A two-pass random forests classification of airborne lidar and image data on urban scenes

Random forests ensemble classifier showed to be suitable for classifying multisource data such as lidar and RGB image for urban scene mapping. However, two major problems remain: (1) the class boundaries are not well classified, a common issue in classification (2) the data are highly imbalanced raising another issue more specific to urban scenes. In this paper, we propose a new ensemble method based on the margin paradigm to improve the classification accuracy of minor classes. Random forests classifier is used in a two-pass methodology with an improved capability for classifying imbalanced data.