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Support Vector classifiers for Land Cover Classification


5. Conclusions
The objective of this study was to assess the utility of support vector classifiers for land cover classification using multi- and hyper-spectral data sets in comparison with most frequently used ML and NN classifiers. The results presented above suggest several conclusions. First the support vector classifier outperforms ML and NN classifiers in term of classification accuracy with both data sets. Several user-defined parameters affects the performance of the support vector classifier, but this study suggests that it is easier to find appropriate values for these parameters than it is for parameters defining the NN classifier. The level of classification accuracy achieved with the support vector classifier is better than both ML and NN classifiers when used with small number of training data.

Acknowledgement
The DAIS data were collected and processed by DLR and were kindly made available by Prof. J. Gumuzzio of the Autonomous University of Madrid. Computing facilities were provided by the School of Geography, University of Nottingham.

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