A comparison of algorithms for supervised classification using hyperspectral data
A.V. Kuznetsov, V.V. Myasnikov

Image Processing Systems Institute, Russian Academy of Sciences,
Samara State Aerospace University

Full text of article: Russian language.

Abstract:
The present work is concerned with the problem of selecting the best hyperspectral image (HSI) classification algorithm. There are compared the following algorithms in our paper: decision tree using cross-validation function, decision tree C4.5 (C5.0), Bayesian classifier , maximum likelihood classifier, minimizing MSE classifier, including a special case - classification on conjugation, spectral angle mapper classifier(for mean vector and nearest neighbor) and support vector machine (SVM). There are presented experimental results of these algorithms for hyperspectral images received by AVIRIS satellite and during SpecTIR project.

Key words:
hyperspectral image, decision tree, C5.0, Bayes, MSE, conjugation classification, spectral angle mapper classification, SVM.

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