Composite Kernels for Support Vector Classification of Hyper-Spectral Data

Authors:
Mojtaba Kohram;Mahd. Noor Sap
Affiliations:
Faculty of Computer Science and Information Systems, University Technology Malaysia,;Faculty of Computer Science and Information Systems, University Technology Malaysia,
Venue:
MICAI '08 Proceedings of the 7th Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
Year:
2008

Citing 6
Cited 0

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Hyperspectral Image Analysis--A Robust Algorithm Using Support Vectors and Principal Components

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Spectral Angle Based Kernels for the Classification of Hyperspectral Images Using Support Vector Machines

AMS '08 Proceedings of the 2008 Second Asia International Conference on Modelling & Simulation (AMS)
Support Vector Machine with Composite Kernels for Time Series Prediction

ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
An information-theoretic approach to spectral variability, similarity, and discrimination for hyperspectral image analysis

IEEE Transactions on Information Theory

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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.