Ground filtering is an essential procedure in almost all LiDAR applications. However, most existing ground filtering algorithms require different amounts of user input to manually set up initial parameters, such as terrain relief amplitude and average slope, which is subjective, time consuming, and prone to errors. Here, we propose a simple terrain relief index derived from raw airborne LiDAR data to automatically tune the slope-related parameters of ground filtering algorithms. The terrain relief index is a ratio between the height difference of the entire point cloud and the maximum above ground level of non-ground points. The latter variable can be estimated with the maximum local height difference of raw LiDAR data through gridding. We validated our method using the benchmark airborne LiDAR datasets provided by the International Society for Photogrammetry and Remote Sensing. The results showed a high correlation (r = 0.876) between the terrain relief index and the referential terrain relief amplitude. The degree of correlation was greater across larger areas (r = 0.926) than small areas (r = 0.861) regardless of the type of land cover (e.g., city or forest). The terrain relief index was introduced into two existing filtering algorithms: Cloth Simulation Filtering (CSF) and Progressive Morphological (PM) Filter, by relating the terrain relief index to the cloth rigidness of the CSF and the slope threshold of the PM filter. To compare the results, the two algorithms were implemented both with manually tuned parameters and with the parameters derived from the terrain relief index. The results showed that there was only a slight discrepancy in average Total Error (0.1%) between them in the CSF, which means that the terrain relief index can automatically determine the cloth rigidness without noticeable loss of accuracy. The average difference between the slope threshold provided by the terrain relief index and the manually tuned optimal slope threshold was 0.142 rad (8.136°) for the PM filter, which is acceptable relative to manually parameter setting without any prior knowledge. The terrain relief index can estimate the terrain relief amplitude from raw airborne LiDAR data, and the parameter settings suggested in this paper for the filtering algorithm can improve automation.

几乎在所有LiDAR应用中，接地滤波都是必不可少的步骤。然而，大多数现有的地面滤波算法需要不同数量的用户输入来手动设置初始参数，例如地形起伏幅度和平均斜率，这是主观的，耗时的并且容易出错。在这里，我们提出了一个从原始机载LiDAR数据得出的简单地形起伏指数，以自动调整地面滤波算法的与坡度相关的参数。地形起伏指数是整个点云的高度差与非地面点的最高地面高度之间的比率。可以通过网格化使用原始LiDAR数据的最大局部高度差来估计后一个变量。我们使用国际摄影测量与遥感学会提供的基准机载LiDAR数据集验证了我们的方法。结果显示出高度相关性（r  = 0.876）在地形起伏指数和参考地形起伏幅度之间。较大区域（r  = 0.926）的相关度大于较小区域（r = 0.861），而与土地覆盖的类型（例如城市或森林）无关。通过将地形起伏指数与CSF的布料刚度和PM过滤器的斜率阈值相关联，将地形起伏指数引入到两个现有的过滤算法中：布模拟过滤（CSF）和渐进形态（PM）过滤器。为了比较结果，两种算法均使用手动调整的参数和从地形地形指数得出的参数来实现。结果表明，在CSF中，它们之间的平均总误差之间只有很小的差异（0.1％），这意味着地形起伏指数可以自动确定布料的硬度，而不会显着降低精度。对于PM滤波器，由地形起伏指数提供的坡度阈值和手动调整的最佳坡度阈值之间的平均差为0.142 rad（8.136°），相对于没有任何先验知识的手动参数设置而言是可接受的。地形起伏指数可以从原始机载LiDAR数据估计地形起伏幅度，并且本文为滤波算法建议的参数设置可以提高自动化程度。