Recurrent neural networks for atmospheric noise removal from InSAR time series with missing values

Zhao, Zhuoyi; Wu, Zherong; Zheng, Yi; Ma, Peifeng

doi:10.1016/j.isprsjprs.2021.08.009

Cited by 28 publications

(10 citation statements)

References 35 publications

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“…The second type uses external auxiliary data to estimate TDAD, including meteorological models [34,35], GNSS data [36][37][38][39][40][41][42], spectral measurements [43,44], or a fusion of these data sources [1,45]. The third type is based on deep neural network approaches, such as [46,47]; methods of this kind are based on the premise that topography is strongly correlated with TDAD and aim to model it to reduce the influence of TDAD. Although the above methods have achieved certain results in different regions, there are some drawbacks to them.…”

Section: Introductionmentioning

confidence: 99%

Time-Series InSAR with Deep-Learning-Based Topography-Dependent Atmospheric Delay Correction for Potential Landslide Detection

Zhou,

Dai,

Tang

et al. 2023

Remote Sensing

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Synthetic aperture radar interferometry (InSAR) has emerged as an effective technique for monitoring potentially unstable landslides and has found widespread application. Nevertheless, in mountainous reservoir regions, the precision of time-series InSAR outcomes is often constrained by topography-dependent atmospheric delay (TDAD) effects. To address this limitation, we propose a novel InSAR time-series method that integrates TDAD correction. This approach employs advanced deep learning algorithms to individually model and mitigate TDAD for each interferogram, thereby enhancing the accuracy of small baseline subset InSAR (SBAS-InSAR) and stacking InSAR time-series analyses. Utilizing Sentinel-1 data, we apply this method to identify potential landslides in the Baihetan reservoir area, located in southwestern China, where we successfully identified 26 potential landslide sites. Comparative experimental results demonstrate a significant reduction (averaging 70% and reaching up to 90%) in phase standard deviation (StdDev) in the corrected interferograms, indicating a marked decrease in phase–topography correlation. Furthermore, the corrected time-series InSAR results effectively remove TDAD signals, leading to clearer displacement boundaries and a remarkable reduction in other spurious displacement signals. Overall, this method efficiently addresses TDAD in time-series InSAR, enabling precise identification of potentially unstable landslides influenced by TDAD, and providing essential technical support for early landslide hazard detection using time-series InSAR.

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Section: Introductionmentioning

confidence: 99%

Time-Series InSAR with Deep-Learning-Based Topography-Dependent Atmospheric Delay Correction for Potential Landslide Detection

Zhou,

Dai,

Tang

et al. 2023

Remote Sensing

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“…Artificial intelligence has been used not only for displacement time series analysis [17][18][19], but also to evaluate deformation patterns in interferograms [20][21][22]. The exploration of InSAR time series through artificial intelligence has also been used to mitigate uncertainty sources of the data, such as atmospheric effects [23][24][25] or unwrapping errors [26]. Two recent trends in this field are the separation of effects coming from different sources [27,28] and the prediction of future InSAR observations [29,30].…”

Section: Introductionmentioning

confidence: 99%

SARClust—A New Tool to Analyze InSAR Displacement Time Series for Structure Monitoring

Roque

Falcão

Perissin³

et al. 2023

Sustainability

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Interferometric Synthetic Aperture Radar (InSAR) has proved its efficiency for displacement monitoring in urban areas. However, the large volume of data generated by this technology turns the retrieval of information useful for structure monitoring into a big data problem. In this study, a new tool (SARClust) to analyze InSAR displacement time series is proposed. The tool performs the clustering of persistent scatterers (PSs) based on dissimilarities between their displacement time series evaluated through dynamic time warping. This strategy leads to the formation of clusters containing PSs with similar displacements, which can be analyzed together, reducing data dimensionality, and facilitating the identification of displacement patterns potentially related to structural damage. A proof of concept was performed for downtown Lisbon, Portugal, where ten distinct displacement patterns were identified. A relationship between clusters presenting centimeter-level displacements and buildings located on steep slopes was observed. The results were validated through visual inspections and comparison with another tool for time series analysis. Agreement was found in both cases. The innovation in this study is the attention brought to SARClust’s ability to (i) analyze vertical and horizontal displacements simultaneously, using an unsupervised procedure, and (ii) characterize PSs assisting the displacement interpretation. The main finding is the strategy to identify signs of structure damage, even on isolated buildings, in a large amount of InSAR data. In conclusion, SARClust is of the utmost importance to detect potential signs of structural damage in InSAR displacement time series, supporting structure safety experts in more efficient and sustainable monitoring tasks.

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“…Due to the spatial and temporal variations in the atmospheric refractive index, the stratified tropospheric delay usually exhibits seasonal oscillations [14,15]. The spatial-temporal filtering method assumes that the atmosphere is not related in time and only has a weakening effect on the turbulently mixed delay that changes randomly in time and space, and it cannot weaken the error effect of a vertically stratified delay [16].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of InSAR Tropospheric Delay Correction Methods in the Plateau Monsoon Climate Region Considering Spatial–Temporal Variability

Yang,

Zuo,

Guo

et al. 2023

Sensors

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The tropospheric delay caused by the temporal and spatial variation of meteorological parameters is the main error source in interferometric synthetic aperture radar (InSAR) applications for geodesy. To minimize the impact of tropospheric delay errors, it is necessary to select the appropriate tropospheric delay correction method for different regions. In this study, the interferogram results of the InSAR, corrected for tropospheric delay using the Linear, Generic Atmospheric Correction Online Service for InSAR (GACOS) and ERA-5 atmospheric reanalysis dataset (ERA5) methods, are presented for the study area of the junction of the Hengduan Mountains and the Yunnan–Kweichow Plateau, which is significantly influenced by the plateau monsoon climate. Four representative regions, Eryuan, Binchuan, Dali, and Yangbi, are selected for the study and analysis. The phase standard deviation (STD), phase–height correlation, and global navigation satellite system (GNSS) data were used to evaluate the effect of tropospheric delay correction by integrating topographic, seasonal, and meteorological factors. The results show that all three methods can attenuate the tropospheric delay, but the correction effect varies with spatial and temporal characteristics.

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Recurrent neural networks for atmospheric noise removal from InSAR time series with missing values

Cited by 28 publications

References 35 publications

Time-Series InSAR with Deep-Learning-Based Topography-Dependent Atmospheric Delay Correction for Potential Landslide Detection

Time-Series InSAR with Deep-Learning-Based Topography-Dependent Atmospheric Delay Correction for Potential Landslide Detection

SARClust—A New Tool to Analyze InSAR Displacement Time Series for Structure Monitoring

Evaluation of InSAR Tropospheric Delay Correction Methods in the Plateau Monsoon Climate Region Considering Spatial–Temporal Variability

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