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Feasibility of Random Forest and Support Vector Machine classifiers is tested for the discrimination of 7 types of vegetation near lake Poosjärvi in Western Finland. Four sets of features grouped as basic, textural, ICA or PCA based, and rotational features are applied. The results indicate that the Random Forest classification scheme outperforms the Support Vector Machine classifier. For both classifiers...
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