一种机载LiDAR数据漏洞检测方法

获取机载LiDAR数据时易受地形起伏、航摄高度、地物镜面反射等因素的影响，导致LiDAR数据出现漏洞，若对其不进行检测处理，将严重影响LiDAR数据的生产与应用。鉴于此，本文利用点云的位置与属性信息，基于等比例内缩和点云格网化算法，检测机载LiDAR数据中存在的漏洞区域，有效支撑了LiDAR数据的质量检测和补摄工作。试验结果表明，该方法检测到的漏洞区域完整、精确，等比例内缩算法与等值内缩算法相比，减弱了地形起伏对漏洞检测精度的影响，且更科学合理。

A vulnerability detection method for airborne LiDAR data

Airborne LiDAR equipment is susceptible to terrain undulations, flight heights, specular reflections and other factors, which leads to holes in LiDAR data. Moreover, if the holes are not detected or processed, it will seriously affect the production and application of LiDAR data. In view of this, this paper uses the position and attribute information of the point cloud, based on the equal-scale shrinkage and point cloud rasterization algorithm to detect the vulnerability areas in the airborne LiDAR data, and then supports the quality detection of the LiDAR data and the supplementary scanning of the vulnerability areas. Experimental results show that the vulnerability area detected by this method is complete and accurate, and the proportional reduction algorithm reduces the influence of terrain fluctuations on the vulnerability detection accuracy compared to the equivalent reduction algorithm, which is more scientific and reasonable.