Composite Kernels for Hyperspectral Image Classification
IEEE Geoscience and Remote Sensing LettersPublished 1 January 2006
Gustau Camps‐Valls, Luis Gómez‐Chova, Jordi Muñoz-Marı́, Joan Vila‐Francés, Javier Calpe‐Maravilla
Citations1,072
SJR quartileQ1
SJR score1.26
SNIP1.37
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TL;DR
This framework of composite kernels demonstrates enhanced classification accuracy as compared to traditional approaches that take into account the spectral information only, flexibility to balance between the spatial and spectral information in the classifier, and computational efficiency.
Abstract
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.
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ChemistryEarth and Planetary SciencesEngineering
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