利用光学遥感影像光流场模型进行地表形变分析

丁明涛; 陈浩杰; 李振洪; 刘振江

doi:10.13203/j.whugis20240071

利用光学遥感影像光流场模型进行地表形变分析

丁明涛^{1, 2, 3, 4, 5,},
陈浩杰^{1, 2, 3},
李振洪^{1, 2, 3, 4, ,},
刘振江^{1, 2, 3}

1.
长安大学地质工程与测绘学院,陕西西安,710054
2.
黄土科学全国重点实验室,陕西西安,710054
3.
长安大学地学与卫星大数据研究中心,陕西西安,710054
4.
自然资源部生态地质与灾害防控重点实验室,陕西西安,710054
5.
智慧地球重点实验室,北京,100029

基金项目:

国家重点研发计划 2021YFC300400

国家自然科学基金 42374027

陕西省科技创新团队 2021TD-51

陕西省地学大数据与地质灾害防治创新团队（2022）

智慧地球重点实验室基金 KF2023YB04-01

高分专项川藏区域综合治理应用与规模化产业化示范项目 87-Y50G28-9001-22/23

中央高校基本科研业务费专项资金 300102262203

中央高校基本科研业务费专项资金 300102262902

详细信息

作者简介:
丁明涛,博士,副教授,主要从事机器学习、遥感影像处理研究。mingatoding@chd.edu.cn

通讯作者:
李振洪,博士,教授。zhenhong.li@chd.edu.cn

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- 文章访问数: 452
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出版历程
- 收稿日期: 2024-02-29
- 网络出版日期: 2024-05-30
- 刊出日期: 2024-08-04

Analysis of Surface Deformations on the Basis of Optical Flow Field Models from Optical Remote Sensing Images

DING Mingtao^{1, 2, 3, 4, 5,},
CHEN Haojie^{1, 2, 3},
LI Zhenhong^{1, 2, 3, 4, ,},
LIU Zhenjiang^{1, 2, 3}

1.
College of Geological Engineering and Geomatics, Chang'an University, Xi'an710054, China
2.
Key Laboratory of Loess, Xi'an710054, China
3.
Big Data Center for Geosciences and Satellites, Xi'an710054, China
4.
Key Laboratory of Ecological Geology and Disaster Prevention, Ministry of Natural Resources, Xi'an710054, China
5.
Laboratory of Smart Earth, Beijing100029, China

摘要

摘要:
光学遥感影像像素偏移量追踪是反演同震形变场和监测滑坡的一种重要手段。基于相关性匹配的传统像素偏移量追踪方法,通过搜索相关性最强的匹配窗口估计中心像素的位移,计算效率低且在大梯度形变区域失相关现象严重,存在形变边界提取不精确的问题。为高效获取精确的地表形变,将计算机视觉领域的光流场模型引入像素偏移量追踪问题,提出适用于光学遥感影像反演地表形变的光流场方法,给出像素偏移量时序反演的加权改进算法。通过塔吉克斯坦同震形变场模拟实验,评估光流场方法估计地表形变场的可行性及其在最小可探测形变方面的性能；通过加州地震同震形变场反演和白格滑坡偏移量估计实验,讨论光流场方法的计算效率优势和形变区域提取的精确性；通过白格滑坡时序形变分析,进一步论述利用光流场方法估计大梯度形变的有效性和时序反演加权改进算法的鲁棒性。结果显示,相比于传统窗口相关性匹配方法,光流场方法的偏移量追踪精度为0.032像素,计算效率提升了20倍左右,形变区范围提取精度提升了25.9%；改进的加权时序反演算法将光学遥感影像东西向和南北向位移估计的不确定性分别降低了16.2％和12.4％。
- 光流场方法 /
- 滑坡 /
- 同震形变 /
- 计算效率 /
- 加权时序反演算法
Abstract:
Objectives
Pixel offset tracking (POT) for optical remote sensing imagery is widely used to invert coseismic deformation fields and monitor landslides. Traditional pixel offset tracking method estimates the displacement of the central pixel by searching for the matching window with the highest correlation, which is computationally inefficient and suffers from inaccurate deformation boundary extraction due to the decoherence effects in the region with dynamical deformation. We introduce the optical flow field model commonly used in computer vision to the pixel offset tracking problem to obtain accurate surface deformation efficiently.
Methods
The optical flow field method applicable to optical remote sensing images and the improved inversion algorithm for the time series analysis are proposed to inverse the surface deformation. Experiments on the simulated coseismic deformation fields in Tajikistan are detailed to assess the feasibility and the minimum detectable deformation of the optical flow field method. The advantages of the proposed method over computational cost and deformation boundary extraction accuracy are illustrated by the co-seismic deformation field of the California earthquake and the displacement of the Baige landslide. Furthermore, the performance on estimating large gradient deformation and the robustness of the improved time series inversion algorithm are discussed by analyzing the time series deformation of the Baige landslide.
Results
The results show that compared with the traditional window correlation matching method, the optical flow field method has an offset tracking accuracy of 0.032 pixel, which improves the computational efficiency by about 20 times, and the accuracy of the deformation zone is improved by 25.9%. The time series weighted inversion algorithm reduces the uncertainties in the estimation of east-west and north-south displacements of optical remote sensing images by 16.2% and 12.4%, respectively.
Conclusions
The proposed method alleviates the pixel offset tracking problem in the boundary region with large gradient deformation.
- optical flow field method /
- landslides /
- coseismic deformation /
- computational efficiency /
- weighted time series inversion algorithm
http://ch.whu.edu.cn/cn/article/doi/10.13203/j.whugis20240071

HTML全文

http://ch.whu.edu.cn/cn/article/doi/10.13203/j.whugis20240071

图 1 光流场方法地表形变分析流程

Figure 1. Analysis Process of Surface Deformation Based on Optical Flow Field Model

下载: 全尺寸图片幻灯片

图 2 光流场模型

Figure 2. Optical Flow Field Model

下载: 全尺寸图片幻灯片

图 3 2015⁃12⁃05塔吉克斯坦Sentinel-2影像及模拟震后影像

Figure 3. Sentinel-2 Image of Takixtan on December 5, 2015 and Simulated Post-Earthquake Image

下载: 全尺寸图片幻灯片

图 4 模拟形变场、光流场方法观测结果、形变残差及最小可探测形变幅度分析

Figure 4. Simulated Deformation Fields, Observations Based on Optical Flow Field Methods, Analysis of Deformation Residuals and Minimum Detectable Deformation Amplitude

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图 5 南加州区域里奇克雷斯特地震发生前后Sentinel-2影像

Figure 5. Sentinel-2 Images Taken Before and After the Ridgecrest Earthquake in the Southern California

下载: 全尺寸图片幻灯片

图 6 光流场方法观测结果与COSI-Corr方法观测结果对比分析

Figure 6. Comparative Analysis of Observation Results of Optical Flow Field Method and COSI-Corr Method

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图 7 光流场方法观测结果与COSI-Corr方法观测结果线性拟合散点密度图

Figure 7. Scatter Diagram of Linear Fitting Between Optical Flow Field Observation Results and COSI-Corr Observation Results

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图 8 白格滑坡灾前不同时期的Sentinel-2影像

Figure 8. Sentinel-2 Images Taken at Different Times Before the Baige Landslide Disaster

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图 9 光流场方法观测结果与COSI-Corr方法观测结果定性对比

Figure 9. Qualitative Comparison of Optical Flow Field Method Observations with COSI-Corr Method Observations

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图 10 白格滑坡区域概况及其垮塌前后Sentinel-2影像

Figure 10. Regional Overview of the Baige Landslide and the Sentinel-2 Images Taken Before and After the Collapse

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图 11 白格滑坡位移时间序列及特征点累计形变、滑移速率

Figure 11. Time Series of the Displacement of the Baige Landslide as well as the Cumulative Deformation and the Slip Rates at the Feature Points

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图 12 光流场方法最佳参数选择

Figure 12. Optimal Parameter Selection for Optical Flow Field Method

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图 13 改进加权时序反演算法的可靠性及有效性分析

Figure 13. Reliability and Validity Analysis of Improved Weighted Time Series Inversion Algorithm

下载: 全尺寸图片幻灯片

表 1 Sentinel-2影像参数

Table 1 Parameters of Sentinel-2 Images

影像编号	采集时间	季节	影像编号	采集时间	季节
0	2015-11-13	冬季	16	2017-12-02	冬季
1	2015-12-23	冬季	17	2017-12-07	冬季
2	2016-05-11	春季	18	2017-12-17	冬季
3	2016-11-07	秋季	19	2017-12-22	冬季
4	2016-12-07	冬季	20	2018-01-11	冬季
5	2017-01-16	冬季	21	2018-01-16	冬季
6	2017-02-05	冬季	22	2018-01-26	冬季
7	2017-05-16	春季	23	2018-02-05	冬季
8	2017-07-15	夏季	24	2018-03-22	春季
9	2017-08-04	夏季	25	2018-04-16	春季
10	2017-08-24	夏季	26	2018-05-21	春季
11	2017-10-18	秋季	27	2018-06-05	夏季
12	2017-10-28	秋季	28	2018-06-10	夏季
13	2017-11-07	秋季	29	2018-06-25	夏季
14	2017-11-17	秋季	30	2018-07-25	夏季
15	2017-11-27	秋季

下载: 导出CSV

表 2 Sentinel-2不同波段稳定区域标准差比较

Table 2 Comparison of Standard Deviations of Sentinel-2 in Different Wavebands

影像对	方向	标准差/m
影像对	方向	蓝色波段	绿色波段	红色波段	近红外波段	SR波段	灰度波段
2017-07-15—2018-07-25	东西向	0.675	0.429	0.372	1.109	0.597	0.361
2017-07-15—2018-07-25	南北向	0.543	0.496	0.445	0.834	0.864	0.436
2015-11-13—2016-11-07	东西向	0.673	0.563	0.571	1.488	0.614	0.531
2015-11-13—2016-11-07	南北向	0.393	0.258	0.295	0.813	0.421	0.213

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表 3 加州地震COSI-Corr方法与光流场方法结果定量对比

Table 3 Quantitative Comparison of Results from the COSI-Corr Method and Optical Flow Field Method for the California Earthquake

方向	残差平均值/m	残差标准差/m	光流场方法稳定区域标准差/m	COSI-Corr方法稳定区域标准差/m	皮尔逊相关系数	拟合RMSE/m	光流场方法解算时间/min	COSI-Corr方法解算时间/min
东西向	0.012	0.449	0.25	0.36	0.785	0.27	5	96
南北向	0.017	0.443	0.29	0.37	0.882	0.28	5	96

下载: 导出CSV

表 4 白格滑坡COSI-Corr方法与光流场方法结果定量对比

Table 4 Quantitative Comparison of the Results of COSI-Corr Method and Optical Flow Field Method for the Baige Landslide

影像类型	方法	稳定区域标准差/m	最大位移/m	平均位移/m	解算时间/s	覆盖范围/%
光学影像	光流场方法	0.36	34.15	27.75	7	98.2
光学影像	COSI-Corr（64×64）	0.28	34.76	28.13	177	72.3

下载: 导出CSV

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