Multichannel hierarchical image classification using multivariate copulas

Aurélie Voisin; Vladimir A. Krylov; Gabriele Moser; Sebastiano B. Serpico; Josiane Zerubia

doi:10.1117/12.917298

10 February 2012 Multichannel hierarchical image classification using multivariate copulas

Aurélie Voisin, Vladimir A. Krylov, Gabriele Moser, Sebastiano B. Serpico, Josiane Zerubia

Author Affiliations +

Proceedings Volume 8296, Computational Imaging X; 82960K (2012) https://doi.org/10.1117/12.917298
Event: IS&T/SPIE Electronic Imaging, 2012, Burlingame, California, United States

Abstract

This paper focuses on the classification of multichannel images. The proposed supervised Bayesian classification method applied to histological (medical) optical images and to remote sensing (optical and synthetic aperture radar) imagery consists of two steps. The first step introduces the joint statistical modeling of the coregistered input images. For each class and each input channel, the class-conditional marginal probability density functions are estimated by finite mixtures of well-chosen parametric families. For optical imagery, the normal distribution is a well-known model. For radar imagery, we have selected generalized gamma, log-normal, Nakagami and Weibull distributions. Next, the multivariate d-dimensional Clayton copula, where d can be interpreted as the number of input channels, is applied to estimate multivariate joint class-conditional statistics. As a second step, we plug the estimated joint probability density functions into a hierarchical Markovian model based on a quadtree structure. Multiscale features are extracted by discrete wavelet transforms, or by using input multiresolution data. To obtain the classification map, we integrate an exact estimator of the marginal posterior mode.

Citation Download Citation

Aurélie Voisin, Vladimir A. Krylov, Gabriele Moser, Sebastiano B. Serpico, and Josiane Zerubia "Multichannel hierarchical image classification using multivariate copulas", Proc. SPIE 8296, Computational Imaging X, 82960K (10 February 2012); https://doi.org/10.1117/12.917298

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