Ground and building extraction from LiDAR data based on differential morphological profiles and locally fitted surfaces |
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| Institution: | 1. College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;2. School of Engineering and Technology, Central Queensland University, North Rockhampton, QLD 4702, Australia;1. Shandong Provincial Key Laboratory of Laser Polarization and Information Technology, Laser Institute, Qufu Normal University, Shandong 273165, PR China;2. School of Information Science and Engineering, Shandong University, Jinan 250100, PR China;3. State Key laboratory of Crystal Material, Shandong University, Jinan, 250100, PR China;1. School of Mechanical Engineering, Tianjin Polytechnic University, Tianjin 300387, China;2. Tianjin Key Laboratory of Advanced Mechatronics Equipment Technology, Tianjin 300387, China;1. College of Electrical and Optical Engineering & College of Microelectronics, Nanjing University of Posts and Telecommunications, Nanjing, 210003, People’s Republic of China;2. Bell Honors School, Nanjing University of Posts and Telecommunications, Nanjing, 210003, People’s Republic of China;3. College of Science, Nanjing University of Posts and Telecommunications, Nanjing, 210003, People’s Republic of China;1. Materials Science and Engineering Research Center, Beijing Jiaotong University, Beijing 100044, PR China;2. Institute of Physics, Chinese Academy of Science, Beijing 100083, PR China |
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| Abstract: | This paper proposes a new framework for ground extraction and building detection in LiDAR data. The proposed approach constructs the connectivity of a grid over the LiDAR point-cloud in order to perform multi-scale data decomposition. This is realised by forming a top-hat scale-space using differential morphological profiles (DMPs) on points’ residuals from the approximated surface. The geometric attributes of the contained features are estimated by mapping characteristic values from DMPs. Ground definition is achieved by using features’ geometry, whilst their surface and regional attributes are additionally considered for building detection. A new algorithm for local fitting surfaces (LoFS) is proposed for extracting planar points. Finally, transitions between planar ground and non-ground regions are observed in order to separate regions of similar geometrical and surface properties but different contexts (i.e. bridges and buildings). The methods were evaluated using ISPRS benchmark datasets and show superior results in comparison to the current state-of-the-art. |
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| Keywords: | LiDAR Ground extraction Buildings detection Mathematical morphology DMP LoFS |
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