Weighted Support Vector Machines For Tree Species Classification Using Lidar Data

Tree species classification at individual tree crowns (ITCs) level using remote sensing data requires the availability of a sufficient number of reliable reference samples. Two main issues that affect the classification performance are: a) an imbalanced distribution of the tree species classes; and b) the presence of unreliable samples due to field collection errors, coordinates misalignments, etc. In this study, we present a weighted Support Vector Machine (WSVM) classifier that addresses these problems by considering: 1) different weights for different classes of tree species to mitigate the effects of the class imbalance distribution; and 2) different weights for different training samples according to their importance for the considered classification problem. Experimental results obtained on a study area located in the Italian Alps showed that the proposed method increased the overall and kappa accuracies of about 2%, and the mean class accuracy of about 10% with respect to a standard SVM.