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Composite kernels for hyperspectral image classification

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5 Author(s)
Camps-Valls, G. ; Grup de Processament Digital de Senyals, Univ. de Valencia, Spain ; Gomez-Chova, L. ; Munoz-Mari, J. ; Vila-Frances, J.
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This letter presents a framework of composite kernel machines for enhanced classification of hyperspectral images. This novel method exploits the properties of Mercer's kernels to construct a family of composite kernels that easily combine spatial and spectral information. This framework of composite kernels demonstrates: 1) enhanced classification accuracy as compared to traditional approaches that take into account the spectral information only: 2) flexibility to balance between the spatial and spectral information in the classifier; and 3) computational efficiency. In addition, the proposed family of kernel classifiers opens a wide field for future developments in which spatial and spectral information can be easily integrated.

Published in:

Geoscience and Remote Sensing Letters, IEEE  (Volume:3 ,  Issue: 1 )

Date of Publication:

Jan. 2006

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