Atmospheric Effect Correction for InSAR With Wavelet Decomposition-Based Correlation Analysis Between Multipolarization Interferograms

Fu, Hai Qiang; Zhu, Jianjun; Wang, Chang Cheng; Zhao, Rong; Xie, Qinghua

doi:10.1109/tgrs.2018.2821716

Cited by 13 publications

(6 citation statements)

References 35 publications

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“…Performance gauged on the ability to retrieve a priori error (Hu et al 2018;Tymofyeyeva and Fialko 2015). Similarly, statistical diagnostic outputs can be used to assess accuracy, which can be determined by comparing the results with the values from other measurements (Li et al 2003a;Gong et al 2013) or estimated from mean calculated from a window or the whole of the interferogram (Fu et al 2018). Reduced standard deviation or increased correlation after the correction indicates an improved performance.…”

Section: Tropospheric Corrections In Insar Applicationsmentioning

confidence: 99%

Mitigation of Atmospheric Artefacts in Multi Temporal InSAR: A Review

Kirui

Reinosch

Isya

et al. 2021

PFG

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The complexity of the atmosphere renders the modelling of the atmospheric delay in multi temporal InSAR difficult. This limits the potential of achieving millimetre accuracy of InSAR-derived deformation measurements. In this paper we review advances in tropospheric delay modelling in InSAR, tropospheric correction methods and integration of the correction methods within existing multi temporal algorithms. Furthermore, we investigate ingestion of the correction techniques by different InSAR applications, accuracy performance metrics and uncertainties of InSAR derived measurements attributed to tropospheric delay. Spatiotemporal modelling of atmospheric delay has evolved and can now be regarded as a spatially correlated turbulent delay with varying degree of anisotropy random in time and topographically correlated seasonal stratified delay. Tropospheric corrections methods performance is restricted to a case by case basis and ingestion of these methods by different applications remains limited due to lack of their integration into existing algorithms. Accuracy and uncertainty assessments remain challenging with most studies adopting simple statistical metrics. While advances have been made in tropospheric modelling, challenges remain for the calibration of atmospheric delay due to lack of data or limited resolution and fusion of multiple techniques for optimal performance.

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Section: Tropospheric Corrections In Insar Applicationsmentioning

confidence: 99%

Mitigation of Atmospheric Artefacts in Multi Temporal InSAR: A Review

Kirui

Reinosch

Isya

et al. 2021

PFG

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“…Within the short synthetic aperture time of SAR data, different polarimetric radio waves travel through the same atmospheric components, so that different polarimetric interferograms contain the same atmospheric phases. In summary, the atmospheric effect is polarization-independent, which provides us with a new idea to estimate and remove the atmospheric effects [8,9].…”

Section: Phase Components In the Dual-polarization Interferogramsmentioning

confidence: 99%

“…If the ionospheric effects caused by the Faraday rotation are not considered, both the ionospheric phases and atmospheric phases are polarization-independent. As a result, it is difficult to distinguish and correct the ionospheric artifacts and atmospheric effects based on the spatial characteristics of the interferometric phases [8,9], and even the orbit error phases cannot be corrected effectively. Therefore, we proposed a step-by-step correction approach to remove the ionospheric artifacts and atmospheric effects in interferograms.…”

Section: Phase Components In the Dual-polarization Interferogramsmentioning

confidence: 99%

“…The latter two kinds of methods depend on the availability of auxiliary data or the number of SAR images, so they have very limited applications. A new method for atmospheric correction, which adopts a wavelet decomposition-based correlation analysis between interferograms in different polarizations to mitigate the atmosphere effects, was proposed [8]. Later, the moving window correlation analysis in this method was replaced by integral linear regression, so that the correlation analysis could identify the atmospheric effect more accurately [9] since the atmospheric phase could be correlated with the nonatmospheric phase in some areas.…”

Section: Introductionmentioning

confidence: 99%

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A Framework for Correcting Ionospheric Artifacts and Atmospheric Effects to Generate High Accuracy InSAR DEM

Liu

Zhou

et al. 2020

Remote Sensing

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Repeat-pass interferometric synthetic aperture radar is a well-established technology for generating digital elevation models (DEMs). However, the interferogram usually has ionospheric and atmospheric effects, which reduces the DEM accuracy. In this paper, by introducing dual-polarization interferograms, a new approach is proposed to mitigate the ionospheric and atmospheric errors of the interferometric synthetic aperture radar (InSAR) data. The proposed method consists of two parts. First, the range split-spectrum method is applied to compensate for the ionospheric artifacts. Then, a multiresolution correlation analysis between dual-polarization InSAR interferograms is employed to remove the identical atmospheric phases, since the atmospheric delay is independent of SAR polarizations. The corrected interferogram can be used for DEM extraction. Validation experiments, using the ALOS-1 PALSAR interferometric pairs covering the study areas in Hawaii and Lebanon of the U.S.A., show that the proposed method can effectively reduce the ionospheric artifacts and atmospheric effects, and improve the accuracy of the InSAR-derived DEMs by 64.9% and 31.7% for the study sites in Hawaii and Lebanon of the U.S.A., respectively, compared with traditional correction methods. In addition, the assessment of the resulting DEMs also includes comparisons with the high-precision Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) altimetry data. The results show that the selection of reference data will not affect the validation results.

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“…Researchers have extensively utilized timeseries InSAR techniques to identify potential landslide hazards [5,6,[14][15][16][17][18][19][20]. Time-series InSAR techniques are still limited by atmospheric delays [21][22][23][24][25][26][27][28][29], although many methods have been proposed to correct different atmospheric delays to improve the accuracy of displacement monitoring (e.g., [27,28,[30][31][32]). Among these atmospheric delays, the influence of topography-dependent atmospheric delay (TDAD) is particularly severe in many cases.…”

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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Atmospheric Effect Correction for InSAR With Wavelet Decomposition-Based Correlation Analysis Between Multipolarization Interferograms

Cited by 13 publications

References 35 publications

Mitigation of Atmospheric Artefacts in Multi Temporal InSAR: A Review

Mitigation of Atmospheric Artefacts in Multi Temporal InSAR: A Review

A Framework for Correcting Ionospheric Artifacts and Atmospheric Effects to Generate High Accuracy InSAR DEM

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

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