Automatic detection of residential buildings using LIDAR data and multispectral imagery |
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| Authors: | Mohammad Awrangjeb Mehdi Ravanbakhsh Clive S. Fraser |
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| Affiliation: | 1. School of Earth Sciences and Engineering, Hohai University, Nanjing 210098, China;2. State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, 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. Forest Resources Management, University of British Columbia, 2424 Main Mall, Vancouver, BC V6T 1Z4, Canada;2. CanmetENERGY, Natural Resources Canada, Canada;1. School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275, China;2. School of Geographical Sciences, Guangzhou University, Guangzhou 510006, China;3. The College of Environment and Planning of Henan University, Henan University, Kaifeng 475000, China;4. Guangdong Key Laboratory of Urbanization and Geo-simulation, Guangzhou 510275, China;5. Guangzhou Urban Planning and Design Survey Research Institute, Guangzhou 510060, China |
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| Abstract: | This paper presents an automatic building detection technique using LIDAR data and multispectral imagery. Two masks are obtained from the LIDAR data: a ‘primary building mask’ and a ‘secondary building mask’. The primary building mask indicates the void areas where the laser does not reach below a certain height threshold. The secondary building mask indicates the filled areas, from where the laser reflects, above the same threshold. Line segments are extracted from around the void areas in the primary building mask. Line segments around trees are removed using the normalized difference vegetation index derived from the orthorectified multispectral images. The initial building positions are obtained based on the remaining line segments. The complete buildings are detected from their initial positions using the two masks and multispectral images in the YIQ colour system. It is experimentally shown that the proposed technique can successfully detect urban residential buildings, when assessed in terms of 15 indices including completeness, correctness and quality. |
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