The airborne LiDAR system, which usually integrated with optical camera, is an efficient way of acquiring 3D
geographic information and enjoys widely application in building DSM. However, when the airborne LiDAR is used in
urban area, where there are a large amount of tall buildings, the characteristic points of buildings are seldom measured
and the measured points are frequently too sparse to create precise building models. In this paper, an approach to DSM
refining DSM in urban area with fusion of airborne LiDAR point cloud data and optical imagery is put forward. Firstly,
the geometric relationship between the airborne LiDAR point and the correspondent pixel on the image synchronously
taken by optical camera is analyzed. The relative position and attitude parameters between the laser rangefinder and the
camera are determined in the process of alignment and calibration. Secondly, the building roof edges on the optical
image are extracted by edge detection. By tracing the building roof edges, the contours of building roofs in vector format
are acquired and the characteristic points of buildings are further extracted. Thirdly, all the LiDAR measured points on
the roof of specific building are separated from the point cloud data by judging the geometric relation between LiDAR
measured points and the building outline, which is represented by a polygon, according to their plane coordinates.
Finally, the DSM refinement for buildings can be implemented. All pixels representing the building roof are given
heights as same as that of nearer LiDAR point inside the polygon. Ortho-photo map and virtual building models of urban
area with higher quality can be reached with the refined DSM and optical images.