A. Voisin, V. Krylov, G. Moser, S. B. Serpico, J. Zerubia.
"Multichannel hierarchical image classification using multivariate copulas",
IS&T/SPIE Electronic Imaging 2012,
Proc. of SPIE, volume 8296, 82960K, San Francisco (USA), January 22-26, 2012.
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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.
Bibtex
@INPROCEEDINGS{VoisinSPIE12,
author = {Voisin, A. and Krylov, V. and Moser, G. and Serpico, S. B. and Zerubia, J.},
title = {Multichannel hierarchical image classification using multivariate copulas},
year = {2012},
booktitle = {Proceedings of SPIE},
volume = {8296},
address = {San Francisco, USA},
pages = {82960K}
}