| 融合序列形态学算子的城区LiDAR滤波方法 |
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| 引用本文： | 孙美玲,李永树,陈强,蔡国林.融合序列形态学算子的城区LiDAR滤波方法[J].西南交通大学学报,2013,26(6):1038-1044. |
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| 作者姓名： | 孙美玲 李永树 陈强 蔡国林 |
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| 基金项目： | 国家自然科学基金资助项目（51178404，41072220，41201434）高等学校博士学科点专项科研基金资助项目 （20100184110019）中央高校基本科研业务费专项资金资助项目（SWJTU09CX010，SWJTU09BR050） |
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| 摘 要： | 为解决单一形态学算子在LiDAR数据滤波中的准确性和自动识别问题，提出了一种融合序列形态学算子的城区LiDAR滤波方法.在顾及多种形态学算子优势互补特性和LiDAR不同地物数据特点的基础上，首先利用形态学开运算及白top-hat变换剔除低粗差噪声和树木、汽车、电力线等小型地物，然后利用形态学梯度查找大型建筑物边缘，最后利用连通性分析和二值形态学重建方法剔除大型建筑物，获得准确的地面与地物分类点.使用ISPRS提供的不同复杂度9组城区测试数据进行实验，结果表明，本文方法的Ⅰ类、Ⅱ类及总误差均值分别达到6.90%、3.33%和5.44%，整体分类与自动识别性能优于常规滤波算法.
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| 关 键 词： | 形态学滤波 白top-hat变换 形态学梯度 二值形态学重建 激光雷达 |
| 收稿时间： | 2013-02-07 |
A Filtering Method for LiDAR Cloud Points in Urban Areas Based on Serial Morphological Operators |
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| SUN Meiling,LI Yongshu,CHEN Qiang,CAI Guolin.A Filtering Method for LiDAR Cloud Points in Urban Areas Based on Serial Morphological Operators[J].Journal of Southwest Jiaotong University,2013,26(6):1038-1044. |
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| Authors: | SUN Meiling LI Yongshu CHEN Qiang CAI Guolin |
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| Abstract: | To improve the accuracy of single morphological operator and automatic classification in LiDAR filtering, a new LiDAR filtering method based on serial morphological operators was presented. In view of the characteristics of morphological operators and the features of different objects in LiDAR point data, first morphological opening operator and white top-hat transformation were applied to LiDAR points with small window size for filtering low outliers and small objects (such as tree, car and electric-power line etc). Then morphological gradient was used to detect building edges. Finally, connectivity analysis and binary morphological reconstruction were applied for removing large buildings. As a result, ground points were retained and non-ground points were removed. The experimental results for nine different complexity urban data provided by ISPRS show that the average values of type Ⅰ, type Ⅱ and total errors are 6.90%, 3.33% and 5.44%. Compared with traditional filter methods, this method improves the effects of classification and automatic recognition. |
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