A reasoned bibliography on SAR interferometry applications and outlook on big interferometric data processing

Kamali, Muhagir El; Abuelgasim, Abdelgadir; Papoutsis, Ioannis; Loupasakis, Constantinos; Kontoes, Charalampos

doi:10.1016/j.rsase.2020.100358

Cited by 10 publications

(9 citation statements)

References 88 publications

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“…Based on the simulated underground goaf data in the previous section and the model parameters determined by geological conditions, the subsidence W of each point on the surface was generated by the PIM, providing the horizontal movement U N in the northsouth direction and the horizontal movement U E in the east-west direction. Simulated InSAR surface deformation observation values were generated using Equation (12) and recorded as D LOS , and taking the underground goaf data and PIM parameters as unknown values, an inversion experiment was carried out. The inversion results were compared with the simulated values, and the inversion values of each parameter were obtained.…”

Section: Simulations Numerical Simulationsmentioning

confidence: 99%

“…In this section, we make a comparison of this approach with the method of this paper. To ensure that the experimental data are not affected by other errors, the LOS deformation observed by InSAR was generated based on the simulated data in Table 1 and Equation (12). Since the LOS deformation has no errors, when the PIM parameters are assumed to be known, the inverted goaf position parameters should be consistent with the simulated values.…”

Section: Inversion Of Goaf Parametersmentioning

confidence: 99%

“…Leveling, GPS, interferometric synthetic aperture radar (InSAR), and other technologies are widely used in mining subsidence monitoring [10,11]. Due to its large coverage, all-weather capability, and high monitoring accuracy [12], InSAR is widely used to measure deformations in glaciers [13,14], volcanoes [15,16], earthquakes [17,18], and other landscapes, as well as urban and general land surface deformations caused by resource exploitation. Scholars worldwide have also tried to apply InSAR technology to the observation of surface deformations caused by mining activities [19,20].…”

Section: Introductionmentioning

confidence: 99%

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Position Inversion of Goafs in Deep Coal Seams Based on DS-InSAR Data and the Probability Integral Methods

Zhang

Fan

et al. 2021

Remote Sensing

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The goafs caused by coal mining cause great harm to the surface farmland, buildings, and personal safety. The existing monitoring methods cost a lot of workforce and material resources. Therefore, this paper proposes an inversion approach for establishing the locations of underground goafs and the parameters of the probability integral method (PIM), thus integrating distributed scatter interferometric synthetic aperture radar (DS-InSAR) data and the PIM. Firstly, a large amount of surface deformation observation data above the goaf are obtained by DS-InSAR, and the line-of-sight deformation is regarded as the true value. Secondly, according to the obtained surface deformations, the ranges of eight goaf location parameters and three PIM parameters are set. Thirdly, a correlation function between the surface deformation and the underground goaf location is constructed. Finally, a particle swarm optimization algorithm is used to search for the optimal parameters in the range of the set parameters to meet the requirement for minimum error between the surface deformation calculated by PIM and the line-of-sight deformation obtained by DS-InSAR. These optimal parameters are thus regarded as the real values of the position of the underground goaf and the PIM parameters. The simulation results show that the maximum relative error between the position of the goaf and the PIM parameters is 2.11%. Taking the 93,604 working face of the Zhangshuanglou coal mine in the Peibei mining area as the research object and 12 Sentinel-1A images as the data source, the goaf location and PIM parameters of the working face were successfully inverted. The inversion results show that the maximum relative error in the goaf location parameters was 16.61%, and the maximum relative error in the PIM parameters was 26.67%.

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Section: Simulations Numerical Simulationsmentioning

confidence: 99%

Section: Inversion Of Goaf Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Position Inversion of Goafs in Deep Coal Seams Based on DS-InSAR Data and the Probability Integral Methods

Zhang

Fan

et al. 2021

Remote Sensing

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“…Data collected by space-borne synthetic aperture radar (SAR) sensors and processed through multi-temporal interferometric SAR (MT-InSAR) techniques have become one of the most powerful tools to guarantee a systematic and comprehensive monitoring of surface displacements over large areas for long periods [2,[7][8][9][10][11]. These techniques have been developed during the last two decades referring to two main approaches, persistent scatterer (PS, [12][13][14]) and small baseline subset (SBAS, [15,16]), and can be used to measure surface displacements due to natural processes and human activities [17,18], removing the effects of atmosphere, topography, and signal noise. They have been used to monitor mass movements [11,19] and anthropogenic structures and infrastructures (e.g., buildings, dams, roads, bridges, and pipelines) affected by such phenomena [10,20,21].…”

Section: Introductionmentioning

confidence: 99%

Combining Sentinel-1 Interferometry and Ground-Based Geomatics Techniques for Monitoring Buildings Affected by Mass Movements

et al. 2021

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Mass movements represent a serious threat to the stability of human structures and infrastructures, and cause loss of lives and severe damages to human properties every year worldwide. Built structures located on potentially unstable slopes are susceptible to deformations due to the displacement of the ground that at worst can lead to total destruction. Synthetic aperture radar (SAR) data acquired by Sentinel-1 satellites and processed by multi-temporal interferometric SAR (MT-InSAR) techniques can measure centimeter to millimeter-level displacement with weekly to monthly updates, characterizing long-term large-scale behavior of the buildings and slopes. However, the spatial resolution and short wavelength weaken the performance of Sentinel-1 in recognizing features (i.e., single buildings) inside image pixels and maintaining the coherence in mountainous vegetated areas. We have proposed and applied a methodology that combines Sentinel-1 interferometry with ground-based geomatics techniques, i.e., global navigation satellite system (GNSS), terrestrial laser scanning (TLS) and terrestrial structure from motion photogrammetry (SfM), for fully assessing building deformations on a slope located in the north-eastern Italian pre-Alps. GNSS allows verifying the ground deformation estimated by MT-InSAR and provides a reference system for the TLS and SfM measurements, while TLS and SfM allow the behavior of buildings located in the investigated slope to be monitored in great detail. The obtained results show that damaged buildings are located in the most unstable sectors of the slope, but there is no direct relationship between the rate of ground deformation of these sectors and the temporal evolution of damages to a single building, indicating that mass movements cause the displacement of blocks of buildings and each of them reacts differently according to its structural properties. This work shows the capability of MT-InSAR, GNSS, TLS and SfM in monitoring both buildings and geological processes that affect their stability, which plays a key role in geohazard analysis and assessment.

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“…Notwithstanding the plethora of examples on the use of satellite data for monitoring micro deformations, still, the processing chain of the radar deformation requires in-depth knowledge of the radar theory [20,21] and hardware accessibility with the high computational performance [22]. Whilst several free software packages have been developed in the recent past and are currently available, these remain technically complex and can be challenging to use for nonexperts [23].…”

Section: Introductionmentioning

confidence: 99%

Detecting Displacements Within Archaeological Sites in Cyprus After a 5.6 Magnitude Scale Earthquake Event Through the Hybrid Pluggable Processing Pipeline (HyP3) Cloud-Based System and Sentinel-1 Interferometric Synthetic Aperture Radar (InSAR) Analysis

Αγαπίου

Lysandrou

2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The distribution of free and open access radar satellite datasets, like those of Sentinel-1, have provided new opportunities for monitoring archaeological sites and monuments in a systematic way, and especially after earthquake events. While optical sensors are established in the scientific literature and radar sensors are lately introduced in the relevant literature, the role of satellite-driven ready products is still limited discussed. With the continuous improvement of remote sensing satellite data quality and accuracy, high-resolution data needs to be processed, while at the same time, this requires high computational complexity. In respect to that, over the last years, various efforts have been made to support high-performance cloud-based processing, providing to the end-users' ready products in a short time. This study presents the results from the exploitation of a relevant new cloud platform, namely the Hybrid Pluggable Processing Pipeline (HyP3) system, that integrates GAMMA software, for detecting ground displacement within archaeological sites in Cyprus, after a 5.6 magnitude scale earthquake in 2015. Ascending and descending pairs of Sentinel-1 images, acquired before and after the event, were processed through the HyP3 platform, revealing small relative ground displacements in the area under study. The processing chain was performed in less than one hour-per pairon the HyP3 system, evidencing a shift within the satellite image processing practice. Similar approaches could be beneficial in the future to support cultural heritage management of large areas.

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A reasoned bibliography on SAR interferometry applications and outlook on big interferometric data processing

Cited by 10 publications

References 88 publications

Position Inversion of Goafs in Deep Coal Seams Based on DS-InSAR Data and the Probability Integral Methods

Position Inversion of Goafs in Deep Coal Seams Based on DS-InSAR Data and the Probability Integral Methods

Combining Sentinel-1 Interferometry and Ground-Based Geomatics Techniques for Monitoring Buildings Affected by Mass Movements

Detecting Displacements Within Archaeological Sites in Cyprus After a 5.6 Magnitude Scale Earthquake Event Through the Hybrid Pluggable Processing Pipeline (HyP3) Cloud-Based System and Sentinel-1 Interferometric Synthetic Aperture Radar (InSAR) Analysis

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