Tree Species ClassicationUsing Terrestrial Photogrammetry

TL;DRAbstract

This thesis investigates how texture classification can be used to automatically classify tree species from image of bark texture. The texture analysis methods evaluated in the thesis are, grey level co-occurrence matrix (GLCM), two different wavelet texture analysis methods and finally the scale-invariant feature transform. To evaluate the methods two classifiers, a linear support vector machine (SVM) and a kernel based import vector machine (IVM) was used. The tree species that were classified were Scotch Pine and Norwegian Spruce and the auxiliary class ground. Three experiments were conducted to test the methods. The experiments used subimages of bark extracted from terrestrial photogrammetry images. For each sub-image, the X ,Y and Z coordinates were available. Thefirst experiment compared the methods by classifying each sub-image individually based on image data alone. In the second experiment the spatial data was added. Additionally feature selection was performed in both experi

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