This paper presents our approach on registering aerial stereo imagery to LiDAR point cloud through iterative parallax mutual information computation. Proposed approach takes photogrammetry collinear equation as strict mathematic mode and three stages are involved: First

dense disparity map is generated from aerial stereo imagery using SGM algorithm; Second

LiDAR depth image with same size and approximate spatial resolution

geometrical distortion to the unregistered aerial image is generated

based on the perspective projection of LiDAR point cloud using initial image orientation parameters. In this step

supposed 2D rigid transformation between LiDAR depth image and the disparity map from aerial stereo imagery to be registered is estimated through maximizing their Mutual Information. As a result

by taking LiDAR depth image as a "bridge"

visible LiDAR points are roughly mapped to aerial image pixels based on the estimated geometry transform parameters and the known projection relations between Lidar point clouds and its depth image. Third

with all mapped aerial image pixels as observed values and their parallax mutual information as weight

photogrammetry space resection algorithm is implemented to obtain improved image orientation parameters. Keeping repeating above two stages until the given iteration calculation condition is met and the registration of LiDAR point clouds with aerial stereo image is realized. Selected aerial stereo imagery with size 7 216×5 428 pixel

60% overlap and spatial resolution 0.5 m are automatically geo-registered to LIDAR point cloud (averaged point distance 1.5 m

horizontal accuracy 25 cm) and approximate to 1 pixel registration precision is obtained. The experimental results show that proposed algorithm has moderate registration precision and is in high degree of automation. Theoretically

with known interior of parameters (IOP) and initial exterior of parameters (EOP)

proposed algorithm is suitable for various aerial stereo imagery taken for different types of scene and thus

has good value in application.