The accuracy of supervised classification is dependent to a large extent on the input training data. In general, the analyst aims to capture a large training set to fully describe the classes spectrally with the conventional statistical classifier in mind. However, it is not always necessary to provide a complete description of the classes if using a support vector machine (SVM) as the classifier. A key attraction of the SVM based approach to classification is that it seeks to fit an optimal hyperplane between the classes and since it uses only the training samples that lie at the edge of the class distributions in feature space (support vectors) it may require only a small training sample. The paper shows the potential of SVM of using only a fraction of the training data (support vectors) collected by the usual random scheme for a study carried in the south western part of Punjab state of India
['Ajay Mathur', 'Giles M. Foody']
Land cover classification by support vector machine: towards efficient training