A method for data density reduction in overlapped airborne LiDAR strips

• Main Authors: Wang, L. (Author), Xu, Y. (Author), You, Z. (Author)
• Format: Article
• Language: English
• Published: Elsevier B.V. 2022
• Subjects: Airborne LIDAR; Data density; Data density reduction; Data reduction; Density reduction; DTM; Entropy; Forestry; Gaussian fitting; Light detection and ranging; Observation data; Optical radar; Overlap area; Reduction rate; Timber
• Online Access: View Fulltext in Publisher

 A partial overlap between adjacent strips during airborne Light Detection And Ranging (LiDAR) data scanning is required to ensure data integrity. The overlap area is observed two times and contains richer target details. To reduce data density yet retaining target details in the overlap area, a method based on reducing the influence of repeated observation data is presented. The proposed method defines the repeated observation data as the multiple samples of the same location from two adjacent strips, identifies them by locating the pairwise closest points from two adjacent strips with their distance below a distance threshold, and eliminates unimportant points inside the repeated observation data according to the criterion that LiDAR points with low curvature or high incidence angle can be removed without affecting the sharp features and the overall quality of data representation of the overlap area. The optimal distance threshold is adaptively determined using a Gaussian fitting model by modeling the mean distance between all the nearest neighbors in one original LiDAR strips. Finally, reduction rate and information entropy metric are put forward to quantitatively evaluate the effectiveness of the proposed method. The developed method is applied to two real airborne LiDAR datasets with urban and forestry scenes for experimental testing. The quantitative accuracy evaluation results show that reduction rate in urban and forestry area can reach to 25.1% and 12.8%, respectively. Furthermore, the data density of the overlap area could be reduced while the information entropy and DTM accuracy still can be maintained. © 2022 The Authors