Implication of the number of utilized images on the quality of generated building models using model-based image fitting

The importance of digital building models and their necessities in many applications have attracted the focus of research efforts in the photogrammetric and computer vision communities. Therefore, many research activities have been attempted using different data sources and processing strategies to increase the automation level and accuracy. Limitations of using data from single sensors and the rapid development of sensor technology encourages combining data from multiple sources. Thus, this paper aims at generating accurate digital building models using airborne LiDAR data with imagery by combining two processing strategies - a data-driven approach and a model-based approach. In this research, initial rectangular models are derived from LiDAR data and model parameters are refined through model-based image fitting procedure. The advantage of the proposed approach is that it can utilize any number of images and overcome the limitation of occlusions which are the most common problem when dealing with large scale imagery over urban areas. The proposed methodology is tested using different number of images and the results demonstrate that the accuracy of the models are consistent regardless of the number of images used, even when a single image is used.