Morphological and triangular irregular network (TIN) ground filters require setting up different parameters to achieve high accuracy for different terrains. A proposed morphologically iterative TIN (MIT) ground filter only requires maximum building size in the processing of raw light detection and ranging (LiDAR) data. This approach applies morphological and TIN densification in an iterative way for separating ground points from off-ground ones. A radial nearest neighbor is designed to select the surrounding nearest neighbors for each point, and these neighbors are analyzed to define the parameters of a local translational 3D plane surface. Experimental results using ISPRS benchmark datasets show that MIT achieves an average total error of <4.0 % , and an average kappa coefficient of >85 % . Further experimental validation with Hong Kong LiDAR datasets reveals that MIT is effective in detecting dense ground points and robust in various terrain situations.

中文翻译：

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机载LiDAR滤波的形态学迭代三角不规则网络

形态和三角形不规则网络（TIN）地面滤波器需要设置不同的参数才能在不同的地形上实现高精度。拟议的形态学迭代TIN（MIT）地面滤波器在处理原始光检测和测距（LiDAR）数据时仅需要最大建筑物大小。该方法以迭代方式应用形态学和TIN致密化，以将地面点与地面点分开。径向最近邻被设计为为每个点选择周围的最近邻，并对这些近邻进行分析以定义局部平移3D平面表面的参数。使用ISPRS基准数据集进行的实验结果表明，MIT的平均总误差<4.0％，平均kappa系数> 85％。