| 高精度CORS站像控点自动提取方法 |
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| 引用本文： | 梁丽芳,王燕云,田时雨,张恒才.高精度CORS站像控点自动提取方法[J].测绘通报,2022,0(11):123-127. |
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| 作者姓名： | 梁丽芳 王燕云 田时雨 张恒才 |
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| 作者单位： | 1. 河北省自然资源档案馆, 河北 石家庄 050031;2. 河北省第二测绘院, 河北 石家庄 050031;3. 中国科学院地理科学与资源研究所, 北京 100101 |
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| 基金项目： | 国家重点研发计划(2021YFB3900803);河北省自然资源厅项目(324-0801-JZN-OAFF) |
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| 摘 要： | CORS站持续稳定运行产生了海量高精度定位点数据,如何挖掘提取高精度可用像控点,以替代部分像控点人工选择或外业测绘工作,具有重要推广价值。本文提出了一种基于高精度CORS定位数据的像控点自动提取方法,并研发了像控点自动提取工具。首先采用Douglas-Peucker算法实现轨迹简化;然后利用ST-DBSCAN提取聚类中心;最后进一步栅格化,优化提取得到高可用像控点集。试验结果表明,像控点提取结果的准确率在94%以上,大大减少了像控点采集的工作量,重复利用高精度像控点信息具有广阔应用前景。
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| 关 键 词： | 像控点 连续运行参考站 精度 轨迹数据 数据挖掘 时空聚类 |
| 收稿时间： | 2021-11-15 |
An automatic extraction method of image control points from CORS high-precision data |
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| LIANG Lifang,WANG Yanyun,TIAN Shiyu,ZHANG Hengcai.An automatic extraction method of image control points from CORS high-precision data[J].Bulletin of Surveying and Mapping,2022,0(11):123-127. |
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| Authors: | LIANG Lifang WANG Yanyun TIAN Shiyu ZHANG Hengcai |
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| Institution: | 1. Natural Resources Data Archives of Hebei Province, Shijiazhuang 050031, China;2. The Second Surveying and Mapping Institute of Hebei Province, Shijiazhuang 050031, China;3. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China |
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| Abstract: | More and more high-precision location points haven been generated by the CORS stations, continuously. How to automatic extract image control points from these high-precision datasets become a huge challenge. It has important value for substituting traditional time-consuming and labor-intensive manual operation or measurement. In this paper, we develop an automatic extraction method of image control points form CORS high-precision data. First, it adopts Douglas-Pucker algorithm to simplify trajectory. Second, ST-DBSCAN is conducted to identify the cluster center. Then, an improved rasterization method is proposed to optimize the candidate of image control points. Finally, we develop an automatic extraction tool based on ArcPy to be deployed in production environment. The experimental results show that our proposed method greatly reduce the workload with the accuracy of 94%, and has a wide field of application with good prospects. |
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| Keywords: | image control points CORS accuracy trajectory data data mining spatio-temporal clustering |
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