  Support Vector Machines (SVMs) are a relatively new supervised classification
technique to the land cover mapping community. They have their roots in
Statistical Learning Theory and have gained prominence because they are robust,
accurate and are effective even when using a small training sample. By their
nature SVMs are essentially binary classifiers, however, they can be adopted to
handle the multiple classification tasks common in remote sensing studies. The
two approaches commonly used are the One-Against-One (1A1) and One-Against-All
(1AA) techniques. In this paper, these approaches are evaluated in as far as
their impact and implication for land cover mapping. The main finding from this
research is that whereas the 1AA technique is more predisposed to yielding
unclassified and mixed pixels, the resulting classification accuracy is not
significantly different from 1A1 approach. It is the authors conclusion
therefore that ultimately the choice of technique adopted boils down to
personal preference and the uniqueness of the dataset at hand.
