Extract Buildings Quickly from Lidar Point Cloud Data

Lidar can capture 3D geographical information quickly, and form a massive discrete point cloud.There are some shortcomings in the existing classification algorithms, such as low precision of classification and slow processing speed, especially in processing buildings close to trees. In this article, a new building extraction algorithm is introduced. The range image is abstracted from the original data and then the height texture is used for assisting classification. The authors tested the algorithm with Lidar data. The result of the experiment shows that this algorithm can distinguish adjacent buildings and trees. Also, the algorithm is good at classification precision and high speed. It has obvious advantages in the urban Lidar points cloud classification.