Assessment of the effectiveness of support vector machines for hyperspectral data

Authors:
Mahesh Pal;P. M. Mather
Affiliations:
School of Geography, University of Nottingham, Nottingham NG7 2RD, UK;School of Geography, University of Nottingham, Nottingham NG7 2RD, UK
Venue:
Future Generation Computer Systems - Special issue: Geocomputation
Year:
2004

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Abstract

Support vector machines (SVMs) have recently been introduced into machine learning for pattern recognition. In this paper, a multi-class SVM is used for classification of DAIS hyperspectral remotely sensed data. Results show that the SVM performs better than maximum likelihood, univariate decision tree and backpropagation neural network classifiers, even with small training data sets, and is almost unaffected by the Hughes phenomenon [IEEE Trans. Inform. Theory IT-14 (1968) 55].