Abstract: For the absence data existed in the airborne LiDAR point cloud, a method for identification and filling the absence data is proposed. The proposed method first detects absence data depending on binary data void image. Next the data void image is segmentd into multiple data voids by means of connected-component labeling algorithm. Then influence region of each data void is obtained by isometrically expanding the outer boundary contour based on the variance and mean curvature criterion. Finally, the optimal spatial interpolation scheme is determined related to different data void types, and then it is used to fill the data voids based on the point clouds in influence region of each data void. ISPRS LiDAR datasets, which are representative of absence data of diverse types, are used to analyze the effectiveness of "influence region" and to detemine the assess optimal spatial interpolation scheme for different data void types. And thus the feasibility and effectiveness of the proposed method is verified.

Key words: absence data, data void, filling, influence region, spatial interpolation