Enhancing Bark Classification with Boosted Support Vector Machines Using Bagging and Feature Selection Techniques

Tree species classification is a challenging task, especially considering the classification based on two-dimensional digital images. We believe bark images have more consistency for the classification because of the usual consistency of the tree trunk pattern over different seasons.

In our study, we utilized data from a Sony (ILCE-7M2) camera with lens: E PZ 16-50 mm to analyse four species of trees in Slovakia. The dataset consisted of 1369 cropped images (cropping the entire bark) and 527 precisely cropped images (small part of the bark) of Fagus sylvatica L., Quercus petraea (Matt.) Liebl., Picea abies (L.) H. Karst., and Abies alba Mill.). However, the ordinary photographs without cropping from the Slovak dataset were not used in the research due to additional markings and numbers on the barks.

Initially, GLCM was used as a feature extractor, and the features are used with different machine learning algorithms, including Support Vector Machine (SVM). However, it failed to provide better accuracy on SVM. Therefore we implemented Convolutional Neural Networks as a feature extractor to investigate the possibilities of boosting SVM with feature selection and Bagging techniques. The research followed by pre-processing the dataset and applying feature extraction. Then applying feature selection (Lasso, Genetic Algorithms, etc.) and bagging. The final results show that SVM algorithms are provided higher accuracy of 95% and a minimum of 84% after these. The normal method's accuracy was 67% on average. In future work, we plan to develop own kernels for SVM to boost accuracy.

When we successfully identify the best approach to classifly the tree species, we will work on the implementation to the workflows of terrestrial laser scanning, mobile laser scanning and terrestrial photogrammetry.

This abstract is based upon work from COST Action 3DForEcoTech, CA20118, supported by COST (European Cooperation in Science and Technology) and APVV-20-0391, IGA-3168.