发表机构
AGH University of Krakow（克拉科夫AGH大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种基于U-Net的异方差回归方法，从无人机摄影测量点云中估计地面垂直位移，相比传统地面滤波方法在多数数据集上表现更优，能有效量化不确定性。
AI 中文摘要
本文介绍了一种算法，该算法利用U-Net架构从无人机（UAV）摄影测量点云中确定垂直地面位移，为传统地面滤波方法提供了一种替代方案。与依赖点云分类的传统地面滤波器不同，所提出的方法采用异方差回归。U-Net模型预测高程校正的条件期望值，旨在减少植被对确定的地面高程的影响。同时，它估计高程校正方差的对数，从而能够直接量化每个高程校正值相关的不确定性。该算法使用三个指标进行评估：垂直位移的均方根误差（RMSE）、具有确定位移值的节点百分比，以及这些值中的异常值百分比。性能采用理想解相似性排序技术（TOPSIS）方法进行评估，并与多个基于地面滤波的算法在四个数据集上进行比较，每个数据集至少包含两个时间间隔。在大多数情况下，基于U-Net的方法相比传统地面滤波技术表现出轻微的性能优势。例如，对于基于U-Net的算法，在其中一个测试数据集中，确定的沉降的RMSE为6.1厘米，具有确定沉降的节点百分比为80.5%，异常值百分比为0.2%。对于相同情况，基于次优模型（SMRF）的算法获得的RMSE为7.7厘米；对于77.3%的节点，沉降被确定；异常值百分比为0.3%。
英文摘要
This article introduces an algorithm that uses a U-Net architecture to determine vertical ground surface displacements from unmanned aerial vehicle (UAV)-photogrammetry point clouds, offering an alternative to traditional ground filtering methods. Unlike con-ventional ground filters that rely on point cloud classification, the proposed approach em-ploys heteroscedastic regression. The U-Net model predicts the conditional expected val-ues of the elevation corrections, aiming to reduce the impact of vegetation on determined ground surface elevations. Concurrently, it estimates the logarithm of the elevation cor-rection variance, allowing for direct quantification of the uncertainty associated with each elevation correction value. The algorithm was evaluated using three metrics: the root mean square error (RMSE) of vertical displacements, the percentage of nodes with deter-mined displacement values, and the percentage of outliers among those values. Perfor-mance was assessed using the technique for order of preference by similarity to ideal so-lution (TOPSIS) method and compared against several ground-filter-based algorithms across four datasets, each including at least two time intervals. In most cases, the U-Net-based approach demonstrated a slight performance advantage over traditional ground filtering techniques. For example, for the U-Net-based algorithm, for one of the test da-tasets, the RMSE of the determined subsidences was 6.1 cm, the percentage of nodes with determined subsidences was 80.5%, and the percentage of outliers was 0.2%. For the same case, the algorithm based on the next best model (SMRF) allowed an RMSE of 7.7 cm to be obtained; for 77.3% of nodes, the subsidences were determined; and the percentage of outliers was 0.3%.
Journal ref2025, Remote Sensing, 17(18), 3259
