Co-clustering for hyperspectral images.
Résumé
Clustering is often used for hyperspectral images in order to assign sets of pixels into a number of different homogeneous groups called clusters. As a result, pixels in the same cluster have similar spectra, i.e. are close to each other in a certain sense. Clustering is a core technique of the chemometrics toolbox but some limitations can be pointed for hyperspectral imaging. A first limitation of clustering is that it only considers information in the spectral dimension. Another is that it groups whole vectors. This means that if one or a few elements of the vectors differ significantly, the vectors cannot be clustered together. These limitations may result in suboptimal grouping.
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