Training set size requirements for the classification of a specific class

Foody, G.M., Mathur, A., Sanchez-Hernandez, C. and Boyd, D.S. (2006) Training set size requirements for the classification of a specific class Remote Sensing of Environment, 104, (1), pp. 1-14. (doi:10.1016/j.rse.2006.03.004).


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The design of the training stage of a supervised classification should account for the properties of the classifier to be used. Consideration of the way the classifier operates may enable the training stage to be designed in a manner which ensures that the aim of the classification is satisfied with the use of a small, inexpensive, training set. It may, therefore, be possible to reduce the training set size requirements from that generally expected with the use of standard heuristics. Substantial reductions in training set size may be possible if interest is focused on a single class. This is illustrated for mapping cotton in north-western India by support vector machine type classifiers. Four approaches to reducing training set size were used: intelligent selection of the most informative training samples, selective class exclusion, acceptance of imprecise descriptions for spectrally distinct classes and the adoption of a one-class classifier. All four 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 cotton classification remaining nearly constant at ?95% and ?97% from the user's and producer's perspectives respectively

Item Type: Article
Digital Object Identifier (DOI): doi:10.1016/j.rse.2006.03.004
ISSNs: 0034-4257 (print)
ePrint ID: 57706
Date :
Date Event
15 December 2005Submitted
Date Deposited: 11 Aug 2008
Last Modified: 16 Apr 2017 17:39
Further Information:Google Scholar

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