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第 50 卷第 9 期 武帅莹等:一种基于 GNSS 和机器学习的 InSAR 大气改正方法 1877
[52] YU C, PENNA N T, LI Z H. Generation of Real- CHI H, et al. Improving Tropospheric Corrections
Time Mode High-Resolution Water Vapor Fields on Large-Scale Sentinel-1 Interferograms Using a
from GPS Observations[J]. Journal of Geophysical Machine Learning Approach for Integration with
Research: Atmospheres, 2017, 122(3): 2008-2025. GNSS-Derived Zenith Total Delay (ZTD)[J]. Re⁃
[53] WALTERS R J, ELLIOTT J R , LI Z , et al. mote Sensing of Environment, 2020, 239: 111608.
Rapid Strain Accumulation on the Ashkabad Fault [60] TANG W, LIAO M S, ZHANG L, et al. Study
(Turkmenistan) from Atmosphere-Corrected In⁃ on InSAR Tropospheric Correction Using Global At⁃
SAR[J]. Journal of Geophysical Research: Solid mospheric Reanalysis Products[J]. 2017, 60(2):
Earth, 2013, 118(7): 3674-3690. 527-540.
[54] WILGAN K, HURTER F, GEIGER A, et al. [61] PRATS-IRAOLA P, SCHEIBER R, MAROTTI
Tropospheric Refractivity and Zenith Path Delays L, et al. TOPS Interferometry with TerraSAR-X
from Least-Squares Collocation of Meteorological [J]. IEEE Transactions on Geoscience and Remote
and GNSS Data[J]. Journal of Geodesy, 2017, 91 Sensing, 2012, 50(8): 3179-3188.
(2): 117-134. [62] COSTANTINI M. A Novel Phase Unwrapping
[55] SPOORTHI G E, GORTHI S, GORTHI R K S Method Based on Network Programming[J]. IEEE
S. PhaseNet: A Deep Convolutional Neural Net⁃ Transactions on Geoscience and Remote Sensing,
work for Two-Dimensional Phase Unwrapping[J]. 1998, 36(3): 813-821.
IEEE Signal Processing Letters, 2019, 26(1): 54-58. [63] BÉLISLE E, HUANG Z, LE DIGABEL S, et al.
[56] MA L, LIU Y, ZHANG X L, et al. Deep Learning Evaluation of Machine Learning Interpolation Tech⁃
in Remote Sensing Applications: A Meta-Analysis niques for Prediction of Physical Properties[J]. Com⁃
and Review[J]. ISPRS Journal of Photogrammetry putational Materials Science, 2015, 98: 170-177.
and Remote Sensing, 2019, 152: 166-177. [64] 李永生, 张景发, 姜文亮, 等 . 基于网络法时序 In⁃
[57] JIN J C, CHEN G, MENG X M, et al. Prediction SAR 大气误差校正方法研究[J]. 大地测量与地球
of River Damming Susceptibility by Landslides 动力学, 2015, 35(1): 145-149.
Based on a Logistic Regression Model and InSAR LI Yongsheng, ZHANG Jingfa, JIANG Wenliang,
Techniques: A Case Study of the Bailong River Ba⁃ et al. Atmospheric Artifacts Correction in Time Se⁃
sin, China[J]. Engineering Geology, 2022, 299: ries InSAR Using Network Methodology[J]. Jour⁃
106562. nal of Geodesy and Geodynamics, 2015, 35(1):
[58] XUE X M. Time-Dependent Modeling of Volcano 145-149.
Deformation in Alaska and Transient Detection Using [65] BEKAERT D P S, WALTERS R J, WRIGHT T
Machine Learning Methods[D]. East Lansing, MI, J, et al. Statistical Comparison of InSAR Tropo⁃
USA: Michigan State University, 2021. spheric Correction Techniques[J]. Remote Sensing
[59] SHAMSHIRI R, MOTAGH M, NAHAVAND⁃ of Environment, 2015, 170: 40-47.

