关键词: 机载激光雷达, 多值体素结构, 三维平面提取, 区域增长, 空间连通区域, 法向一致性, 反射强度一致性, 缓冲区分析

Abstract:

The traditional 3D plane extraction algorithm for airborne LIDAR data have defects. For example, designing on discrete LIDAR points leads to difficulties in the design of point-based plane extraction methods. It is easy to generate false detection in the smooth transition region of plane by using only the consistency of geometric features. To overcome the above restrictions, a new 3D plane detection algorithm for airborne LIDAR data was developed based on multi-value voxel connected region construction method. The proposed algorithm is designed based on voxel structure and makes comprehensive use of the geometry and the reflection intensity formation from airborne LIDAR data. It converts the traditional plane feature point clustering into connected region construction based on voxel and reflection intensity statistics under spatial constraints. It gives the multi-valued voxel structure construction scheme of airborne LIDAR point cloud data and the planar extraction scheme on this basis, which contributes to the development of airborne LIDAR point cloud data processing and application based on the theory of multi-valued voxel model. The specific implementation process of the algorithm is showed as follows: ① The airborne LIDAR point cloud data is regularized to a multi-valued voxel structure, where voxel value is the average laser reflection intensity, curvature, and normal vector of the LIDAR point(s) within the voxel. ② In the DSM data of voxel structure, voxels with smaller curvature are selected as seeds, and then the seeds and their 3D connected regions, which are connected with the seeds and have similar reflection intensity and normal, are labelled as the plane. ③ In the non-DSM data of voxel structure, the voxels located in the contour buffer of the connected region with laser reflection intensity satisfying statistical characteristics are labeled as planes. In this paper, airborne LIDAR data provided by ISPRS were used to test the accuracy of the proposed algorithm. The quantitative evaluation results showed that the quality and Kappa coefficient of the proposed method were 92.5% and 89.4%, respectively, which were 9.68% and 11.62% higher than that of the traditional region-growing algorithm using only geometric features.

Key words: airborne LIDAR, multi-valued voxel structure, three-dimensional plane extraction, regional growth, spatially connected region, normal consistency, consistency of reflection intensity, buffer analysis