Accelerating Persistent Scatterer Pixel Selection for InSAR Processing

Reza, Tahsin; Zimmer, Aaron; Blasco, José Manuel Delgado; Ghuman, Parwant; Aasawat, Tanuj Kr; Ripeanu, Matei

doi:10.1109/tpds.2017.2706291

Cited by 10 publications

(7 citation statements)

References 19 publications

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“…Interferometric phase modeling has been investigated in the literature [4][5][6][7]. Interferometric phase measurements are affected by various factors-imaging geometry, topography, atmospheric delay and ground deformation.…”

Section: Mathematical Modelling For Insar Phasementioning

confidence: 99%

“…Since then, time-series InSAR (TSInSAR) techniques have emerged as a powerful strategy to monitor slow and subtle terrain displacements [3]. Several studies [4][5][6][7] have investigated the signal model of interferometric phase and have shown that observed interferometric phases are affected by different factors-imaging geometry, topography, atmospheric delay and ground deformation. Among these factors, the deformation and topography components are the valuable contributors because they contain information to monitor the ground movement and describe surface height.…”

Section: Introductionmentioning

confidence: 99%

“…In general, TSInSAR techniques use SAR images acquired for the same ground area on different dates to construct a stack of N interferograms. The signal phase of single-referenced (aka master) images' resolution cell on the ground is a function of multiple-phase contributors, and the signals of interest can be estimated from the same resolution cell, taken at separate times [4]. However, each cell is represented as a wrapped phase, and the estimation with phase cycle ambiguities makes this task very challenging.…”

Section: Introductionmentioning

confidence: 99%

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IGS-CMAES: A Two-Stage Optimization for Ground Deformation and DEM Error Estimation in Time Series InSAR Data

Sun¹,

Zimmer²,

Mukherjee³

et al. 2021

Remote Sensing

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Interferometric synthetic aperture radar (InSAR) has become an increasingly recognized remote sensing technology for earth surface monitoring. Slow and subtle terrain displacements can be estimated using time-series InSAR (TSInSAR) data. However, a substantial increase in the availability of exclusive time series data necessitates the development of more efficient and effective algorithms. Research in these areas is usually carried out by solving complicated optimization problems, which is very computationally expensive and time-consuming. This work proposes a two-stage black-box optimization framework to jointly estimate the average ground deformation rate and terrain digital elevation model (DEM) error. The method performs an iterative grid search (IGS) to acquire coarse candidate solutions, and then a covariance matrix adaptive evolution strategy (CMAES) is adopted to obtain the final local results. The performance of our method is evaluated using both simulated and real datasets. Both quantitative and qualitative comparisons using different optimizers support the reliability and effectiveness of our work. The proposed IGS-CMAES achieves higher accuracy with a significantly fewer number of objective function evaluations than other established algorithms. It offers the possibility for wide-area monitoring, where high precision and real-time processing is essential.

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Section: Mathematical Modelling For Insar Phasementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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IGS-CMAES: A Two-Stage Optimization for Ground Deformation and DEM Error Estimation in Time Series InSAR Data

Sun¹,

Zimmer²,

Mukherjee³

et al. 2021

Remote Sensing

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“…Therefore we introduce a teacher-student framework to make it feasible to train DeepInSAR for real-world images. From the literature, stack-based methods, like PtSel [51], always give reliable results. PtSel is an industry level algorithm for coherence estimation and interferometric phase filtering, which searches similar pixels across a stack of interferograms in both spatial and temporal domains.…”

Section: Teacher-student Frameworkmentioning

confidence: 99%

“…For InSAR phase restoration and coherence estimation, we adopt the PtSel method to create filtered phase images for reference, coherence maps with human tuning and full stack processing to make sure the results are sufficiently reliable. The detail of the PtSel algorithm and its GPU implementation can be found at References [51,52]. In our approach, PtSel with expert supervision becomes the teacher of the proposed DeepInSAR model, which is a student.…”

Section: Teacher-student Frameworkmentioning

confidence: 99%

DeepInSAR—A Deep Learning Framework for SAR Interferometric Phase Restoration and Coherence Estimation

et al. 2020

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Over the past decade, using Interferometric Synthetic Aperture Radar (InSAR) remote sensing technology for ground displacement detection has become very successful. However, during the acquisition stage, microwave signals reflected from the ground and received by the satellite are contaminated, for example, due to undesirable material reflectance and atmospheric factors, and there is no clean ground truth to discriminate these noises, which adversely affect InSAR phase computation. Accurate InSAR phase filtering and coherence estimation are crucial for subsequent processing steps. Current methods require expert supervision and expensive runtime to evaluate the quality of intermediate outputs, limiting the usability and scalability in practical applications, such as wide area ground displacement monitoring and predication. We propose a deep convolutional neural network based model DeepInSAR to intelligently solve both phase filtering and coherence estimation problems. We demonstrate our model’s performance using simulated and real data. A teacher-student framework is introduced to handle the issue of missing clean InSAR ground truth. Quantitative and qualitative evaluations show that our teacher-student approach requires less input but can achieve better results than its stack-based teacher method even on new unseen data. The proposed DeepInSAR also outperforms three other top non-stack based methods in time efficiency without human supervision.

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GPU accelerated interferometric SAR processing for Sentinel-1 TOPS data

Balz

Luo

et al. 2019

Computers & Geosciences

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Accelerating Persistent Scatterer Pixel Selection for InSAR Processing

Cited by 10 publications

References 19 publications

IGS-CMAES: A Two-Stage Optimization for Ground Deformation and DEM Error Estimation in Time Series InSAR Data

IGS-CMAES: A Two-Stage Optimization for Ground Deformation and DEM Error Estimation in Time Series InSAR Data

DeepInSAR—A Deep Learning Framework for SAR Interferometric Phase Restoration and Coherence Estimation

GPU accelerated interferometric SAR processing for Sentinel-1 TOPS data

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