一种基于GNSS和机器学习的InSAR大气改正方法

武帅莹, 刘国祥, 贾洪果, 张波, 张瑞, 毛文飞, 吴婷婷

武帅莹, 刘国祥, 贾洪果, 张波, 张瑞, 毛文飞, 吴婷婷. 一种基于GNSS和机器学习的InSAR大气改正方法[J]. 武汉大学学报 ( 信息科学版). DOI: 10.13203/j.whugis20220191
引用本文: 武帅莹, 刘国祥, 贾洪果, 张波, 张瑞, 毛文飞, 吴婷婷. 一种基于GNSS和机器学习的InSAR大气改正方法[J]. 武汉大学学报 ( 信息科学版). DOI: 10.13203/j.whugis20220191
WU Shuaiying, LIU Guoxiang, JIA Hongguo, ZHANG Bo, ZHANG Rui, MAO Wenfei, WU Tingting. An InSAR Atmospheric Correction Method Based on GNSS and Machine Learning[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20220191
Citation: WU Shuaiying, LIU Guoxiang, JIA Hongguo, ZHANG Bo, ZHANG Rui, MAO Wenfei, WU Tingting. An InSAR Atmospheric Correction Method Based on GNSS and Machine Learning[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20220191

一种基于GNSS和机器学习的InSAR大气改正方法

基金项目: 

国家重点研发计划(2021YFB2300501);国家自然科学基金(42171355,42071410);四川省科技计划(2021YFH0038,2018JY0564,2019ZDZX0042,2020JDTD0003)。

详细信息
    作者简介:

    武帅莹,博士生,主要从事雷达干涉测量及应用研究。shining@my.swjtu.edu.cn

    通讯作者:

    刘国祥,博士,教授,主要从事永久散射体雷达干涉及区域地表形变反演的研究。rsgxliu@swjtu.edu.cn

An InSAR Atmospheric Correction Method Based on GNSS and Machine Learning

  • 摘要:

    准确分离干涉相位中的大气延迟是提高合成孔径雷达干涉测量技术(InSAR)形变监测精度的关键所在。为此,本文提出一种基于机器学习的 GNSS 大气延迟建模方法,以南加州地区为研究区域,对 2019 年夏季和 2020 冬季各一副 Sentinel-1 短时基线干涉图进行大气改正。结果表明,经该方法大气改正后的 InSAR 干涉相位均方根误差(RMSE)平均降低了 78%,显著优于基于相位-高程关系的幂律函数和广泛使用的气象模型改正效果。同时,通过模拟实验,验证了本文方法在存在地表形变情况时仍然可靠。本研究可为基于 GNSS大气数据的 InSAR 对流层延迟时空制图提供参考。

    Abstract:

     Objectives:   The InSAR technique has been widely applied to monitoring land deformation with very high spatiotemporal resolution. The elimination of atmospheric delay in the interferometric phase is critical to achieving higher accuracy of land deformation monitoring with the technique.   Methods:   Hence this study proposes a GNSS atmospheric delay modeling method based on the combination of the machine learning approach and the K-means algorithm. In the experiment on the performance evaluation of the proposed, two sentinel-1 short-time baseline interferograms in southern California for summer 2019 and winter 2020 are used, respectively.   Results:   The results after the correction of tropospheric delays show that: (1) The root mean square error (RMSE) of the interferometric phase was reduced by an average of 78%, which is significantly better than the conventional methods, including thepower law function (highest 73%), GACOS estimation (61%), and weather models (58%); (2) The average correlation between the phase and elevation greatly decreased from 0.56 to 0.23.   Conclusions:   The experiment results indicate that the proposed method can effectively reduce the stratified tropospheric delay, and the modeled tropospheric delays are robust to surface deformation. Besides, the proposed method is potentially used in areas with low spatial density GNSS stations through simulation experiments. This study can provide a reference for InSAR tropospheric delay spatiotemporal mapping based on GNSS atmospheric data.

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