Sequential DS-ISBAS InSAR Deformation Parameter Dynamic Estimation and Quality Evaluation
Today, synthetic aperture radar (SAR) satellites provide large amounts of SAR data at unprecedented temporal resolutions, which promotes hazard dynamic monitoring and disaster mitigation with interferometric SAR (InSAR) technology. This study focuses on big InSAR data dynamical processing in areas o...
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| Format: | Article |
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MDPI AG
2023-04-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/8/2097 |
| _version_ | 1827743850167271424 |
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| author | Baohang Wang Chaoying Zhao Qin Zhang Xiaojie Liu Zhong Lu Chuanjin Liu Jianxia Zhang |
| author_facet | Baohang Wang Chaoying Zhao Qin Zhang Xiaojie Liu Zhong Lu Chuanjin Liu Jianxia Zhang |
| author_sort | Baohang Wang |
| collection | DOAJ |
| description | Today, synthetic aperture radar (SAR) satellites provide large amounts of SAR data at unprecedented temporal resolutions, which promotes hazard dynamic monitoring and disaster mitigation with interferometric SAR (InSAR) technology. This study focuses on big InSAR data dynamical processing in areas of serious decorrelation and large gradient deformation. A new stepwise temporal phase optimization method is proposed to alleviate the decorrelation, customized for deformation parameter dynamical estimation. Subsequently, the sequential estimation theory is introduced to the intermittent small baseline subset (ISBAS) approach to dynamically obtain deformation time series with dense coherent targets. Then, we analyze the reason for the unstable accuracy of deformation parameters using sequential distributed scatterers-ISBAS technology, and construct five indices to describe the quality of deformation parameters pixel-by-pixel. Finally, real data of the post-failure Baige landslide at the Jinsha River in China is used to demonstrate the validity of the proposed approach. |
| first_indexed | 2024-03-11T04:34:22Z |
| format | Article |
| id | doaj.art-bfaab442597746e68d89f2c7e4160cb6 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T04:34:22Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-bfaab442597746e68d89f2c7e4160cb62023-11-17T21:12:00ZengMDPI AGRemote Sensing2072-42922023-04-01158209710.3390/rs15082097Sequential DS-ISBAS InSAR Deformation Parameter Dynamic Estimation and Quality EvaluationBaohang Wang0Chaoying Zhao1Qin Zhang2Xiaojie Liu3Zhong Lu4Chuanjin Liu5Jianxia Zhang6School of Geography and Oceanography, Minjiang University, Fuzhou 350108, ChinaSchool of Geological Engineering and Geomatics, Chang’an University, Xi’an 710054, ChinaSchool of Geological Engineering and Geomatics, Chang’an University, Xi’an 710054, ChinaSchool of Geological Engineering and Geomatics, Chang’an University, Xi’an 710054, ChinaRoy M. Huffington Department of Earth Sciences, Southern Methodist University, Dallas, TX 75275, USAThe Second Monitoring and Application Center, China Earthquake Administration, Xi’an 710054, ChinaSchool of Geography and Oceanography, Minjiang University, Fuzhou 350108, ChinaToday, synthetic aperture radar (SAR) satellites provide large amounts of SAR data at unprecedented temporal resolutions, which promotes hazard dynamic monitoring and disaster mitigation with interferometric SAR (InSAR) technology. This study focuses on big InSAR data dynamical processing in areas of serious decorrelation and large gradient deformation. A new stepwise temporal phase optimization method is proposed to alleviate the decorrelation, customized for deformation parameter dynamical estimation. Subsequently, the sequential estimation theory is introduced to the intermittent small baseline subset (ISBAS) approach to dynamically obtain deformation time series with dense coherent targets. Then, we analyze the reason for the unstable accuracy of deformation parameters using sequential distributed scatterers-ISBAS technology, and construct five indices to describe the quality of deformation parameters pixel-by-pixel. Finally, real data of the post-failure Baige landslide at the Jinsha River in China is used to demonstrate the validity of the proposed approach.https://www.mdpi.com/2072-4292/15/8/2097sequential deformationInSARintermittent small baseline subsetphase optimizationBaige landslide |
| spellingShingle | Baohang Wang Chaoying Zhao Qin Zhang Xiaojie Liu Zhong Lu Chuanjin Liu Jianxia Zhang Sequential DS-ISBAS InSAR Deformation Parameter Dynamic Estimation and Quality Evaluation Remote Sensing sequential deformation InSAR intermittent small baseline subset phase optimization Baige landslide |
| title | Sequential DS-ISBAS InSAR Deformation Parameter Dynamic Estimation and Quality Evaluation |
| title_full | Sequential DS-ISBAS InSAR Deformation Parameter Dynamic Estimation and Quality Evaluation |
| title_fullStr | Sequential DS-ISBAS InSAR Deformation Parameter Dynamic Estimation and Quality Evaluation |
| title_full_unstemmed | Sequential DS-ISBAS InSAR Deformation Parameter Dynamic Estimation and Quality Evaluation |
| title_short | Sequential DS-ISBAS InSAR Deformation Parameter Dynamic Estimation and Quality Evaluation |
| title_sort | sequential ds isbas insar deformation parameter dynamic estimation and quality evaluation |
| topic | sequential deformation InSAR intermittent small baseline subset phase optimization Baige landslide |
| url | https://www.mdpi.com/2072-4292/15/8/2097 |
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