for high-resolution dual-polarization (dual-pol) 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 dictionary-based 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 dictionary-based copula-selection method 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.
Year
DOI
Venue
2011
10.1109/JSTSP.2010.2103925
Selected Topics in Signal Processing, IEEE Journal of
Keywords
Field
DocType
Bayes methods,Markov processes,geophysical image processing,image classification,radar imaging,spaceborne radar,synthetic aperture radar,Bayesian image classification,Markov random field approach,PDF model,contextual information,dictionary-based copula-selection method,dual-polarization amplitude satellite synthetic aperture radar images,epidemiological study,finite mixture modeling,high-resolution dual-pol TerraSAR-X scenes,multivariate distribution modeling,probability density function model,single polarization COSMO-SkyMed scene,stochastic expectation maximization approach,supervised high-resolution dual-polarization SAR image classification,Copula,Markov random field,dictionary-based pdf estimation,polarimetric synthetic aperture radar,probability density function (pdf),supervised classification
Data modeling,Joint probability distribution,Pattern recognition,Markov random field,Computer science,Expectation–maximization algorithm,Synthetic aperture radar,Artificial intelligence,Contextual image classification,Probability density function,Marginal distribution
Journal
Volume
Issue
ISSN
5
3
1932-4553
Citations 
PageRank 
References 
28
1.24
27
Authors
4
Name
Order
Citations
PageRank
Vladimir A. Krylov113314.81
Gabriele Moser291976.92
Serpico, S.B.356048.52
Josiane Zerubia4492.24