Automated identification of tree species from images of the bark , leaves and needles

In this paper a method for the automated identification of tree species from images of leaves, bark and needles is presented. The automated identification of leaves uses local features to avoid segmentation. For the automated identification of images of the bark this method is compared to a combination of GLCM and wavelet features. For classification a Support Vector machine is used. The needle images are analyzed for features which can be used for classification. The proposed method is evaluated on a dataset provided by the “Österreichische Bundesforste AG” (“Austrian federal forests”). The dataset contains 1183 images of the most common Austrian trees. The classification rate of the bark dataset was 69.7%.

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