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Classification of wood and leaf components of trees is an essential prerequisite for deriving vital tree attributes, such as wood mass,\udleaf area index (LAI) and woody-to-total area. Laser scanning emerges to be a promising solution for such a request. Intensity\udbased approaches are widely proposed, as different components of a tree can feature discriminatory optical properties at the operating\udwavelengths of a sensor system. For geometry based methods, machine learning algorithms are often used to separate wood and leaf\udpoints, by providing proper training samples. However, it remains unclear how the chosen machine learning classifier and features used\udwould influence classification results. To this purpose, we compare four popular machine learning classifiers, namely Support Vector\udMachine (SVM), Na¨ıve Bayes (NB), Random Forest (RF), and Gaussian Mixture Model (GMM), for separating wood and leaf points\udfrom terrestrial laser scanning (TLS) data. Two trees, an <i>Erytrophleum fordii</i> and a <i>Betula pendula</i> (silver birch) are used to test the\udimpacts from classifier, feature set, and training samples. Our results showed that RF is the best model in terms of accuracy, and local\uddensity related features are important. Experimental results confirmed the feasibility of machine learning algorithms for the reliable\udclassification of wood and leaf points. It is also noted that our studies are based on isolated trees. Further tests should be performed on\udmore tree species and data from more complex environments

FEASIBILITY OF MACHINE LEARNING METHODS FOR SEPARATING WOOD AND LEAF POINTS FROM TERRESTRIAL LASER SCANNING DATA