Use of lidar-derived NDTI and intensity for rule-based object-oriented extraction of building footprints

Buildings play an essential role in urban intra-construction, planning, and climate. The precise knowledge of building footprints not only serves as a primary source for interpreting complex urban characteristics, but also provides regional planners with more realistic and multidimensional scenarios for urban management. The recently developed airborne light detection and ranging (lidar) technology provides a very promising alternative for building-footprint measurement. In this study, lidar intensity data, a normalized digital surface model (nDSM) of the first and last returns, and the normalized difference tree index (NDTI) derived from the two returns are used to extract building footprints using rule-based object-oriented classification. The study area is chosen in London, Ontario, based on the various types of buildings surrounded by trees. An integrated segmentation approach and a hierarchical rule-based classification strategy are proposed during the process. The results indicate that the proposed object-based classification is a very effective semi-automatic method for building-footprint extraction, with buildings and trees successfully separated. An overall accuracy of 94.0% and a commission error of 6.3% with a kappa value of 0.84 are achieved. Lidar-derived NDTI and intensity data are of great importance in object-based building extraction, and the kappa value of the proposed method is double that of the object-based method without NDTI or intensity.