| 基于LiDAR点云数据的低矮植被分类方法 |
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| 引用本文： | 孙嘉悦,张旭晴,牛雪峰.基于LiDAR点云数据的低矮植被分类方法[J].世界地质,2018,37(4):1265-1273. |
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| 作者姓名： | 孙嘉悦 张旭晴 牛雪峰 |
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| 作者单位： | 吉林大学地球探测科学与技术学院, 长春 130026 |
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| 基金项目： | 国家自然科学基金面上项目（41472243）. |
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| 摘 要： | 基于LiDAR点云数据进行小区域低矮植被分类方法的研究,利用渐进加密三角网算法分离地面点与低矮植被点,通过分析调整阈值对分离效果的影响验证该算法的适用程度。本文研究表明渐进加密三角网算法适用于低矮植被分布多的地势平坦地区,不适用于地形起伏较大的山区与城市地区。
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| 关 键 词： | LiDAR 分类 滤波 |
| 收稿时间： | 2018-01-22 |
| 修稿时间： | 2018-09-30 |
Low vegetation classification based on LiDAR point cloud data |
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| SUN Jia-yue,ZHANG Xu-qing,NIU Xue-feng.Low vegetation classification based on LiDAR point cloud data[J].World Geology,2018,37(4):1265-1273. |
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| Authors: | SUN Jia-yue ZHANG Xu-qing NIU Xue-feng |
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| Institution: | College of Geo-exploration Science and Technology, Jilin University, Changchun 130026, China |
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| Abstract: | Based on the LiDAR point cloud data, the research on the method of low-level vegetation isolation in small area is carried out. The ground points and the low vegetation points are separated using the progressive cryptographic triangulation algorithm. The application of the algorithm is verified by analyzing the effect of adjusting the threshold on the separation effect. The result shows that the progressive cryptographic triangulation algorithm is suitable for flat areas with wide distribution of low vegetation but not suitable for mountainous areas and urban areas with large terrain fluctuations. |
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| Keywords: | LiDAR classification filtering |
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