Abstract
Abstract. Light Detection And Ranging (LiDAR) is an active remote sensing technology used for several applications. A segmentation of Airborne Laser Scanning (ALS) point cloud is very important task that still interest many scientists. In this paper, the Connected Component Analysis (CCA), or Connected Component Labeling is proposed for clustering non-planar objects from Airborne Laser Scanning (ALS) LiDAR point cloud. From raw point cloud, sub-surface segmentation method is applied as preliminary filter to remove planar surfaces. Starting from unassigned points , CCA is applied on 3D data considering only neighboring distance as initial parameter. To evaluate the clustering, an interactive labeling of the resulting components is performed. Then, components are classified using Support Vector Machine, Random Forest and Decision Tree. The ALS data used is characterized by a low density (4–6 points/m2), and is covering an urban area, located in residential parts of Vaihingen city in southern Germany. The visualization of the results shown the potential of the proposed method to identify dormers, chimneys and ground class.
Highlights
Light Detection And Ranging (LiDAR) is an active remote sensing technology used for several applications
The visualization of the results shown the great potential of connected component analysis when applying it for clustering non-planar surfaces such as dormers and chimneys
The process of Connected Component Analysis (CCA) is described in algorithm 1
Summary
Light Detection And Ranging (LiDAR) is an active remote sensing technology used for several applications. Most of point clouds segmentation methods are typically adapted to extract planar surfaces as roof faces and other surfaces from airborne laser scanning data. For this purpose, different segmentation methods have been developed based, for example, on Hough transform (Maltezos, Ioannidis, 2016), RANSAC (Chen et al, 2012), or surface growing (Alharthy, Bethel, 2004). The purpose was to gather better model data, as roof features and shapes become more apparent regarding these subsurface segments. Despite the fact that the subsurface growing algorithm gives good results, even for complex building roof shapes, the problem remains with roof faces generated by dormers and chimneys.
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