Automatic selection of permanent scatterers-based GCPs for refinement and reflattening in InSAR DEM generation

Feng, Yongjiu; Zhou, Yilun; Chen, Yanling; Li, Pengshuo; Xi, Mengrong; Tong, Xiaohua

doi:10.1080/17538947.2022.2070678

Cited by 7 publications

(3 citation statements)

References 45 publications

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“…In the experiment, the SAR image captured on November 29, 2021, was selected as the primary reference image. Ground Control Points (GCP) generated by PS-InSAR were utilized for orbit refinement and re-flattening, thereby improving the accuracy of SBAS-InSAR processing 22 . Figure 5 illustrates the deformation rate results obtained by processing the research area using SBAS-InSAR and overlaying them on Google Earth Pro (V7.3.6.9345; software available at https://www.google.com/earth/about/versions/#download-pro ).…”

Section: Study Areas and Deformation Characteristicsmentioning

confidence: 99%

Land subsidence analysis along high-speed railway based on EEMD-Prophet method

Dongwei,

Yuci,

Yuzheng

et al. 2024

Sci Rep

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Environmental changes and ground subsidence along railway lines are serious concerns during high-speed railway operations. It is worth noting that AutoRegressive Integrated Moving Average (ARMA), Long Short-Term Memory (LSTM), and other prediction methods may present limitations when applied to predict InSAR time series results. To address this issue, this study proposes a prediction method that decomposes the nonlinear settlement time series of feature points obtained through InSAR technology using Ensemble Empirical Mode Decomposition (EEMD). Subsequently, multiple Intrinsic Mode Functions (IMFs) are generated, and each IMF is individually predicted using the Prophet forecasting model. Finally, we employ an equal-weight superimposition method to combine the results, resulting in the prediction of the InSAR settlement time series. The predicted values of each component are subsequently weighted equally and combined to derive the final prediction outcome. This paper selects InSAR monitoring data along a high-speed railway in inland China and uses the proposed method and ARMA and Prophet models to carry out comparative experiments. The experimental results show that compared with the ARMA and Prophet models, the method in this paper improves the root mean square error by 58.01% and 32.3%, and increases the mean absolute error by 62.69% and 33.78%, respectively. The predicted settlement values generated by our method exhibit better agreement with the actual InSAR monitoring values.

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Section: Study Areas and Deformation Characteristicsmentioning

confidence: 99%

Land subsidence analysis along high-speed railway based on EEMD-Prophet method

Dongwei,

Yuci,

Yuzheng

et al. 2024

Sci Rep

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“…PS-SBAS-InSAR is divided into two modules: a PS module and an SBAS module. The PS module considers temporal stability and spatial continuity to select a suitable number of PSs and then identifies the GCPs by automatically evaluating the coherence, average amplitude, and amplitude deviation index (ADI) of the PSs [17]. The evaluation of the coherence coefficient, average amplitude, and ADI are defined as…”

Section: The Ps-sbas-insar Technologymentioning

confidence: 99%

“…In general, GCPs can be selected [16] from relevant targets that maintain backscattering characteristics for a long period (i.e., permanent scatterers, PSs). In 2022, Feng et al [17] developed a new method (auto-PS-GCP-Thin) to automatically extract robust GCPs from candidate PSs, enhancing the efficiency and accuracy of GCP selection. We thus utilized this method to automatically select GCPs and applied the GCPs to SBAS to develop a new deformation detection method (PS-SBAS-InSAR), aiming to solve the insufficient and unreliable samples in coastal lowlands and improve subsidence monitoring accuracy.…”

Section: Introductionmentioning

confidence: 99%

Large-Scale Surface Deformation Monitoring Using SBAS-InSAR and Intelligent Prediction in Typical Cities of Yangtze River Delta

Wang,

Feng,

Tong

et al. 2023

Remote Sensing

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Large-scale short-term monitoring and prediction of surface deformation are of great significance for the prevention and control of geohazards in rapidly urbanizing developing cities. Most studies focus on individual cities, but it would be more meaningful to address large urban agglomerations and consider the relevance of the regions within them. In addition, the commonly used linear fitting prediction methods cannot accurately capture the dynamic mechanisms of deformation. In this study, we proposed an automatic PS extraction method (named PS-SBAS-InSAR) that improves SBAS-InSAR to extract surface deformation and an Informer-based short-term surface deformation prediction method for case studies in 16 typical cities of the Yangtze River Delta (YRD). The results show that PS-SBAS-InSAR successfully extracted accurate surface deformation sequences of the YRD. During the period from January 2019 to January 2021, the YRD experienced a slight deformation with an average deformation rate within [−4, 4] mm/year. Geographically neighboring cities may have associated deformation distributions and similar deformation trends, as indicated by average deformation rate maps and landscape metrics. Both types of deformation (i.e., subsidence/uplift) tend to occur simultaneously, with specific areas of subsidence/uplift occurring in close proximity to areas of concentrated deformation. The Informer model effectively captured the time-series variation in surface deformation, suggesting a slowdown of deformation over the next two months (February 2021–March 2021). Our work contributes to a better understanding of changes and trends in large-scale surface deformation and provides useful methods for monitoring and predicting surface deformation in coastal areas.

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