Various Building Detection Methods with the Use of Image and Lidar Data

Original scientific paper In this work, an automated approach for building detection using airborne images and LIDAR data is presented. A combined approach of four methods achieved the best results, using slope-based DSM filtering as well as classification of multispectral images, elevation data and vertical LIDAR point density. The first variant of building detection is based on multispectral classification and DSM filtering. In the second variant, DSM blobs, mainly consisting of buildings and trees, are detected by subtraction of the DTM from the DSM. The third variant uses the planimetric density of raw LIDAR DTM data to detect the above-ground objects. The fourth variant is like the third one, but uses the vertical density of the raw LIDAR data (all points) to distinguish trees and buildings. In the evaluation, the combination of the four methods yields 94 % correct detection at an omission error of 12 %.