Time-Series InSAR with Deep-Learning-Based Topography-Dependent Atmospheric Delay Correction for Potential Landslide Detection
Synthetic aperture radar interferometry (InSAR) has emerged as an effective technique for monitoring potentially unstable landslides and has found widespread application. Nevertheless, in mountainous reservoir regions, the precision of time-series InSAR outcomes is often constrained by topography-de...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/22/5287 |
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| author | Hao Zhou Keren Dai Xiaochuan Tang Jianming Xiang Rongpeng Li Mingtang Wu Yangrui Peng Zhenhong Li |
| author_facet | Hao Zhou Keren Dai Xiaochuan Tang Jianming Xiang Rongpeng Li Mingtang Wu Yangrui Peng Zhenhong Li |
| author_sort | Hao Zhou |
| collection | DOAJ |
| description | Synthetic aperture radar interferometry (InSAR) has emerged as an effective technique for monitoring potentially unstable landslides and has found widespread application. Nevertheless, in mountainous reservoir regions, the precision of time-series InSAR outcomes is often constrained by topography-dependent atmospheric delay (TDAD) effects. To address this limitation, we propose a novel InSAR time-series method that integrates TDAD correction. This approach employs advanced deep learning algorithms to individually model and mitigate TDAD for each interferogram, thereby enhancing the accuracy of small baseline subset InSAR (SBAS-InSAR) and stacking InSAR time-series analyses. Utilizing Sentinel-1 data, we apply this method to identify potential landslides in the Baihetan reservoir area, located in southwestern China, where we successfully identified 26 potential landslide sites. Comparative experimental results demonstrate a significant reduction (averaging 70% and reaching up to 90%) in phase standard deviation (StdDev) in the corrected interferograms, indicating a marked decrease in phase–topography correlation. Furthermore, the corrected time-series InSAR results effectively remove TDAD signals, leading to clearer displacement boundaries and a remarkable reduction in other spurious displacement signals. Overall, this method efficiently addresses TDAD in time-series InSAR, enabling precise identification of potentially unstable landslides influenced by TDAD, and providing essential technical support for early landslide hazard detection using time-series InSAR. |
| first_indexed | 2024-03-09T16:29:05Z |
| format | Article |
| id | doaj.art-10d6b027a55e48bf86a3274465b97cc2 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T16:29:05Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-10d6b027a55e48bf86a3274465b97cc22023-11-24T15:04:13ZengMDPI AGRemote Sensing2072-42922023-11-011522528710.3390/rs15225287Time-Series InSAR with Deep-Learning-Based Topography-Dependent Atmospheric Delay Correction for Potential Landslide DetectionHao Zhou0Keren Dai1Xiaochuan Tang2Jianming Xiang3Rongpeng Li4Mingtang Wu5Yangrui Peng6Zhenhong Li7College of Earth Science, Chengdu University of Technology, Chengdu 610059, ChinaCollege of Earth Science, Chengdu University of Technology, Chengdu 610059, ChinaCollege of Computer Science and Cyber Security (Oxford Brookes College), Chengdu University of Technology, Chengdu 610059, ChinaCollege of Earth Science, Chengdu University of Technology, Chengdu 610059, ChinaCollege of Earth Science, Chengdu University of Technology, Chengdu 610059, ChinaHuadong Engineering Corporation Limited, Hangzhou 311122, ChinaCollege of Earth Science, Chengdu University of Technology, Chengdu 610059, ChinaCollege of Geological Engineering and Geomatics, Chang’an University, Xi’an 710064, ChinaSynthetic aperture radar interferometry (InSAR) has emerged as an effective technique for monitoring potentially unstable landslides and has found widespread application. Nevertheless, in mountainous reservoir regions, the precision of time-series InSAR outcomes is often constrained by topography-dependent atmospheric delay (TDAD) effects. To address this limitation, we propose a novel InSAR time-series method that integrates TDAD correction. This approach employs advanced deep learning algorithms to individually model and mitigate TDAD for each interferogram, thereby enhancing the accuracy of small baseline subset InSAR (SBAS-InSAR) and stacking InSAR time-series analyses. Utilizing Sentinel-1 data, we apply this method to identify potential landslides in the Baihetan reservoir area, located in southwestern China, where we successfully identified 26 potential landslide sites. Comparative experimental results demonstrate a significant reduction (averaging 70% and reaching up to 90%) in phase standard deviation (StdDev) in the corrected interferograms, indicating a marked decrease in phase–topography correlation. Furthermore, the corrected time-series InSAR results effectively remove TDAD signals, leading to clearer displacement boundaries and a remarkable reduction in other spurious displacement signals. Overall, this method efficiently addresses TDAD in time-series InSAR, enabling precise identification of potentially unstable landslides influenced by TDAD, and providing essential technical support for early landslide hazard detection using time-series InSAR.https://www.mdpi.com/2072-4292/15/22/5287topography-dependent atmospheric delayBaihetan reservoir areapotential landslidestime-series InSARdeep neural network |
| spellingShingle | Hao Zhou Keren Dai Xiaochuan Tang Jianming Xiang Rongpeng Li Mingtang Wu Yangrui Peng Zhenhong Li Time-Series InSAR with Deep-Learning-Based Topography-Dependent Atmospheric Delay Correction for Potential Landslide Detection Remote Sensing topography-dependent atmospheric delay Baihetan reservoir area potential landslides time-series InSAR deep neural network |
| title | Time-Series InSAR with Deep-Learning-Based Topography-Dependent Atmospheric Delay Correction for Potential Landslide Detection |
| title_full | Time-Series InSAR with Deep-Learning-Based Topography-Dependent Atmospheric Delay Correction for Potential Landslide Detection |
| title_fullStr | Time-Series InSAR with Deep-Learning-Based Topography-Dependent Atmospheric Delay Correction for Potential Landslide Detection |
| title_full_unstemmed | Time-Series InSAR with Deep-Learning-Based Topography-Dependent Atmospheric Delay Correction for Potential Landslide Detection |
| title_short | Time-Series InSAR with Deep-Learning-Based Topography-Dependent Atmospheric Delay Correction for Potential Landslide Detection |
| title_sort | time series insar with deep learning based topography dependent atmospheric delay correction for potential landslide detection |
| topic | topography-dependent atmospheric delay Baihetan reservoir area potential landslides time-series InSAR deep neural network |
| url | https://www.mdpi.com/2072-4292/15/22/5287 |
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