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Classification of different forest types with machine learning algorithms
2016
Sabanci, K., Karamanoglu Mehmetbey Univ., Karaman (Turkey) | Uenlersen, M.F., Necmettin Erbakan Univ., Selçuklu, Konya (Turkey) | Polat, K., Abant Izzet Baysal Univ., Gölköy Yerleşkesi, Merkez, Bolu (Turkey)
In this study, forest type mapping data set taken from UCI (University of California, Irvine) machine learning repository database has been classified using different machine learning algorithms including Multilayer Perceptron, k-NN, J48, Naïve Bayes, Bayes Net and KStar. In this dataset, there are 27 spectral values showing the type of three different forests (Sugi, Hinoki, mixed broadleaf). As the performance measure criteria, the classification accuracy has been used to evaluate the classifier algorithms and then to select the best method. The best classification rates have been obtained 90.43% with MLP, and 89.1013% with k-NN classifier (for k=5). As can be seen from the obtained results, the machine learning algorithms including MLP and k-NN classifier have obtained very promising results in the classification of forest type with 27 spectral features.
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