Support Vector classifiers for Land Cover Classification

Details Support Vector classifiers for Land Cover Classification Support Vector classifiers for Land Cover Classification

2008-02-15

arxiv.org/abs/0802.2138v1

Computer Science

Neural and Evolutionary Computing

11 pages, 1 figure, Published in MapIndia Conference 2003

Scientific paper

10.1080/01431160802007624

Support vector machines represent a promising development in machine learning research that is not widely used within the remote sensing community. This paper reports the results of Multispectral(Landsat-7 ETM+) and Hyperspectral DAIS)data in which multi-class SVMs are compared with maximum likelihood and artificial neural network methods in terms of classification accuracy. Our results show that the SVM achieves a higher level of classification accuracy than either the maximum likelihood or the neural classifier, and that the support vector machine can be used with small training datasets and high-dimensional data.

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