An improved building detection in complex sites using the LIDAR height variation and point density
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In this paper, the height variation in LIDAR (Light Detection And Ranging) point cloud data and point density are analyzed to remove the false building detection in highly vegetation and hilly sites. In general, the LIDAR points in a tree area have higher height variations than those in a building area. Moreover, the density of points having similar height values is lower in a tree area than in a building area. The proposed method uses such information as an improvement to a current state-of-the-art building detection method. The qualitative and object-based quantitative analyzes have been performed to verify the effectiveness of the proposed building detection method as compared with a current method. The analysis shows that proposed building detection method successfully reduces false building detection (i.e. trees in high complex sites of Australia and Germany), and the average correctness and quality have been improved by 6.36% and 6.16% respectively.
2013 28th International Conference on Image and Vision Computing New Zealand (IVCNZ 2013)
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Photogrammetry and Remote Sensing