An assessment of support vector machines for land cover classification
International Journal of Remote Sensing
By: C. Huang, L.S. Davis, and J.R.G. Townshend
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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.
Suggested Citation
Huang, C., Davis, L., and Townshend, J., 2002, An assessment of support vector machines for land cover classification: International Journal of Remote Sensing, v. 23, no. 4, p. 725-749, https://doi.org/10.1080/01431160110040323.
| 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 |
| Publication Date | November 25, 2010 |
| Year Published | 2002 |
| Language | English |
| Publisher | Taylor & Francis |
| Contributing office(s) | Earth Resources Observation and Science (EROS) Center |
| Description | 25 p. |
| First page | 725 |
| Last page | 749 |