Image Classification Using SVMs: One-against-One Vs One-against-All
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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 conclusion therefore 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., Gregg, H., & Tshilidzi, M. (2007). Image classification using SVMs: one-against-one vs one-against-all. arXiv preprint arXiv:0711.2914.