A relative evaluation of multi-class image classification by support vector machines

Foody, Giles M.; Mathur, Ajay

Abstract

Support vector machines (SVM) have considerable potential as classifiers of remotely sensed data. A constraint on their application in remote sensing has been their binary nature, requiring multi-class classifications to be based upon a large number of binary analyses. Here, an approach for multi-class 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 data sets 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 (p90% 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.

• Journal Article Type: Article
• Deposit Date: Feb 26, 2013
• Journal: IEEE Transactions on Geoscience and Remote Sensing
• Print ISSN: 0196-2892
• Electronic ISSN: 0196-2892
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 42
• Issue: 6
• DOI: https://doi.org/10.1109/TGRS.2004.827257
• Public URL: https://nottingham-repository.worktribe.com/output/1021476
• Publisher URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=1304900
• Additional Information: (c) 2004 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.