Image georeferencing using LID AR data

LIDAR technology is increasingly becoming an industry-standard tool for collecting high resolution data about physical surfaces. LIDAR is characterized by directly collecting numerical 3D coordinates of object space points. Still, the discrete and positional nature of LIDAR datasets makes it difficult to derive semantic surface information. Furthermore, reconstructed surfaces from LIDAR data lack any inherent redundancy that can be utilized to enhance the accuracy of acquired data. In comparison to LIDAR systems, photogrammetry produces surfaces rich in semantic information that can be easily identified in the captured imagery. The redundancy associated with photogrammetric intersection results in highly accurate surfaces. However, the extended amount of time needed by the photogrammetric procedure to manually identify conjugate points in overlapping images is a major disadvantage. The automation of the matching problem is still an unreliable task especially when dealing with large scale imagery over urban areas. Also, photogrammetric surface reconstruction demands adequate control in the form of control points and/or GPS/INS units. In view of the complementary characteristics of LIDAR and photogrammetric systems, a more complete surface description can be achieved through the integration of both datasets. The advantages of both systems can be fully utilized only after successful registration of the photogrammetric and LIDAR data relative to a common reference frame. The adopted registration methodology has to define a set of basic components, mainly: registration primitives, mathematical function, and similarity assessment. This paper presents the description and implementation of a registration approach that utilizes straightline features derived from both datasets as the registration primitives. LIDAR lines are used as control for the imagery and are directly incorporated in the photogrammetric triangulation. The performance analysis is based on the quality of fit between the LIDAR and photogrammetric models including derived orthophotos.