Robust Time-Series InSAR Deformation Monitoring by Integrating Variational Mode Decomposition and Gated Recurrent Units
Continuous and large-scale surface deformation monitoring is critical for the comprehension of natural hazards and environmental changes. This can be facilitated by time-series interferometric synthetic aperture radar (TS-InSAR), which provides unprecedented spatial and temporal resolution. However,...
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10595127/ |
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| author | Peifeng Ma Zeyu Jiao Zherong Wu |
| author_facet | Peifeng Ma Zeyu Jiao Zherong Wu |
| author_sort | Peifeng Ma |
| collection | DOAJ |
| description | Continuous and large-scale surface deformation monitoring is critical for the comprehension of natural hazards and environmental changes. This can be facilitated by time-series interferometric synthetic aperture radar (TS-InSAR), which provides unprecedented spatial and temporal resolution. However, the original TS-InSAR measurements, being a superposition of trend, seasonal, and noise signals, often suffer from outlier and annual seasonal variations due to the influences of atmospheric delay, especially in coastal and mountainous areas, resulting in skewed monitoring if neglected. To address these issues, an integration method of variational mode decomposition and gated recurrent unit (VMD-GRU) is proposed in this study to enhance the robustness of continuous large-scale surface deformation monitoring. The VMD decomposes low-frequency trend, specific-frequency seasonal, and high-frequency noise components from the original TS-InSAR data via frequency-domain variational optimization first. Then, by eliminating the seasonal component decomposed by VMD from the original time series, the time series is reconstructed, effectively removing the influence of annual seasonal variations. Subsequently, GRU is utilized to further eradicate noise from the reconstructed time series, mitigating the influence of outliers and noise, thereby yielding a trend component that intuitively reflects surface deformation. Experiments on physical-based synthetic and real-world datasets demonstrate that the proposed VMD-GRU outperforms the existing methods. By introducing the frequency priors, the proposed method significantly enhances the robustness and accuracy of continuous large-scale surface deformation monitoring, providing a more reliable understanding of natural hazards and environmental changes. |
| format | Article |
| id | doaj-art-dd8ac32d7da543679d9796b56269cc88 |
| institution | Kabale University |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-dd8ac32d7da543679d9796b56269cc882025-01-21T00:00:28ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-01183208322110.1109/JSTARS.2024.342667610595127Robust Time-Series InSAR Deformation Monitoring by Integrating Variational Mode Decomposition and Gated Recurrent UnitsPeifeng Ma0https://orcid.org/0000-0002-1457-5388Zeyu Jiao1https://orcid.org/0000-0002-8012-7663Zherong Wu2https://orcid.org/0000-0002-9536-1348Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Hong Kong, SAR, ChinaInstitute of Space and Earth Information Science, The Chinese University of Hong Kong, Hong Kong, SAR, ChinaInstitute of Space and Earth Information Science, The Chinese University of Hong Kong, Hong Kong, SAR, ChinaContinuous and large-scale surface deformation monitoring is critical for the comprehension of natural hazards and environmental changes. This can be facilitated by time-series interferometric synthetic aperture radar (TS-InSAR), which provides unprecedented spatial and temporal resolution. However, the original TS-InSAR measurements, being a superposition of trend, seasonal, and noise signals, often suffer from outlier and annual seasonal variations due to the influences of atmospheric delay, especially in coastal and mountainous areas, resulting in skewed monitoring if neglected. To address these issues, an integration method of variational mode decomposition and gated recurrent unit (VMD-GRU) is proposed in this study to enhance the robustness of continuous large-scale surface deformation monitoring. The VMD decomposes low-frequency trend, specific-frequency seasonal, and high-frequency noise components from the original TS-InSAR data via frequency-domain variational optimization first. Then, by eliminating the seasonal component decomposed by VMD from the original time series, the time series is reconstructed, effectively removing the influence of annual seasonal variations. Subsequently, GRU is utilized to further eradicate noise from the reconstructed time series, mitigating the influence of outliers and noise, thereby yielding a trend component that intuitively reflects surface deformation. Experiments on physical-based synthetic and real-world datasets demonstrate that the proposed VMD-GRU outperforms the existing methods. By introducing the frequency priors, the proposed method significantly enhances the robustness and accuracy of continuous large-scale surface deformation monitoring, providing a more reliable understanding of natural hazards and environmental changes.https://ieeexplore.ieee.org/document/10595127/Frequency priorsgated recurrent units (GRUs)surface deformation monitoringtime-series InSARvariational mode decomposition (VMD) |
| spellingShingle | Peifeng Ma Zeyu Jiao Zherong Wu Robust Time-Series InSAR Deformation Monitoring by Integrating Variational Mode Decomposition and Gated Recurrent Units IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Frequency priors gated recurrent units (GRUs) surface deformation monitoring time-series InSAR variational mode decomposition (VMD) |
| title | Robust Time-Series InSAR Deformation Monitoring by Integrating Variational Mode Decomposition and Gated Recurrent Units |
| title_full | Robust Time-Series InSAR Deformation Monitoring by Integrating Variational Mode Decomposition and Gated Recurrent Units |
| title_fullStr | Robust Time-Series InSAR Deformation Monitoring by Integrating Variational Mode Decomposition and Gated Recurrent Units |
| title_full_unstemmed | Robust Time-Series InSAR Deformation Monitoring by Integrating Variational Mode Decomposition and Gated Recurrent Units |
| title_short | Robust Time-Series InSAR Deformation Monitoring by Integrating Variational Mode Decomposition and Gated Recurrent Units |
| title_sort | robust time series insar deformation monitoring by integrating variational mode decomposition and gated recurrent units |
| topic | Frequency priors gated recurrent units (GRUs) surface deformation monitoring time-series InSAR variational mode decomposition (VMD) |
| url | https://ieeexplore.ieee.org/document/10595127/ |
| work_keys_str_mv | AT peifengma robusttimeseriesinsardeformationmonitoringbyintegratingvariationalmodedecompositionandgatedrecurrentunits AT zeyujiao robusttimeseriesinsardeformationmonitoringbyintegratingvariationalmodedecompositionandgatedrecurrentunits AT zherongwu robusttimeseriesinsardeformationmonitoringbyintegratingvariationalmodedecompositionandgatedrecurrentunits |