Automated update of building information in maps from high-resolution aerial imagery is one of the most important and challenging researches in the field of photogrammetric and remote sensing. Up-to-date building information is necessary for many practical applications such as fixed assets inventory, city planning, GIS application analysis, etc. Urban land used to study and focused on building extraction and height estimation from space borne optical imagery. The advantage of such methods is a 3D visualization of urban areas, digital urban mapping, and GIS databases for decision makers. In particular, for efficient building extraction from optical multi-angular imagery, first need to remove the presence of shadows in very high resolution (VHR) images, because shadow in VHR can represent a serious obstacle for their full exploitation. This paper proposes to face this problem as a whole through the proposal of a complete processing chain for Shadow Detection and Reconstruction in VHR images. After, a template matching algorithm is formulated for automatic estimation of relative building height, and the relative height estimates are utilized in conjunction with a support vector machine (SVM)-based classifier for extraction of buildings from non-buildings. The final results are presented as a building map and an approximate 3D model of buildings building extraction. The building detection accuracy of the proposed method is improved to 88%, compared to 83% without using multi-angular information.
Building extraction, height estimation, shadow detection, shadow reconstruction, support vector machines (SVMs), very high resolution (VHR) images
