This paper presents our research on registering single aerial image to a LiDAR point cloud. Given its high spatial resolution

spatial positioning accuracy

and efficiency in capturing data of physical surfaces

LiDAR has been influenced by and has significantly changed photogrammetry. The fusion of LiDAR data with aerial images offers various applications

such as DOM generation

virtual reality

city modeling

and military training

because of the complementary nature of the information provided by the two systems. However

the two datasets should be geo-registered into a common coordinate frame prior to such integration

which proves to be quite challenging in terms of either automation or accuracy. Such a challenge may be partly caused by inefficiency in the feature measurement or detection stage. For example

the identification of point of interest or straight line feature is viable and reliable in optical images but is difficult to achieve in LiDAR point clouds because of its poor discontinuity measurements. To this end

an automatic geo-registration approach based on "pin-hole "imaging simulation and iterative gradient mutual information computation is proposed to align single aerial image to discrete LiDAR point clouds. The proposed approach takes photogrammetry collinear equation as strict mathematic mode and involves three stages. First

a virtual "pin-hole"imaging process restored from aerial image orientation parameters is established on urban LiDAR point clouds to generate simulated

gray

LiDAR-depth images.The generated LiDAR-depth images are geometrically similar to aerial images. Hence

difficulties in registration caused by distinct differences in spatial resolution

perspective distortion

and size between the two types of data sources can be greatly alleviated.Second

the geometric transform parameters between LiDAR depth images and aerial images are successfully estimated with the gradient mutual information as the similarity measurement. Moreover

the image pyramid partitioning strategy is implemented to accelerate the search for parameter space. In this stage

LiDAR laser feet points can be roughly mapped on aerial image pixels on the basis of the estimated geometric transform parameters and the known projection relations between LiDAR point clouds and their depth images. Third

the photogrammetry space resection algorithm is implemented using all the mapped aerial image pixels as observed values and their gradient mutual information as weight to improve image orientation parameters. The three stages are repeated until the given iterative calculation condition is met and the LiDAR point clouds are registered with single aerial image.Selected airborne LiDAR data and an aerial image with different initial parameter values are tested with the proposed approach.Approximately 0. 5 pixel is obtained

indicating a higher registration precision compared with the ICP algorithm.（ 1） The "pinhole"simulation imaging and iterative gradient mutual information calculation successfully resolve the difficult heterologous correspondence problem between LiDAR point clouds and optical aerial images;（ 2） The photogrammetry space resection algorithm can obtain registration parameters with minimum projection errors and reliable precision evaluation by maximizing the use of intensive space information from LiDAR data and recovering optical bundles of laser beams directly.