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), 1335-1343. (doi:10.1109/TGRS.2004.827257).


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Original Publication URL: http://dx.doi.org/10.1109/TGRS.2004.827257


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.

Item Type: Article
Digital Object Identifier (DOI): doi:10.1109/TGRS.2004.827257
ISSNs: 0196-2892 (print)
Related URLs:
Keywords: Accuracy comparison, remote sensing, supervised classification, support vector machine (SVM), training set.
Subjects: G Geography. Anthropology. Recreation > G Geography (General)
Divisions : University Structure - Pre August 2011 > School of Geography > Remote Sensing and Spatial Analysis
ePrint ID: 15436
Accepted Date and Publication Date:
June 2004Published
17 September 2003Submitted
Date Deposited: 14 Apr 2005
Last Modified: 31 Mar 2016 11:29
URI: http://eprints.soton.ac.uk/id/eprint/15436

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