Aerial Lidar Data Classification using AdaBoost

We use the AdaBoost 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 92% accuracy. We also apply our classifier to our entire dataset, and present visual classification results both with and without uncertainty. We implement and experiment with several variations within the AdaBoost family of algorithms. We observe that our results are robust and stable over all the various tests and algorithmic variations. We also investigate features and values that are most critical in distinguishing between the classes. This insight is important...

• Type: Conference
• Year: 2007
• Where: 3DIM
• Authors: Suresh K. Lodha, Darren N. Fitzpatrick, David P. Helmbold
• Keywords: 3DIM 2007, Height Variation, Image Processing, Lidar Return Intensity, Lidar Scattered Height