Weighted Support Vector Machines for Tree Species Classification Using Lidar Data

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.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