An Active Learning Method for DEM Extraction From Airborne LiDAR Point Clouds
An Active Learning Method for DEM Extraction From Airborne LiDAR Point Clouds
复制标题
一种从机载 LiDAR 点云中提取 DEM 的主动学习方法
DOI：
10.1109/access.2019.2926497
复制
发表时间：
2019
期刊：
影响因子：
3.9
通讯作者：
Hu Youjian
中科院分区：
文献类型：
--
作者：
Hui Zhenyang;Jin Shuanggen;Cheng Penggen;Yao Ziggah Yevenyo;Wang Leyang;Wang Yuqian;Hu Haiying;Hu Youjian
机载光探测与测距（LiDAR）是一种流行的主动遥感技术，近年来发展非常迅速。针对机载激光雷达点云在复杂地形环境下过滤精度低、避免过多人为干预的问题，提出一种基于主动学习的点云过滤方法。在所提出的方法中，通过多尺度形态学操作自动获取和标记初始训练样本。这样做时，不需要手动选择和标记训练样本，即根据主动学习中使用的预言机逐步添加训练样本。在本文中，预言机被设置为从点到拟合曲面的残差的 sigmoid 函数。随后，使用更新的训练样本逐步修正训练模型。最后，通过基于斜率的方法进一步优化分类结果。使用国际摄影测量与遥感协会（ISPRS）提供的具有不同过滤挑战的三个数据集来测试所提出的方法。与其他十种著名的滤波方法相比，该方法可以获得最小的平均总误差（5.51%）。因此，可以得出结论，所提出的方法对于不同的地形环境都表现良好。
Airborne Light Detection and Ranging (LiDAR) is a popular active remote sensing technology that has been developing very rapidly in recent years. To solve the problems of low filtering accuracy of airborne LiDAR point clouds in complex terrain environments and avoiding too much human intervention, this paper proposes a point cloud filtering method based on active learning. In the proposed method, the initial training samples are acquired and marked automatically by multi-scale morphological operations. In so doing, no training samples are selected and labeled manually, i.e., the training samples are added gradually according to the oracle used in active learning. In this paper, the oracle is set to a sigmoid function of residuals from the points to the fitted surface. Subsequently, the training model is revised progressively using the updated training samples. Finally, the classification results are further optimized by a slope-based method. Three datasets with different filtering challenges provided by the International Society for Photogrammetry and Remote Sensing (ISPRS) were used to test the proposed method. Comparing with the other ten famous filtering methods, the proposed method can achieve the smallest average total error (5.51%). Thus, it can be concluded that the proposed method performs very well toward different terrain environments.