Aerial lidar data classification using expectation-maximization

Abstract

We use the Expectation-Maximization (EM) algorithm to classify 3D aerial lidar scattered height data into four categories: road, grass, buildings, and trees. To do so we use five features: height, height variation, normal variation, lidar return intensity, and image intensity. We also use only lidar-derived features to organize the data into three classes (the road and grass classes are merged). We apply and test our results using ten regions taken from lidar data collected over an area of approximately eight square miles, obtaining higher than 94% accuracy. We also apply our classifier to our entire dataset, and present visual classification results both with and without uncertainty. We use several approaches to evaluate the parameter and model choices possible when applying EM to our data. We observe that our classification results are stable and robust over the various subregions of our data which we tested. We also compare our results here with previous classification efforts using this data.