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Proceedings Paper

Issues in training SVM classifications
Author(s): Giles M. Foody; Ajay Mathur; Carolina Sanchez-Hernandez; Doreen S. Boyd
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Paper Abstract

SVM classification has great potential in remote sensing. The nature of SVM classification also provides opportunities for accurate classification from relatively small training sets, especially if interest is focused on a single class. Five approaches to reducing training set size from that suggested by conventional heuristics are discussed: intelligent selection of the most informative training samples, selective class exclusion, acceptance of imprecise descriptions for spectrally distinct classes, the adoption of a one-class classifier and a focus on boundary regions. All five approaches were able to reduce the training set size required considerably below that suggested by conventional widely used heuristics without significant impact on the accuracy with which the class of interest was classified. For example, reductions in training set size of ~90% from that suggested by a conventional heuristic are reported with the accuracy of the class of interest remaining nearly constant at ~95% and ~97% from the user's and producer's perspectives respectively.

Paper Details

Date Published: 6 October 2006
PDF: 15 pages
Proc. SPIE 6365, Image and Signal Processing for Remote Sensing XII, 63650Q (6 October 2006); doi: 10.1117/12.695460
Show Author Affiliations
Giles M. Foody, Univ. of Southampton (United Kingdom)
Ajay Mathur, Punjab Remote Sensing Ctr. (India)
Carolina Sanchez-Hernandez, Ordnance Survey (United Kingdom)
Doreen S. Boyd, Bournemouth Univ. (United Kingdom)

Published in SPIE Proceedings Vol. 6365:
Image and Signal Processing for Remote Sensing XII
Lorenzo Bruzzone, Editor(s)

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