29 May 2013 An approach to DSM refinement with fusion of airborne lidar point cloud data and optical imagery
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Abstract
The airborne LiDAR system, which usually integrated with optical camera, is an efficient way of acquiring 3D geographic information and enjoys widely application in building DSM. However, when the airborne LiDAR is used in urban area, where there are a large amount of tall buildings, the characteristic points of buildings are seldom measured and the measured points are frequently too sparse to create precise building models. In this paper, an approach to DSM refining DSM in urban area with fusion of airborne LiDAR point cloud data and optical imagery is put forward. Firstly, the geometric relationship between the airborne LiDAR point and the correspondent pixel on the image synchronously taken by optical camera is analyzed. The relative position and attitude parameters between the laser rangefinder and the camera are determined in the process of alignment and calibration. Secondly, the building roof edges on the optical image are extracted by edge detection. By tracing the building roof edges, the contours of building roofs in vector format are acquired and the characteristic points of buildings are further extracted. Thirdly, all the LiDAR measured points on the roof of specific building are separated from the point cloud data by judging the geometric relation between LiDAR measured points and the building outline, which is represented by a polygon, according to their plane coordinates. Finally, the DSM refinement for buildings can be implemented. All pixels representing the building roof are given heights as same as that of nearer LiDAR point inside the polygon. Ortho-photo map and virtual building models of urban area with higher quality can be reached with the refined DSM and optical images.
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Xiangyang Hao, Xiangyang Hao, Weiqiang Zhang, Weiqiang Zhang, Lixing Jiang, Lixing Jiang, } "An approach to DSM refinement with fusion of airborne lidar point cloud data and optical imagery", Proc. SPIE 8756, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2013, 87560H (29 May 2013); doi: 10.1117/12.2014816; https://doi.org/10.1117/12.2014816
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