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基于光学遥感自适应偏移追踪技术的滑坡监测

谭晴1,3, 吴希文2,3, 王华4, 邝健明2   

  1. 1. 广东工业大学 信息工程学院, 广东 广州 510006;
    2. 广东工业大学 土木与交通工程学院, 广东 广州 510006;
    3. 广东工业大学 大湾区城市环境安全与绿色发展教育部重点实验室, 广东 广州 510006;
    4. 华南农业大学 资源环境学院,广东 广州 510642
  • 收稿日期:2023-06-01 出版日期:2024-05-25 发布日期:2024-06-17
  • 通信作者: 吴希文(1983-),男,教授,博士生导师,主要研究方向为InSAR与地表形变监测,E-mail:hayman.ng@gdut.edu.cn E-mail:hayman.ng@gdut.edu.cn
  • 作者简介:谭晴(2000-),男,硕士研究生,主要研究方向为InSAR与地表形变监测,E-mail:1654334401@qq.com
  • 基金资助:
    国家自然科学基金资助面上项目(42274016)；广东省自然科学基金资助项目(2021A1515011483)；广东省林业科学数据中心项目(2021B1212100004)

Landslide Monitoring Based on Optical Remote Sensing Adaptive Offset Tracking Method

Tan Qing1,3, Ng Alex Hay-Man2,3, Wang Hua4, Kuang Jian-ming2   

  1. 1. School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China;
    2. School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, China;
    3. Key Laboratory for City Cluster Environmental Safety and Green Development of the Ministry of Education, Guangdong University of Technology, Guangzhou 510006, China;
    4. College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China
  • Received:2023-06-01 Online:2024-05-25 Published:2024-06-17

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摘要： 传统偏移跟踪方法主要依赖规则匹配窗口的归一化互相关(Normalized Cross Correlation, NCC) 操作。然而，在光学遥感影像分析中，规则窗口内往往存在如水体、阴影、云层等干扰因素的像素。当应用于滑坡监测时，这些干扰因素可能会导致偏移估计出现错误。为了解决这个问题，本文提出了一种自适应偏移跟踪算法。在偏移估计前，先进行预处理，识别出研究区域中水体、阴影、云层等干扰因素的位置，并生成相应的水体、阴影、云层掩膜。然后在偏移估计过程中，通过干扰因素的掩膜找出互相关窗口中干扰因素的位置，并在计算时排除互相关窗口内的干扰因素像素，以此来提高偏移估计的准确性和可靠性。通过在白格滑坡的实验验证，证明了该方法能显著提高偏移跟踪的准确性和可靠性。

关键词: 光学遥感, 归一化互相关, 白格滑坡, 自适应偏移跟踪

Abstract: Traditional offset tracking primarily relies on normalized cross correlation tracking method based on the regular matching window. However, in the analysis of optical remote sensing images, pixels representing disturbance factors such as cloud layers, water bodies, shadows are often present within the regular window. When applied to landslide monitoring, these disturbance factors may lead to errors in the offset estimation. In order to address this issue, an adaptive offset tracking algorithm is presented. Prior to the offset estimation, a pre-processing step is carried out to identify the locations of these disturbance factors in the study area and generate the corresponding masks. During offset estimation process, the disturbance factors of cross-correlation window can be found from its masks, then pixels representing disturbance factors within the cross-correlation window are excluded, thereby improving the accuracy and reliability of offset estimation experimental validation on the Baige landslide , which has demonstrated that this method can significantly enhance the accuracy and reliability of offset tracking.

Key words: optical remote sensing, normalized cross correlation, the Baige landslide, adaptive offset tracking

中图分类号: 

  • P642.22
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