Support vector machine based decision tree for very high resolution multispectral forest mapping

The goal of this study is the discrimination of seven tree species. As a well known approach the k-nearest neighbor classifier is compared to a support vector machine based decision tree. This classifier uses advanced support vector machines to implement a hierarchical classification scheme by combining it with decision tree induction. At each node of the decision tree a support vector machine is trained. Furthermore the impact of LIDAR differential data and kernel choice is evaluated. The effects of two separability measures and three grouping strategies in the decision tree induction on the classification results of the support vector machine based decision tree (SVMDT) are studied.