An svm multiclassifier approach to land cover mapping
  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 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.
