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A relative evaluation of multiclass image classification by support vector machines

Foody, G.M. and Mathur, A. (2004) A relative evaluation of multiclass image classification by support vector machines IEEE Transactions on Geoscience and Remote Sensing, 42, (6), pp. 1335-1343. (doi:10.1109/TGRS.2004.827257).

Record type: Article

Abstract

Support vector machines (SVMs) have considerable potential as classifiers of remotely sensed data. A constraint on their application in remote sensing has been their binary nature, requiring multiclass classifications to be based upon a large number of binary analyses. Here, an approach for multiclass classification of airborne sensor data by a single SVM analysis is evaluated against a series of classifiers that are widely used in remote sensing, with particular regard to the effect of training set size on classification accuracy. In addition to the SVM, the same datasets were classified using a discriminant analysis, decision tree, and multilayer perceptron neural network. The accuracy statements of the classifications derived from the different classifiers were compared in a statistically rigorous fashion that accommodated for the related nature of the samples used in the analyses. For each classification technique, accuracy was positively related with the size of the training set. In general, the most accurate classifications were derived from the SVM approach, and with the largest training set the SVM classification was significantly (p < 0.05)more accurate (93.75%) than that derived from the discriminant analysis (90.00%) and decision tree algorithms (90.31%). Although each classifier could yield a very accurate classification, > 90% correct, the classifiers differed in the ability to correctly label individual cases and so may be suitable candidates for an ensemble-based approach to classification.

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More information

Submitted date: 17 September 2003
Published date: June 2004
Keywords: Accuracy comparison, remote sensing, supervised classification, support vector machine (SVM), training set.

Identifiers

Local EPrints ID: 15436
URI: http://eprints.soton.ac.uk/id/eprint/15436
ISSN: 0196-2892
PURE UUID: d7291d27-b86c-43e6-894a-5d2625a11a88

Catalogue record

Date deposited: 14 Apr 2005
Last modified: 17 Jul 2017 16:49

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Contributors

Author: G.M. Foody
Author: A. Mathur

University divisions

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