Synthetic Aperture Radar(SAR)interferometry is one of the most powerful remote sensing tools for ground deformation detection.However,tropospheric delay greatly limits the measurement accuracy of the InSAR technique.W...Synthetic Aperture Radar(SAR)interferometry is one of the most powerful remote sensing tools for ground deformation detection.However,tropospheric delay greatly limits the measurement accuracy of the InSAR technique.While vertically stratified tropospheric delays have been extensively investigated and well tackled,turbulent tropospheric phase noise still remains an intractable issue.In recent years,great efforts have been made to reduce the influence of turbulent atmospheric delay.This contribution is intended to provide a systematic review of the progress achieved in this field.First,it introduces the physical characteristics of atmospheric signals in interferograms.Then,a review of the main mitigation algorithms proposed in the literature is provided.In addition,the strengths and weaknesses of each approach are analyzed to provide guidance for choosing a suitable method accordingly.Finally,sug-gestions for resolving the challenging issues and an outlook for future research are given.展开更多
In the past 30 years,the small baseline subset(SBAS)InSAR time-series technique has emerged as an essential tool for measuring slow surface displacement and estimating geophysical parameters.Because of its ability to ...In the past 30 years,the small baseline subset(SBAS)InSAR time-series technique has emerged as an essential tool for measuring slow surface displacement and estimating geophysical parameters.Because of its ability to monitor large-scale deformation with millimeter accuracy,the SBAS method has been widely used in various geodetic fields,such as ground subsidence,landslides,and seismic activity.The obtained long-term time-series cumulative deformation is vital for studying the deformation mecha-nism.This article reviews the algorithms,applications,and challenges of the SBAS method.First,we recall the fundamental principle and analyze the shortcomings of the traditional SBAS algorithm,which provides a basic framework for the following improved time series methods.Second,we classify the current improved SBAS techniques from different perspectives:solving the ill-posed equation,increasing the density of high-coherence points,improving the accuracy of monitoring deformation and measuring the multi-dimensional deformation.Third,we summarize the application of the SBAS method in monitoring ground subsidence,permafrost degradation,glacier movement,volcanic activity,landslides,and seismic activity.Finally,we discuss the difficulties faced by the SBAS method and explore its future development direction.展开更多
In this paper,we carried out a combination of permanent scatterer and quasi permanent scatterer time-series InSAR image analyses to extract geometric information over the area of the Three Gorges Dam.For the first tim...In this paper,we carried out a combination of permanent scatterer and quasi permanent scatterer time-series InSAR image analyses to extract geometric information over the area of the Three Gorges Dam.For the first time,we measured and analyzed the deformation of the Three Gorges Dam and its surrounding area using 40 SAR images acquired from 2003 to 2008.Our results indicate that the temporal deformation of the left part of the dam has ceased and that the deformation of the dam was influenced by the changing level of the Yangtze River.Seasonal deformation due to varying temperature is also observed.The obtained results agree well with the published results of the Three Gorges Dam deformation obtained by employing conventional survey methods.We also found that there is an area of abnormal subsidence near Zigui County.This paper demonstrates the potential of time-series InSAR image analysis in the monitoring of dam stability and measurement of subsidence.展开更多
The rapid integration of Internet of Things(IoT)technologies is reshaping the global energy landscape by deploying smart meters that enable high-resolution consumption monitoring,two-way communication,and advanced met...The rapid integration of Internet of Things(IoT)technologies is reshaping the global energy landscape by deploying smart meters that enable high-resolution consumption monitoring,two-way communication,and advanced metering infrastructure services.However,this digital transformation also exposes power system to evolving threats,ranging from cyber intrusions and electricity theft to device malfunctions,and the unpredictable nature of these anomalies,coupled with the scarcity of labeled fault data,makes realtime detection exceptionally challenging.To address these difficulties,a real-time decision support framework is presented for smart meter anomality detection that leverages rolling time windows and two self-supervised contrastive learning modules.The first module synthesizes diverse negative samples to overcome the lack of labeled anomalies,while the second captures intrinsic temporal patterns for enhanced contextual discrimination.The end-to-end framework continuously updates its model with rolling updated meter data to deliver timely identification of emerging abnormal behaviors in evolving grids.Extensive evaluations on eight publicly available smart meter datasets over seven diverse abnormal patterns testing demonstrate the effectiveness of the proposed full framework,achieving average recall and F1 score of more than 0.85.展开更多
基金This work was partly supported by the National Science Fund for Distinguished Young Scholars,grant number 41925016the National Natural Science Foundation of China,grant number 41804008.
文摘Synthetic Aperture Radar(SAR)interferometry is one of the most powerful remote sensing tools for ground deformation detection.However,tropospheric delay greatly limits the measurement accuracy of the InSAR technique.While vertically stratified tropospheric delays have been extensively investigated and well tackled,turbulent tropospheric phase noise still remains an intractable issue.In recent years,great efforts have been made to reduce the influence of turbulent atmospheric delay.This contribution is intended to provide a systematic review of the progress achieved in this field.First,it introduces the physical characteristics of atmospheric signals in interferograms.Then,a review of the main mitigation algorithms proposed in the literature is provided.In addition,the strengths and weaknesses of each approach are analyzed to provide guidance for choosing a suitable method accordingly.Finally,sug-gestions for resolving the challenging issues and an outlook for future research are given.
基金This work was funded by the National Key R&D Program of China(2019YFC1509205)the National Natural Science Foundation of China(Nos.42174023 and 41804015)+1 种基金the Postgraduate Scientific Research Innovation Project of Hunan Province(150110074)the Postgraduate Scientific Research Innovation Project of Central South University(212191010).
文摘In the past 30 years,the small baseline subset(SBAS)InSAR time-series technique has emerged as an essential tool for measuring slow surface displacement and estimating geophysical parameters.Because of its ability to monitor large-scale deformation with millimeter accuracy,the SBAS method has been widely used in various geodetic fields,such as ground subsidence,landslides,and seismic activity.The obtained long-term time-series cumulative deformation is vital for studying the deformation mecha-nism.This article reviews the algorithms,applications,and challenges of the SBAS method.First,we recall the fundamental principle and analyze the shortcomings of the traditional SBAS algorithm,which provides a basic framework for the following improved time series methods.Second,we classify the current improved SBAS techniques from different perspectives:solving the ill-posed equation,increasing the density of high-coherence points,improving the accuracy of monitoring deformation and measuring the multi-dimensional deformation.Third,we summarize the application of the SBAS method in monitoring ground subsidence,permafrost degradation,glacier movement,volcanic activity,landslides,and seismic activity.Finally,we discuss the difficulties faced by the SBAS method and explore its future development direction.
基金supported by National Basic Research Program of China (Grant Nos. 2007CB714405, 2006CB701300)National Natural Science Foundation of China (Grant No. 40721001)Three Gorges Region Geologic Disaster Protection Major Research Program (Grant No. SXKY3-6-4)
文摘In this paper,we carried out a combination of permanent scatterer and quasi permanent scatterer time-series InSAR image analyses to extract geometric information over the area of the Three Gorges Dam.For the first time,we measured and analyzed the deformation of the Three Gorges Dam and its surrounding area using 40 SAR images acquired from 2003 to 2008.Our results indicate that the temporal deformation of the left part of the dam has ceased and that the deformation of the dam was influenced by the changing level of the Yangtze River.Seasonal deformation due to varying temperature is also observed.The obtained results agree well with the published results of the Three Gorges Dam deformation obtained by employing conventional survey methods.We also found that there is an area of abnormal subsidence near Zigui County.This paper demonstrates the potential of time-series InSAR image analysis in the monitoring of dam stability and measurement of subsidence.
文摘董志塬地区位于黄土高原中心地带,滑坡灾害频发,亟需明确滑坡易发性分区,以支持该区域滑坡隐患的科学防控。因此,本文以董志塬为研究区,选取高程、坡向和NDVI等12个影响因素作为评价因子,基于频率比(frequency ratio,FR)模型,结合随机森林(random forest,RF)与人工神经网络(artificial neural network,ANN)模型开展滑坡静态易发性评价,并分析各因子对评价精度的贡献。结果表明,FRRF和FR-ANN模型的曲线下面积(area under the curve,AUC)值分别为0.922和0.918,表明FR-RF模型在董志塬滑坡易发性评价中的精度更高。坡度、坡向和道路密度对滑坡易发性的贡献率分别为16.7%、15.3%和1.4%。为克服地形复杂和数据更新滞后的问题,本文将FR-RF模型的易发性结果与InSAR Stacking结果相结合,将静态滑坡易发性评价精度由6.9%提升到8.1%。动态易发性结果表明,董志塬滑坡高易发区主要分布于河流沿岸,占总面积的6.5%,该区域的滑坡数量占总滑坡数的23.6%,滑坡密度15.7个/km^(2)。低易发区主要位于远离河流的中部区域,占总面积的81.7%,滑坡数量占总滑坡数的57.8%,滑坡密度4.7个/km^(2)。本研究通过融合InSAR Stacking方法,解决了静态滑坡易发性评价数据更新滞后问题,减少了假阴性错误,为传统滑坡易发性评价赋予了时效性,可以实现董志塬滑坡易发性动态评价,为灾害防治提供了重要数据支持。
文摘The rapid integration of Internet of Things(IoT)technologies is reshaping the global energy landscape by deploying smart meters that enable high-resolution consumption monitoring,two-way communication,and advanced metering infrastructure services.However,this digital transformation also exposes power system to evolving threats,ranging from cyber intrusions and electricity theft to device malfunctions,and the unpredictable nature of these anomalies,coupled with the scarcity of labeled fault data,makes realtime detection exceptionally challenging.To address these difficulties,a real-time decision support framework is presented for smart meter anomality detection that leverages rolling time windows and two self-supervised contrastive learning modules.The first module synthesizes diverse negative samples to overcome the lack of labeled anomalies,while the second captures intrinsic temporal patterns for enhanced contextual discrimination.The end-to-end framework continuously updates its model with rolling updated meter data to deliver timely identification of emerging abnormal behaviors in evolving grids.Extensive evaluations on eight publicly available smart meter datasets over seven diverse abnormal patterns testing demonstrate the effectiveness of the proposed full framework,achieving average recall and F1 score of more than 0.85.
