Shangshu Cai ^1,2  Sisi Yu ^3,4,5,6,*  

Author Details：

1.State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China. 2. Key Laboratory of Mine Environmental Monitoring and Improving around Poyang Lake of Ministry of Natural Resources, East China University of Technology, Nanchang 330013, China. 3. Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, China. 4. Sino-Africa Joint Research Center, Chinese Academy of Sciences, Wuhan 430074, China. 5. Department of Public Administration, Law School, Shantou University, Shantou 515063, China. 6. Institute of Local Government Development, Shantou University, Shantou 515063, China. *. Author to whom correspondence should be addressed.

Abstract

Ground filtering is one of the essential steps for processing airborne light detection and ranging data in forestry applications. However, the performance of existing methods is still limited in forested areas due to the complex terrain and dense vegetation. To overcome this limitation, we proposed an improved surface-based filter based on multi-directional narrow window and cloth simulation. The innovations mainly involve two aspects as follows: (1) sufficient and uniformly distributed ground seeds are identified by merging the lowest points and line segments from the point clouds within a multi-directional narrow window; (2) complete and accurate ground points are extracted using a cyclic scheme that includes incorrect ground point elimination using the internal force adjustment of cloth simulation, terrain reconstruction with moving least-squares plane fitting, and ground point extraction based on progressively refined terrain. The proposed method was tested in five forested sites with various terrain characteristics and vegetation distributions. Experimental results showed that the proposed method could accurately separate ground points from non-ground points in different forested environments, with the average kappa coefficient of 88.51% and total error of 4.22%. Moreover, the comparative experiments proved that the proposed method performed better than the classical methods involving the slope-based, mathematical morphology-based and surface-based methods.

地面滤波是林业应用中处理机载光探测和测距数据的重要步骤之一。然而，由于地形复杂，植被茂密，现有方法在森林地区的性能仍然有限。为了克服这一局限性，我们提出了一种基于多向窄窗和布料模拟的改进的基于表面的滤波器。创新主要涉及以下两个方面：（1）在多向窄窗口内，通过合并点云的最低点和线段，识别出充足且分布均匀的地面种子;（2）采用循环方案提取完整、准确的地面点，包括使用布料模拟的内力调整消除不正确的地面点，使用移动最小二乘平面拟合进行地形重建，以及基于逐步细化地形的地面点提取。在5个具有不同地形特征和植被分布的林地中对所提方法进行了测试。

实验结果表明，所提方法能够准确区分不同森林环境下的地面点与非地面点，平均kappa系数为88.51%，总误差为4.22%。对比实验证明，所提方法优于基于斜率、基于数学形态学和基于表面的经典方法。

注：本成果获得汕头大学地方政府发展研究所2022年度开放基金（No. 07422002）的资助。

Cite this article:

Shangshu Cai, Sisi Yu. Filtering Airborne LiDAR Data in Forested Environments Based on Multi-Directional Narrow Window and Cloth Simulation. Remote Sensing. 2023; 15(5):1400.

https://doi.org/10.3390/rs15051400