Co-clustering for hyperspectral images.Report as inadecuate

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1 MODAL - MOdel for Data Analysis and Learning Inria Lille - Nord Europe, LPP - Laboratoire Paul Painlevé, CERIM - Santé publique : épidémiologie et qualité des soins-EA 2694, Polytech Lille, Université de Lille 1, IUT’A 2 ERIC - Entrepôts, Représentation et Ingénierie des Connaissances 3 LASIR - LAboratoire de Spectrochimie Infrarouge et Raman

Abstract : 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.

Author: Julien Jacques - Cyril Ruckebusch -



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