Orthophoto production aims at the elimination of sensor tilt and terrain relief effects from captured perspective imagery. Uniform scale and the absence of relief displacement in orthophotos make them an important component of GIS databases, where the user can directly determine geographic locations, measure distances, compute areas, and derive other useful information about the area in question. Differential rectification has been traditionally used for orthophoto generation. For large scale imagery over urban areas, differential rectification produces serious artifacts in the form of double mapped areas at object space locations with sudden relief variations, e.g., in the vicinity of buildings. Such artifacts are removed through true orthophoto generation methodologies which are based on the identification of occluded portions of the object space in the involved imagery. Existing methodologies suffer from several problems such as their sensitivity to the sampling interval of the digital surface model (DSM) as it relates to the ground sampling distance (GSD) of the imaging sensor. Moreover, current methodologies rely on the availability of a digital building model (DBM), which requires an additional and expensive pre-processing. This paper presents new methodologies for true orthophoto generation while circumventing the problems associated with existing techniques. The feasibility and performance of the suggested techniques are verified through experimental results with simulated and real data.
This research is concerned with a methodology for automated generation of polyhedral building models for complex structures, whose rooftops are bounded by straight lines. The process starts by utilizing LiDAR data for building hypothesis generation and derivation of individual planar patches constituting building rooftops. Initial boundaries of these patches are then refined through the integration of LiDAR and photogrammetric data and hierarchical processing of the planar patches. Building models for complex structures are finally produced using the refined boundaries. The performance of the developed methodology is evaluated through qualitative and quantitative analysis of the generated building models from real data.
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