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.
['Gustavo Camps-Valls', 'Luis Gomez-Chova', 'Jordi Muñoz-Marí', 'Joan Vila-Francés', 'Javier Calpe-Maravilla']
Composite kernels for hyperspectral image classification