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遥感技术与应用  2021, Vol. 36 Issue (6): 1272-1283    DOI: 10.11873/j.issn.1004-0323.2021.6.1272
LiDAR专栏     
基于无人机LiDAR点云的多类型植被覆盖滩涂地形滤波
刘帅1(),栾奎峰2(),谭凯1,张卫国1
1.华东师范大学 河口海岸学国家重点实验室,上海 200241
2.上海海洋大学 海洋科学学院,上海 201306
Multi-type Vegetation Coverage Tidal Flat Terrain Filtering based on UAV LiDAR Point Cloud
Shuai Liu1(),Kuifeng Luan2(),Kai Tan1,Weiguo Zhang1
1.State Key Laboratory of Estuarine and Coastal Research,East China Normal University,Shanghai 200241,China
2.College of Marine Sciences,Shanghai Ocean University,Shanghai 201306,China
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摘要:

滩涂是宝贵的自然资源,其地形的高精度反演具有重要科学价值,而现有技术和方法存在较大的局限性。无人机激光雷达技术可快速获取大范围滩涂高精度、高密度的三维点云数据,是滩涂地形反演的重要技术之一。如何对点云数据中滩涂植被进行高精度滤波是地形反演要解决的技术难点,尤其是当滩涂覆盖有茂密的异质性植被(如不同种类和几何形态)时,对滤波算法的通用性和鲁棒性提出了更高的要求。以上海崇明西滩湿地某一滩涂作为研究区域,选取草丛、灌木和高大乔木3类典型植被覆盖的局部区域,利用基于坡度滤波、渐进数学形态学滤波和布料模拟滤波3种常用的点云滤波算法进行点云数据处理,比较分析了3种方法的适用性。结果表明:布料模拟滤波对于3个典型区域实验结果的总误差分别为1.57%、0.16%和0.23%, Kappa系数分别为96.74%、98.70%和99.30%。相较于其他两种算法,布料模拟滤波精度更高,更适用于多类型植被覆盖滩涂区域。因此,采用布料模拟滤波对整个研究区域进行处理,取得了较好的滤波效果,与真实地面吻合度较高。最后,通过克里金插值得到整个研究区域高精度的地形数据。

关键词: 滩涂无人机激光雷达植被滤波数字高程模型    
Abstract:

Tidal flats are precious natural resources, and the high-precision inversion of the topography has important scientific value. However, the existing technologies and methods have great limitations. UAV LiDAR technology can quickly obtain high-precision and high-density three-dimensional point cloud data of large-area tidal flats, which is one of the important technologies for tidal flat terrain inversion. How to perform high-precision filtering of the tidal flat vegetation in the point cloud data is a technical difficulty to be solved in terrain inversion. Particularly, the universality and robustness of the filtering algorithm should be considered when the tidal flat is covered with dense heterogeneous vegetation (e.g., different types and geometric forms). In this paper, a tidal flat of Chongming Xitan in Shanghai is selected as the research area. Three typical vegetation coverage areas (grass, shrub and tall tree), are selected. Three typical point cloud filtering algorithms (slope filtering, progressive mathematical morphology filtering, and cloth simulation filtering) are used to process the point cloud data, and the results are compared to analyze the applicability of the three methods. The results show that the total error of cloth simulation filtering for the three typical areas is 1.57%, 0.16% and 0.23% respectively, and the kappa coefficient is 96.74%, 98.70% and 99.30% respectively. Compared with the other two algorithms, the accuracy of the cloth simulation filtering is higher, and it is more suitable for multi-type vegetation covering tidal flat areas. Therefore, the cloth simulation filtering is used to process the entire study area. A satisfactory filtering result is obtained, which is in good agreement with the real topography. Finally, the high-precision topographic data of the entire study area is obtained through kriging interpolation.

Key words: Tidal flats    UAV    LiDAR    Vegetation filtering    Digital elevation model
收稿日期: 2021-07-06 出版日期: 2022-01-26
ZTFLH:  TP75  
基金资助: 国家重点研发计划项目(2017YFE0107400);国家自然科学基金项目(41901399);自然资源部地理国情监测重点实验室开放基金(2020NGCM06);上海市科委社发创新攻关项目(20DZ1204701);上海市海洋局科研项目(沪海科2020-05);城市空间信息工程北京市重点实验室开放基金项目(20210221);测绘遥感信息工程湖南省重点实验室开放基金项目(E22134)
通讯作者: 栾奎峰     E-mail: liushuai820@126.com;kfluan@shou.edu.cn
作者简介: 刘帅(2000-),男,湖北洪湖人,硕士研究生,主要从事激光雷达测绘方面的研究。E?mail:liushuai820@126.com
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引用本文:

刘帅,栾奎峰,谭凯,张卫国. 基于无人机LiDAR点云的多类型植被覆盖滩涂地形滤波[J]. 遥感技术与应用, 2021, 36(6): 1272-1283.

Shuai Liu,Kuifeng Luan,Kai Tan,Weiguo Zhang. Multi-type Vegetation Coverage Tidal Flat Terrain Filtering based on UAV LiDAR Point Cloud. Remote Sensing Technology and Application, 2021, 36(6): 1272-1283.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2021.6.1272        http://www.rsta.ac.cn/CN/Y2021/V36/I6/1272

图1  布料模拟原理图
图2  研究区域
图3  3类典型滩涂植被原始点云数据
数据点数量区域范围点云密度/(pt/m2)植被类型植被高度/m
1643 627110 m × 100 m58.51草丛植被3~4
2576 496100 m × 100 m57.65灌木植被12~16
3205 710160 m × 15 m85.71高大乔木22~26
表1  实验数据信息
图4  草丛植被区域滤波结果
滤波算法T.I/%T.II/%T.E./%Kappa
基于坡度滤波3.172.542.790.942
渐进数学形态学滤波6.561.833.720.922
布料模拟滤波0.782.111.570.967
表2  草丛植被滤波精度评价
图5  灌木植被区域滤波结果
滤波算法T.I/%T.II/%T.E./%Kappa
基于坡度滤波0.440.190.210.983
渐进数学形态学滤波4.230.140.400.966
布料模拟滤波0.030.170.160.987
表3  灌木植被滤波精度评价
图6  灌木植被区域滤波结果
滤波算法T.I/%T.II/%T.E./%Kappa
基于坡度滤波0.270.550.490.985
渐进数学形态学滤波2.600.240.740.978
布料模拟滤波0.090.270.230.993
表4  高大乔木滤波精度评价
区域点云数量区域范围点云密度区域植被类型
542 016105 m × 95 m54.33 pt/m2草丛植被、灌木植被
530 913100 m × 95 m55.88 pt/m2灌木植被、高大乔木
267 90875 m × 60 m59.54 pt/m2草丛植被、高大乔木
550 88190 m × 65 m94.17 pt/m2草丛植被、灌木植被、高大乔木
表5  所选区域点云数据信息
图7  4个区域滤波前后对比图
区域T.I/%T.II/%T.E./%Kappa
0.920.580.650.979
1.620.450.930.981
1.521.001.280.974
0.800.670.740.985
表6  崇明西滩典型区域滤波精度评价
图8  崇明西滩滤波前后点云数据
图9  研究区域DEM
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