In the last decade, airborne light detection and ranging (LiDAR) scanning (ALS) technology has become a powerful technique for remote sensing, imaging, and mapping. However, the data obtained from any measurement system can include inaccurate signals affected by systematic errors or by the external environment. De-noising to remove inaccurate outlier points is a fundamental and challenging problem for ALS-based mapping applications. The proposed method aims to recover the patterned (planar and linear) points within the assigned outlier and removed points. The method consists of 3 steps. First, statistical outlier removal (SOR) filtering is implemented, and outlier points are detected with the filtering method. Next, the machine learning system reclassifies the filtered outlier points. If the classification result is “inlier” , that point is added to the filtered inlier point cloud as an inlier point. The accuracy of outlier points was evaluated against a manually determined validation set. The results achieved \(99\%\) and \(98\%\) according to the highest overall accuracy criterion and kappa coefficient, respectively. These findings are a promising step to test the proposed method in three different test areas and extend it to widespread spatial dimensions. Furthermore, the findings show that many useful points are removed by SOR filtering. The developed methodology contributes to the reduction of errors caused by data losses in various modelling studies, especially for power transmission line and 3D façade modelling studies.

在过去十年中，机载光探测和测距 (LiDAR) 扫描 (ALS) 技术已成为遥感、成像和制图的强大技术。然而，从任何测量系统获得的数据都可能包含受系统误差或外部环境影响的不准确信号。去噪以去除不准确的异常点是基于 ALS 的映射应用程序的一个基本且具有挑战性的问题。所提出的方法旨在恢复分配的异常值和移除点内的模式化（平面和线性）点。该方法由3个步骤组成。首先，实现统计离群点去除（SOR）过滤，用过滤方法检测离群点。接下来，机器学习系统对过滤后的离群点进行重新分类。如果分类结果是“inlier”，该点被添加到过滤后的内点云中作为内点。根据手动确定的验证集评估异常点的准确性。取得的成果\(99\%\)和\(98\%\) 分别根据最高的整体精度标准和 kappa 系数。这些发现是在三个不同的测试区域测试所提出的方法并将其扩展到广泛的空间维度的有希望的一步。此外，研究结果表明，SOR 过滤去除了许多有用的点。所开发的方法有助于减少各种建模研究中由数据丢失引起的错误，特别是对于输电线路和 3D 立面建模研究。