A. Voisin, V. Krylov, G. Moser, S. B. Serpico, J. Zerubia. "Classification of Multi-Sensor Remote Sensing Images Using an Adaptive Hierarchical Markovian Model",
European Signal Processing Conference 2012,
Proc. of IEEE EUSIPCO 2012, pp. 2511-2515, Bucharest (Romania), August 27-31, 2012.
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Abstract
In this paper, we propose a novel method for the classification of the multi-sensor remote sensing imagery, which represents a vital and fairly unexplored classification problem. The proposed classifier is based on an explicit hierarchical graphbased model sufficiently flexible to deal with multi-source coregistered datasets at each level of the graph. The suggested supervised method relies on a two-step technique. In the first step, a joint statistical model is developed for the input images that consists of the finite mixtures of automatically chosen parametric families for single images, and multivariate copulas to model joint class-conditional statistics at each resolution. As a second step, we plug the estimated joint probability density functions into a hierarchical Markovian model based on a quad-tree structure. Multi-scale features correspond to different resolution images or are extracted by discrete wavelet transforms. To obtain the classification map, we resort to 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 = {Classification of Multi-Sensor Remote Sensing Images Using an Adaptive Hierarchical {M}arkovian Model},
year = {2012},
booktitle = {Proc. of IEEE EUSIPCO},
address = {Bucharest, Romania},
pages = {2511--2515}
}