Recent advances in the availability of very high-resolution (VHR) satellite data together with efficient data acquisition and large area coverage have led to an upward trend in their applications for automatic 3-D building model reconstruction which require large-scale and frequent updates, such as disaster monitoring and urban management. Digital Surface Models (DSMs) generated from stereo satellite imagery suffer from mismatches, missing values, or blunders, resulting in rough building shape representations. To handle 3-D building model reconstruction using such low-quality DSMs, we propose a novel automatic multistage hybrid method using DSMs together with orthorectified panchromatic (PAN) and pansharpened data (PS) of multispectral (MS) satellite imagery. The algorithm consists of multiple steps including building boundary extraction and decomposition, image-based roof type classification, and initial roof parameter computation which are prior knowledge for the 3-D model fitting step. To fit 3-D models to the normalized DSM (nDSM) and to select the best one, a parameter optimization method based on exhaustive search is used sequentially in 2-D and 3-D. Finally, the neighboring building models in a building block are intersected to reconstruct the 3-D model of connecting roofs. All corresponding experiments are conducted on a dataset including four different areas of Munich city containing 208 buildings with different degrees of complexity. The results are evaluated both qualitatively and quantitatively. According to the results, the proposed approach can reliably reconstruct 3-D building models, even the complex ones with several inner yards and multiple orientations. Furthermore, the proposed approach provides a high level of automation by limiting the number of primitive roof types and by performing automatic parameter initialization.

Keywords: Digital Surface Model (DSM), Worldview-2, Orthophoto, Model-driven, Automatic 3D Roof Modelling Through the improvements of satellite sensor and matching technology, the derivation of 3D models from space borne stereo data obtained a lot of interest for various applications such as mobile navigation, urban planning, telecommunication, and tourism. The automatic reconstruction of 3D building models from space borne point cloud data is still an active research topic. The challenging problem in this field is the relatively low quality of the Digital Surface Model (DSM) generated by stereo matching of satellite data comparing to airborne LiDAR data. In order to establish an efficient method to achieve high quality models and complete automation from the mentioned DSM, in this paper a new method based on a model-driven strategy is proposed. For improving the results, refined orthorectified panchromatic images are introduced into the process as additional data. The idea of this method is based on ridge line extraction and analysing height values in direction of and perpendicular to the ridgeline direction. After applying pre-processing to the orthorectified data, some feature descriptors are extracted from the DSM, to improve the automatic ridge line detection. Applying RANSAC a line is fitted to each group of ridge points. Finally these ridge lines are refined by matching them or closing gaps. In order to select the type of roof model the heights of point in extension of the ridge line and height differences perpendicular to the ridge line are analysed. After roof model selection, building edge information is extracted from canny edge detection and parameters derived from the roof parts. Then the best model is fitted to extracted façade roofs based on detected type of model. Each roof is modelled independently and final 3D buildings are reconstructed by merging the roof models with the corresponding walls.