Neighborhood systems for airborne laser data

Analysis of common neighborhood definitions for airborne laser data, triangulation or roster-based, reveals deficiencies in modeling the measured objects. Concepts that originate from 2D data structures are used for modeling complex 3D objects and for handling datasets with different point densities. Realizing these shortcomings, this paper proposes a new neighborhood system for airborne laser data. Based on laser data characteristics the proposed systems consider, among other features, point density, layered and overhanging structures, and local surface trends. Parameters for the proposed systems are derived from theoretical and practical observations. The paper demonstrates the type of neighborhood that is established by the proposed systems, and shows that artifacts that are usually created by the common neighborhoods are avoided here, and that structures within the data that are usually masked are revealed. The paper demonstrates how subsequent applications benefit from the new system. Finally, the estimation of surface normals by the proposed systems is compared to the triangulation; results show a significant improvement in the reliability and quality of the estimation.