Automatic extraction of building roofs using LIDAR data and multispectral imagery |
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| Institution: | 1. Gippsland School of Information Technology, Monash University, Vic 3842, Australia;2. CRC for Spatial Information, Dept. of Infrastructure Engineering, University of Melbourne, Australia;1. School of Remote Sensing and Information Engineering, Wuhan University, 129 Luoyu Rd., 430079 Wuhan, China;2. School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA;3. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Rd., 430079 Wuhan, China;1. Hacettepe University, Department of Geomatics Engineering, 06800 Cankaya-Ankara, Turkey;2. Department of Space Sciences and Technologies, Akdeniz University, 07058 Antalya, Turkey;1. Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, China;2. Beijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing 100048, China;3. Hunan Provincial Key Laboratory of Geo-Information Engineering in Surveying, Mapping and Remote Sensing, Hunan University of Science and Technology, Hunan, China;1. College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Jiangjun St. 29, 211106 Nanjing, China;2. Institute of Photogrammetry and GeoInformation, Leibniz Universität Hannover, Nienburger Straße 1, 30167 Hannover, Germany |
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| Abstract: | Automatic 3D extraction of building roofs from remotely sensed data is important for many applications including city modelling. This paper proposes a new method for automatic 3D roof extraction through an effective integration of LIDAR (Light Detection And Ranging) data and multispectral orthoimagery. Using the ground height from a DEM (Digital Elevation Model), the raw LIDAR points are separated into two groups. The first group contains the ground points that are exploited to constitute a ‘ground mask’. The second group contains the non-ground points which are segmented using an innovative image line guided segmentation technique to extract the roof planes. The image lines are extracted from the grey-scale version of the orthoimage and then classified into several classes such as ‘ground’, ‘tree’, ‘roof edge’ and ‘roof ridge’ using the ground mask and colour and texture information from the orthoimagery. During segmentation of the non-ground LIDAR points, the lines from the latter two classes are used as baselines to locate the nearby LIDAR points of the neighbouring planes. For each plane a robust seed region is thereby defined using the nearby non-ground LIDAR points of a baseline and this region is iteratively grown to extract the complete roof plane. Finally, a newly proposed rule-based procedure is applied to remove planes constructed on trees. Experimental results show that the proposed method can successfully remove vegetation and so offers high extraction rates. |
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| Keywords: | Building Feature Extraction Reconstruction Automation Integration LIDAR Orthoimage |
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