Practical Assessment of StaMPS Parameter Settings for ComSAR-Based Railway Settlement Monitoring Using Sentinel-1 InSAR

Railway settlement requires long-term monitoring with millimeter-level accuracy; however, conventional leveling provides limited spatial coverage and requires considerable field effort. This study presents a practical assessment of selected StaMPS parameter settings for ComSAR-based railway settlement monitoring using Sentinel-1 InSAR, with conventional StaMPS PS-InSAR used as a comparative workflow. A ComSAR-based compressed interferogram stack and a conventional PS-InSAR workflow were applied to a section of the Honam High-Speed Railway using 61 Sentinel-1A IW SLC scenes acquired between 28 December 2018 and 29 December 2020. Track Concrete Layer (TCL) leveling data acquired at three annual epochs in December 2018, 2019, and 2020 were projected onto the radar line of sight (LOS) using local incidence angles. The InSAR LOS displacement time series were then compared with the leveling-based linear reference trend using trend-referenced RMSE and a 5 m spatial matching criterion. Four StaMPS parameters were evaluated: spatial resampling grid size before unwrapping (unwrap_grid_size), Goldstein filter window size (unwrap_gold_n_win), temporal window for phase unwrapping (unwrap_time_win), and temporal low-pass filtering window (scn_time_win). A univariate parameter-effect assessment was conducted, in which each parameter was varied individually while the remaining parameters were held at their reference settings. Among the four parameters examined, unwrap_grid_size produced the clearest first-order RMSE response in the ComSAR-based workflow under the reference settings used in this study. Grid sizes of 10–20 m produced clear degradation in trend-referenced validation performance, with the RMSE increasing to approximately 6.7–7.4 mm, whereas 40 m was the smallest tested grid size that recovered practically stable RMSE-based validation performance, at approximately 5.58 mm. In contrast, unwrap_gold_n_win, unwrap_time_win, and scn_time_win produced only small or localized numerical RMSE variations, with no consistent deterioration trend across the tested settings. The conventional PS-InSAR workflow also showed relatively stable RMSE-based validation performance across the tested parameter ranges, with RMSE values of approximately 4.87–4.91 mm. These findings provide case-study-based practical guidance for selecting StaMPS parameter settings in ComSAR-based railway settlement monitoring.

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Journal
Applied Sciences
Published
2026-09-14
DOI
https://doi.org/10.3390/app16189107
Primary Topic
Synthetic Aperture Radar (SAR) Applications and Techniques
Type
article
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Practical Assessment of StaMPS Parameter Settings for ComSAR-Based Railway Settlement Monitoring Using Sentinel-1 InSAR

Sukjo Yoon, Jeongho Oh, Youngmin Kim, Hyeonwoo Yu
Applied Sciences
Synthetic Aperture Radar (SAR) Applications and Techniques
article

Practical Assessment of StaMPS Parameter Settings for ComSAR-Based Railway Settlement Monitoring Using Sentinel-1 InSAR

Sukjo Yoon, Jeongho Oh, Youngmin Kim, Hyeonwoo Yu
article en

Abstract

Railway settlement requires long-term monitoring with millimeter-level accuracy; however, conventional leveling provides limited spatial coverage and requires considerable field effort. This study presents a practical assessment of selected StaMPS parameter settings for ComSAR-based railway settlement monitoring using Sentinel-1 InSAR, with conventional StaMPS PS-InSAR used as a comparative workflow. A ComSAR-based compressed interferogram stack and a conventional PS-InSAR workflow were applied to a section of the Honam High-Speed Railway using 61 Sentinel-1A IW SLC scenes acquired between 28 December 2018 and 29 December 2020. Track Concrete Layer (TCL) leveling data acquired at three annual epochs in December 2018, 2019, and 2020 were projected onto the radar line of sight (LOS) using local incidence angles. The InSAR LOS displacement time series were then compared with the leveling-based linear reference trend using trend-referenced RMSE and a 5 m spatial matching criterion. Four StaMPS parameters were evaluated: spatial resampling grid size before unwrapping (unwrap_grid_size), Goldstein filter window size (unwrap_gold_n_win), temporal window for phase unwrapping (unwrap_time_win), and temporal low-pass filtering window (scn_time_win). A univariate parameter-effect assessment was conducted, in which each parameter was varied individually while the remaining parameters were held at their reference settings. Among the four parameters examined, unwrap_grid_size produced the clearest first-order RMSE response in the ComSAR-based workflow under the reference settings used in this study. Grid sizes of 10–20 m produced clear degradation in trend-referenced validation performance, with the RMSE increasing to approximately 6.7–7.4 mm, whereas 40 m was the smallest tested grid size that recovered practically stable RMSE-based validation performance, at approximately 5.58 mm. In contrast, unwrap_gold_n_win, unwrap_time_win, and scn_time_win produced only small or localized numerical RMSE variations, with no consistent deterioration trend across the tested settings. The conventional PS-InSAR workflow also showed relatively stable RMSE-based validation performance across the tested parameter ranges, with RMSE values of approximately 4.87–4.91 mm. These findings provide case-study-based practical guidance for selecting StaMPS parameter settings in ComSAR-based railway settlement monitoring.

Applied SciencesVol. 16(18)
Korea Railroad Research Institute (KR), Anyang University (KR)
Sustainable cities and communities
Openalex Percentile: Top 7%
Synthetic Aperture Radar (SAR) Applications and Techniques
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