Bridge detection in light detecting and ranging data based on morphological filter and skeleton extraction
Bridge detection in light detecting and ranging data based on morphological filter and skeleton extraction
复制标题
基于形态滤波和骨架提取的光探测测距数据中的桥梁检测
DOI：
10.1117/1.jrs.8.083610
复制
发表时间：
2014
影响因子：
1.7
通讯作者：
Miao, Qiguang
中科院分区：
文献类型：
--
作者：
Duan, Yiping;Song, Jianfeng;Miao, Qiguang
摘要提出了一种基于自适应形态滤波和骨架提取的激光雷达（LiDAR）数据自动检测水上桥梁的方法。它受到机器学习中数据驱动和基于推理的方法的启发。首先，我们的算法考虑了激光雷达数据的三维特性。我们设计了一种自适应形态滤波器，将数据分为两类：接地点和非接地点。其次，利用高程特征对河流进行提取。这样，可以大大减少搜索空间。第三，通过形态细化算法将河流表示为一条骨架线。这种简洁的表示使得所提出的方法更有效地检测桥梁。最后提出了基于骨架线的最短距离规则。利用分类图和规则的融合来检测桥梁。在不同场景下的实验证明了该方法的灵活性。实验结果表明，该方法具有良好的水上桥梁检测性能。
Abstract An automatic approach for detecting bridges over water from light detection and ranging (LiDAR) data based on adaptive morphological filter and skeleton extraction is presented. It is inspired by data-driven and inference-based methods in machine learning. First, the three-dimensional characteristics of LiDAR data are considered in our algorithm. We design an adaptive morphological filter to classify the data into two classes, ground points and nonground points. Second, the elevation feature is used to extract the river. In this way, the search space can be greatly reduced. Third, the river is represented as a skeleton line by the morphological thinning algorithm. This concise representation makes the proposed approach more efficient to detect bridges. Finally, we propose the shortest distance rule based on the skeleton line. The fusion of the classification map and the rule is used to detect bridges. The flexibility of the proposed method is demonstrated by experiments on several different scenes. The experimental results show that the proposed approach has good performance in detecting a bridge over water.
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DOI：
10.1016/j.isprsjprs.2006.07.004
发表时间：
2006-10
影响因子：
12.7
作者：
G. Sithole;G. Vosselman
通讯作者：
G. Sithole;G. Vosselman
DOI：
10.1109/tgrs.2008.923631
发表时间：
2008-09-01
影响因子：
8.2
作者：
Chaudhuri, D.;Samal, Ashok
通讯作者：
Samal, Ashok
DOI：
--
发表时间：
2011
期刊：
Journal of Test and Measurement Technology
影响因子：
--
作者：
Huang Hongping
通讯作者：
Huang Hongping
DOI：
10.1109/dfua.2003.1219959
发表时间：
2003-05
期刊：
2003 2nd GRSS/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas
影响因子：
--
作者：
G. Sithole;G. Vosselman
通讯作者：
G. Sithole;G. Vosselman
影响因子：
4.8
作者：
Xiangyun Hu;Xiaokai Li;Yongjun Zhang
通讯作者：
Xiangyun Hu;Xiaokai Li;Yongjun Zhang