Composite kernels for support vector classification of hyper-spectral data
The incorporation of prior knowledge into the Support Vector Machine (SVM) architecture is a problem which if solved can lead to much more accurate classifiers in the near future. This result could be particularly effective in the classification of remote sensing imagery, where an abundance of infor...
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| Main Authors: | , |
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| Format: | Book Section |
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Springer Verlag
2008
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/12518/ http://dx.doi.org/10.1007/978-3-540-88636-535 |
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| Summary: | The incorporation of prior knowledge into the Support Vector Machine (SVM) architecture is a problem which if solved can lead to much more accurate classifiers in the near future. This result could be particularly effective in the classification of remote sensing imagery, where an abundance of information is available prior to classification. The most evident method to feed prior knowledge into the SVM algorithm is through the SVM kernel function. This paper proposes several composite kernel functions designed specifically for land cover classification of remote sensing imagery. These kernels make use of the spectral signature information, inherently available in remote sensing imagery. The results achieved from these kernels are very much satisfactory and surpass all previous results produced by classical kernels. |
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