LIDAR DATA SEGMENTATION AND CLASSIFICATION BASED ON OCTREE STRUCTURE

Lidar (or laser scanning) has become a viable technique for the collection of a large amount of accurate 3D point data densely distributed on the scanned object surface. The inherent 3D nature of the sub-randomly distributed point cloud provides abundant spatial information. To explore valuable spatial information from laser scanned data becomes an active research topic. The subrandomly distributed point cloud should be segmented and classified before the extraction of spatial information and the first step in the processing of the spatial data extraction is an organization of the lidar data. This paper proposes a new algorithm to split and merge the lidar data based on the octree structure. After the process a lidar data set can be segmented to 3D plane clusters and classified by the plane attributes derived from each 3D plane, such as area, gradient, intensity etc. Some example and analysis of practical data set will be performed for segmentation and classification using the proposed methods here. The test result shows the potential of applying this method to extracting spatial information from lidar data. 1. INTRUDUCTION Lidar (or laser scanning) has become a viable technique in recent decades. The ability of collecting a large amount of accurate 3D point data densely distributed on the scanned object surface has brought us a new research topic (Ackermann, 1999). The inherent 3D nature of the sub-randomly distributed point cloud contains abundant space information and can be further extracted for digital elevation model generation, 3D building model reconstruction, and trees detection (Haala and Brenner, 1999; Maas and Vosselman, 1999; Priestnall, et al., 2000; Vosselman and Dijkman, 2001). To explore valuable spatial information from lidar data becomes an active topic. Lidar data record 3D surface information in detail. To explore valuable spatial information from the huge amount of 3D data is difficult and time consuming. Segmentation is generally prerequisite for spatial feature extracting. Most segmentation techniques were developed from 2.5D grid data or image data (Masaharu and Hasegawa, 2000; Geibel and Stilla, 2000). Subrandomly distributed point cloud is transformed into a grid data set through an interpolation procedure to apply an image-based segmentation and Classification (S/C), but some important spatial information may be lost (Axelsson, 1999; Gamba and Casella, 2000). Eventually, new S/C methods suitable for lidar data are needed for practical application. An octree-structure-based split-and-merge segmentation method for organizing lidar point cloud into clusters of 3D planes is proposed here. The method is hierarchically splitting the point cloud set on the octree structure until the points contained in each sub-node are coplanar, or say distributed in a 3D plane or less than 3 points. The neighbouring 3D planes with similar attribute are merged after splitting to form larger planes. The segmented 3D planes than can be classified according to the attributes derived from each 3D plane. The proposed method would suitably work for airborne lidar data as well as ground-based laser scanning data. Some results on both kind of lidar are presented in this paper finally. 2. OCTREE-STRUCTURE-BASED SEGMENTATION The principle of the method is to segment point cloud into 3D planes. A split and merge segmentation based on the octree structure (Fig. 1.a) is developed. 2.1 Split process The split process starts from the whole data set as a root node. The data set space will be divided into 8 equal sub-spaces, if the data set could not pass the distance and area threshold. The split generates 8 sub-nodes (Figure 1.b) representing the split spaces. Each sub-node will be split continuously until the scan points contained in the split space of the sub-node are distributed close to a 3D best-fit plane or less than 3 points.