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An assessment of support vector machines for land cover classification

International Journal of Remote Sensing

By:
, , and
DOI: 10.1080/01431160110040323

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Abstract

The support vector machine (SVM) is a group of theoretically superior machine learning algorithms. It was found competitive with the best available machine learning algorithms in classifying high-dimensional data sets. This paper gives an introduction to the theoretical development of the SVM and an experimental evaluation of its accuracy, stability and training speed in deriving land cover classifications from satellite images. The SVM was compared to three other popular classifiers, including the maximum likelihood classifier (MLC), neural network classifiers (NNC) and decision tree classifiers (DTC). The impacts of kernel configuration on the performance of the SVM and of the selection of training data and input variables on the four classifiers were also evaluated in this experiment.

Additional Publication Details

Publication type:
Article
Publication Subtype:
Journal Article
Title:
An assessment of support vector machines for land cover classification
Series title:
International Journal of Remote Sensing
DOI:
10.1080/01431160110040323
Volume
23
Issue:
4
Year Published:
2002
Language:
English
Larger Work Type:
Article
Larger Work Subtype:
Journal Article
Larger Work Title:
International Journal of Remote Sensing
First page:
725
Last page:
749
Number of Pages:
25