Classification of Images Using Support Vector Machines
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Date
2007
Journal Title
Journal ISSN
Volume Title
Publisher
arXiv preprint arXiv
Abstract
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 conclusions that ultimately the choice of technique adopted boils down to personal
preference and the uniqueness of the dataset at hand.
Description
Keywords
Support Vector Machines, One-against-one, One-against-All
Citation
Anthony, G., Greg, H., & Tshilidzi, M. (2007). Classification of images using support vector machines. arXiv preprint arXiv:0709.3967.