Airborne LiDAR Data Filtering Based on Geodesic Transformations of Mathematical Morphology
Airborne LiDAR Data Filtering Based on Geodesic Transformations of Mathematical Morphology
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DOI：
10.3390/rs9111104
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发表时间：
2017-10
期刊：
影响因子：
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通讯作者：
Yong Li;B. Yong;P. V. Oosterom;M. Lemmens;Huayi Wu;L. Ren;M. Zheng;Jiajun Zhou
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文献类型：
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作者：
Yong Li;B. Yong;P. V. Oosterom;M. Lemmens;Huayi Wu;L. Ren;M. Zheng;Jiajun Zhou
快速获取覆盖大测量区域的精确且密集的三维地理空间信息的能力使得机载光探测和测距（LiDAR）成为地理空间应用和分析的众多领域中的强大技术。LiDAR数据滤波是数字高程模型生成、土地覆盖分类和目标重建的第一步，也是必不可少的一步。形态学滤波方法具有概念简单、易于实现的优点，能够有效地滤除非地面点。然而，形态学方法的滤波质量对结构元素敏感，而结构元素是数学运算滤波成功的关键因素。针对激光雷达点云滤波对结构元素选择的依赖性，提出一种基于数学形态学测地线变换的激光雷达点云滤波方法。与传统的形态学变换相比，测地线变换仅使用基本结构元素，并在有限次迭代后收敛。因此，该算法不需要选择不同的窗口大小或确定最大窗口大小，这可以增强对未知环境的鲁棒性和自动化。实验结果表明，该滤波方法在不同的地形条件下具有良好的滤波性能，能够有效地保留地形细节，滤除各种复杂环境下的非地面点
The capability of acquiring accurate and dense three-dimensional geospatial information that covers large survey areas rapidly enables airborne light detection and ranging (LiDAR) has become a powerful technology in numerous fields of geospatial applications and analysis. LiDAR data filtering is the first and essential step for digital elevation model generation, land cover classification, and object reconstruction. The morphological filtering approaches have the advantages of simple concepts and easy implementation, which are able to filter non-ground points effectively. However, the filtering quality of morphological approaches is sensitive to the structuring elements that are the key factors for the filtering success of mathematical operations. Aiming to deal with the dependence on the selection of structuring elements, this paper proposes a novel filter of LiDAR point clouds based on geodesic transformations of mathematical morphology. In comparison to traditional morphological transformations, the geodesic transformations only use the elementary structuring element and converge after a finite number of iterations. Therefore, this algorithm makes it unnecessary to select different window sizes or determine the maximum window size, which can enhance the robustness and automation for unknown environments. Experimental results indicate that the new filtering method has promising and competitive performance for diverse landscapes, which can effectively preserve terrain details and filter non-ground points in various complicated environments