An SVM Multiclassifier Approach to Land Cover Mapping
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Date
2020
Journal Title
Journal ISSN
Volume Title
Publisher
ASPRS
Abstract
From the advent of the application of satellite imagery to land cover mapping, one of the growing areas of research
interest has been in the area of image classification. Image classifiers are algorithms used to extract land cover
information from satellite imagery. Most of the initial research has focussed on the development and application of
algorithms to better existing and emerging classifiers. In this paper, a paradigm shift is proposed whereby a
‘committee’ of classifiers is used to determine the final classification output. Two of the key components of an
ensemble system are that there should be diversity among the classifiers and that there should be a mechanism
through which the results are combined. In this paper, the members of the ensemble system include: Linear SVM,
Gaussian (Radial Basis Function) SVM and Quadratic SVM. The final output was determined through a simple
majority vote of the individual classifiers. From the results obtained it was observed that the final derived map
generated by an ensemble system can potentially improve on the results derived from the individual classifiers
making up the ensemble system. The ensemble system classification accuracy was, in this case, better than the linear
and quadratic SVM result. It was however less than that of the RBF SVM. Areas for further research could focus on
improving the diversity of the ensemble system used in this research.
Description
Keywords
Ensemble Systems, Support Vector Machines, Land Cover Mapping
Citation
Anthony, G., Gregg, H., & Tshilidzi, M. (2010). An SVM multiclassifier approach to land cover mapping. arXiv preprint arXiv:1007.1766.