Land-Cover Controls on the Accuracy of PS-InSAR-Derived Concrete Track Settlement Measurements.
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| Title: | Land-Cover Controls on the Accuracy of PS-InSAR-Derived Concrete Track Settlement Measurements. |
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| Authors: | Kim, Byung-kyu1 (AUTHOR), Kim, Joonyoung2 (AUTHOR), Park, Jeongjun3 (AUTHOR), Lee, Ilwha1,4 (AUTHOR), Yoo, Mintaek1,4 (AUTHOR) mintaekyoo@gachon.ac.kr |
| Source: | Remote Sensing. Nov2025, Vol. 17 Issue 21, p3537. 18p. |
| Subjects: | Radar interferometry, Statistical accuracy, High speed trains, Spatial arrangement, Reflectance |
| Geographic Terms: | South Korea |
| Abstract: | Highlights: What are the main findings? PS-InSAR accurately captured millimeter-scale settlements along the Honam High-Speed Railway embankments, showing strong agreement with leveling survey results (MAE = 1.7–4.2 mm). Quantitative regression analysis demonstrated that land-cover composition—particularly the balance between vegetation and high-reflectivity surfaces—explains a significant portion of the variability in PS-InSAR accuracy and persistent scatterer density. What is the implication of the main finding? The study transforms the well-known limitation of vegetation-induced decorrelation into a predictive framework by statistically modeling its influence on PS-InSAR performance. The proposed regression-based approach provides practical guidance for selecting monitoring zones and determining when complementary ground-based surveys are required, thereby improving the reliability of satellite-based settlement monitoring strategies for railway infrastructure management. Accurate monitoring of settlement in high-speed railway embankments is critical for operational safety and long-term serviceability. This study investigates the applicability of Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) for quantifying millimeter-scale deformations and emphasizes how surrounding environmental factors influence measurement accuracy. Using 29 TerraSAR-X images acquired between 2016 and 2018, PS-InSAR-derived settlements were compared with precise leveling survey data across twelve representative embankment sections of the Honam High-Speed Railway in South Korea. Temporal and spatial discrepancies between the two datasets were harmonized through preprocessing, allowing robust accuracy assessment using mean absolute error (MAE) and standard deviation (SD). Results demonstrate that PS-InSAR reliably captures settlement trends, with MAE ranging from 1.7 to 4.2 mm across different scenes. However, significant variability in accuracy was observed depending on local land-cover composition. Correlation analysis revealed that vegetation-dominated areas, such as agricultural and forest land, reduce persistent scatterer density and increase measurement variability, whereas high-reflectivity surfaces, including transportation facilities and buildings, enhance measurement stability and precision. These findings confirm that environmental conditions are decisive factors in determining the performance of PS-InSAR. The study highlights the necessity of integrating site-specific land-cover information when designing and interpreting satellite-based monitoring strategies for railway infrastructure management. [ABSTRACT FROM AUTHOR] |
| Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 189611879 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Land-Cover Controls on the Accuracy of PS-InSAR-Derived Concrete Track Settlement Measurements. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Kim%2C+Byung-kyu%22">Kim, Byung-kyu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kim%2C+Joonyoung%22">Kim, Joonyoung</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Park%2C+Jeongjun%22">Park, Jeongjun</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lee%2C+Ilwha%22">Lee, Ilwha</searchLink><relatesTo>1,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yoo%2C+Mintaek%22">Yoo, Mintaek</searchLink><relatesTo>1,4</relatesTo> (AUTHOR)<i> mintaekyoo@gachon.ac.kr</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Nov2025, Vol. 17 Issue 21, p3537. 18p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Radar+interferometry%22">Radar interferometry</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+accuracy%22">Statistical accuracy</searchLink><br /><searchLink fieldCode="DE" term="%22High+speed+trains%22">High speed trains</searchLink><br /><searchLink fieldCode="DE" term="%22Spatial+arrangement%22">Spatial arrangement</searchLink><br /><searchLink fieldCode="DE" term="%22Reflectance%22">Reflectance</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22South+Korea%22">South Korea</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Highlights: What are the main findings? PS-InSAR accurately captured millimeter-scale settlements along the Honam High-Speed Railway embankments, showing strong agreement with leveling survey results (MAE = 1.7–4.2 mm). Quantitative regression analysis demonstrated that land-cover composition—particularly the balance between vegetation and high-reflectivity surfaces—explains a significant portion of the variability in PS-InSAR accuracy and persistent scatterer density. What is the implication of the main finding? The study transforms the well-known limitation of vegetation-induced decorrelation into a predictive framework by statistically modeling its influence on PS-InSAR performance. The proposed regression-based approach provides practical guidance for selecting monitoring zones and determining when complementary ground-based surveys are required, thereby improving the reliability of satellite-based settlement monitoring strategies for railway infrastructure management. Accurate monitoring of settlement in high-speed railway embankments is critical for operational safety and long-term serviceability. This study investigates the applicability of Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) for quantifying millimeter-scale deformations and emphasizes how surrounding environmental factors influence measurement accuracy. Using 29 TerraSAR-X images acquired between 2016 and 2018, PS-InSAR-derived settlements were compared with precise leveling survey data across twelve representative embankment sections of the Honam High-Speed Railway in South Korea. Temporal and spatial discrepancies between the two datasets were harmonized through preprocessing, allowing robust accuracy assessment using mean absolute error (MAE) and standard deviation (SD). Results demonstrate that PS-InSAR reliably captures settlement trends, with MAE ranging from 1.7 to 4.2 mm across different scenes. However, significant variability in accuracy was observed depending on local land-cover composition. Correlation analysis revealed that vegetation-dominated areas, such as agricultural and forest land, reduce persistent scatterer density and increase measurement variability, whereas high-reflectivity surfaces, including transportation facilities and buildings, enhance measurement stability and precision. These findings confirm that environmental conditions are decisive factors in determining the performance of PS-InSAR. The study highlights the necessity of integrating site-specific land-cover information when designing and interpreting satellite-based monitoring strategies for railway infrastructure management. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=189611879 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/rs17213537 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 18 StartPage: 3537 Subjects: – SubjectFull: Radar interferometry Type: general – SubjectFull: Statistical accuracy Type: general – SubjectFull: High speed trains Type: general – SubjectFull: Spatial arrangement Type: general – SubjectFull: Reflectance Type: general – SubjectFull: South Korea Type: general Titles: – TitleFull: Land-Cover Controls on the Accuracy of PS-InSAR-Derived Concrete Track Settlement Measurements. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Kim, Byung-kyu – PersonEntity: Name: NameFull: Kim, Joonyoung – PersonEntity: Name: NameFull: Park, Jeongjun – PersonEntity: Name: NameFull: Lee, Ilwha – PersonEntity: Name: NameFull: Yoo, Mintaek IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 17 – Type: issue Value: 21 Titles: – TitleFull: Remote Sensing Type: main |
| ResultId | 1 |