Automatic extraction of ground points, called filtering, is an essential step in producing Digital Terrain Models from airborne LiDAR data. Scene complexity and computational performance are two major problems that should be addressed in filtering, especially when processing large point cloud data with diverse scenes. This paper proposes a fast and intelligent algorithm called Semi-Global Filtering (SGF). The SGF models the filtering as a labeling problem in which the labels correspond to possible height levels. A novel energy function balanced by adaptive ground saliency is employed to adapt to steep slopes, discontinuous terrains, and complex objects. Semi-global optimization is used to determine labels that minimize the energy. These labels form an optimal classification surface based on which the points are classified as either ground or non-ground. The experimental results show that the SGF algorithm is very efficient and able to produce high classification accuracy. Given that the major procedure of semi-global optimization using dynamic programming is conducted independently along eight directions, SGF can also be paralleled and sped up via Graphic Processing Unit computing, which runs at a speed of approximately 3 million points per second. OPEN ACCESSRemote Sens. 2015, 7 10997

ABSTRACT:One of the major problems in processing LiDAR (Light Detection And Ranging) data is its huge data volume which causes very high computational load when dealing with large areas with high point density. A fast and simple algorithm based on scan line analysis is proposed for automatic detection of building points from LiDAR data. At first, ground/non-ground classification is performed to filter out the ground points. Douglas-Peucker algorithm is then used to segment the scan line into segment objects based on height variation. These objects are preliminarily classified into buildings and vegetation based on local analysis using simple rules. At last, the region growing method is used to improve the quality of the extraction. The test data provided by the ISPRS test project on urban object extraction, containing a lot of buildings with complex roof structures, various sizes, and different heights, is used to test the algorithm. The experimental results show that the proposed algorithm can extract building regions effectively.

Plane segmentation is an important step in feature extraction and 3D modeling from light detection and ranging (LiDAR) point cloud. The accuracy and speed of plane segmentation are two issues difficult to balance, particularly when dealing with a massive point cloud with millions of points. A fast and easy-to-implement algorithm of plane segmentation based on cross-line element growth (CLEG) is proposed in this study. The point cloud is converted into grid data. The points are segmented into line segments with the Douglas-Peucker algorithm. Each point is then assigned to a cross-line element (CLE) obtained by segmenting the points in the cross-directions. A CLE determines one plane, and this is the rationale of the algorithm. CLE growth and point growth are combined after selecting the seed CLE to obtain the segmented facets. The CLEG algorithm is validated by comparing it with popular methods, such as RANSAC, 3D Hough transformation, principal component analysis (PCA), iterative PCA, and a state-of-the-art global optimization-based algorithm. Experiments indicate that the CLEG algorithm runs much faster than the other algorithms. The method can produce accurate segmentation at a speed of 6 s per 3 million points. The proposed method also exhibits good accuracy.

By fusing with other sensory data, especially high resolution imagery, LiDAR can be a good source of information for DEM extraction and feature extraction because it provides integrated information of geometric (surface), spectral and spatial property. Nowadays airborne LiDAR system vendors such as Leica and Toposys and others are providing systems with integrated camera capturing 3D point cloud and high resolution images simultaneously, for example, Leica's ALS50II, ALS60, and Toposys' FALCON II. The full potential of an integrated system in surveying and mapping has to be explored yet. In this paper, taking example of Toposys' FALCON data, we discuss some issues of data fusion: (1) cross sensor data registration, including geometric error budget; (2) two methods of fused data generation -imagery fused with range image re-sampled from point cloud and point cloud with assigned image pixel attributes. (3) Occlusion problem and how to solve it. We also show the segmentation results by a combined segmentation algorithm carried out on the fused multiple layer data. The results demonstrate the advantages of data fusion due to rich information and cues of objects in the fused data.