This paper reports on research analysing the potential of Support Vector Machines (SVMs) for mapping vegetation from high spatial resolution Ikonos imagery. The work investigated the utility of SVMs for mapping regional scale upland vegetation using limited ground data. Additionally, it analysed the ability of SVMs to be transferred as a classifier to pixels from remote geographical locations, which were not included in the training process. The classification and transferability of SVMs was investigated when varying their design and training. Overall, the classification and transferability results of SVMs showed very promising results, highlighting their capability and suitability for use in remote sensing classification. 1
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