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Composite kernels for support vector classification of hyper-spectral data

Kohram, Mojtaba and Md Sap , Mohd Noor (2008) Composite kernels for support vector classification of hyper-spectral data. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag, Germany, pp. 360-370. ISBN 978-354088635-8

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Official URL: http://dx.doi.org/10.1007/978-3-540-88636-535

Abstract

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.

Item Type:Book Section
Additional Information:ISBN: 978-354088635-8; 7th Mexican International Conference on Artificial Intelligence, MICAI 2008; Atizapan de Zaragoza; 27 October 2008 through 31 October 2008
Uncontrolled Keywords:artificial intelligence, bionics, knowledge based systems, probability density function, remote sensing, space optics, support vector machines, vectors, composite kernels, kernel functions, land cover classifications, prior knowledges, remote sensing imageries, spectral datums, spectral signatures, support vector classifications, support vectors, SVM algorithms, classification (of information)
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:Computer Science and Information System (Formerly known)
ID Code:12518
Deposited By: Liza Porijo
Deposited On:07 Jun 2011 10:14
Last Modified:07 Jun 2011 10:14

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