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Composite kernels for hyperspectral image classification
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DOI：10.1109/LGRS.2005.857031.png)
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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.
Keyword:
composite kernels
contextual
hyperspectral
image classification
kernel
spectral
support vector machine (SVM)
texture
期刊
IF:
16.4
论文数:
1.0W
被引数:
5.1K
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