Interferometric Data Processing Research Articles

Comparison between DInSAR and Stacking in Deformation Monitoring Using LuTan-1

Abstract. LuTan-1 (LT-1) satellite constellation is used to conduct surface deformation monitoring. Following the successful launch of two satellites, we began to use the independently developed LandSAR software for differential interferometric data processing and application at the beginning of 2022. In this paper, we tested the LandSAR software by assessing the DInSAR and stacking modules. We used 21 LT-1 single look complex (SLC) images from December 2022 to May 2023 to conduct DInSAR and stacking processing of Datong City, Shanxi Province. DInSAR revealed more subsidence funnels and larger relative deformation values compared to stacking. We tested the DInSAR technology with different parameter combination. Results show that multi-looking factor is the most important to detect the deformation details because of the phase gradient limitation of DInSAR. Compared multi-looking ratios of 2×2 and 4×4, the subsiding funnels that were detected decreased from 60 to 47. After deep testing and assessment, we found that the linear model used by stacking affects the result severely, meaning that most of the subsidence of coal mines were not linear. Stacking only found 32 subsidence funnels which were active coal mines during our observation time. The test result show that LandSAR was compatible for DInSAR and stacking. And it is useful to observe linear and nonlinear deformation in the coal mine regions.

The ALMA Interferometric Pipeline Heuristics

We describe the calibration and imaging heuristics developed and deployed in the Atacama Large Millimeter/submillimeter Array (ALMA) interferometric data processing pipeline, as of ALMA Cycle 9 operations. The pipeline software framework is written in Python, with each data reduction stage layered on top of tasks and toolkit functions provided by the Common Astronomy Software Applications package. This framework supports a variety of tasks for observatory operations, including science data quality assurance, observing mode commissioning, and user reprocessing. It supports ALMA and Very Large Array interferometric data along with ALMA and NRO 45 m single dish data, via different stages and heuristics. In addition to producing calibration tables, calibrated measurement sets, and cleaned images, the pipeline creates a WebLog which serves as the primary interface for verifying the quality assurance of the data by the observatory and for examining the contents of the data by the user. Following the adoption of the pipeline by ALMA Operations in 2014, the heuristics have been refined through annual prioritized development cycles, culminating in a new pipeline release aligned with the start of each ALMA Cycle of observations. Initial development focused on basic calibration and flagging heuristics (Cycles 2–3), followed by imaging heuristics (Cycles 4–5). Further refinement of the flagging and imaging heuristics, including the introduction of parallel processing, proceeded for Cycles 6–7. In the 2020 release, the algorithm to identify channels to use for continuum subtraction and imaging was substantially improved by the addition of a moment difference analysis. A spectral renormalization stage was added for the 2021 release (Cycle 8) to correct high spectral resolution visibility data acquired on targets exhibiting strong celestial line emission in their autocorrelation spectra. The calibration heuristics used in the low signal-to-noise regime were improved for the 2022 release (Cycle 9). In the two most recent Cycles, 97% of ALMA data sets were calibrated and imaged with the pipeline, ensuring long-term automated reproducibility of results. We conclude with a brief description of plans for future additions, including a self-calibration stage, support for multi-configuration imaging, and complete calibration and imaging of full polarization data.

Parallel Optimization for Large Scale Interferometric Synthetic Aperture Radar Data Processing

Interferometric synthetic aperture radar (InSAR) has developed rapidly over the past years and is considered as an important method for surface deformation monitoring, benefiting from growing data quantities and improving data quality. However, the handing of SAR big data poses significant challenges for related algorithms and pipeline, particularly in large-scale SAR data processing. In addition, InSAR algorithms are highly complex, and their task dependencies are intricate. There is a lack of efficient optimization models and task scheduling for InSAR pipeline. In this paper, we design parallel time-series InSAR processing models based on multi-thread technology for high efficiency in processing InSAR big data. These models concentrate on parallelizing critical algorithms that have high complexity, with a focus on deconstructing two computationally intensive algorithms through loop unrolling. Our parallel models have shown a significant improvement of 10–20 times in performance. We have also developed a parallel optimization tool, Simultaneous Task Automatic Runtime (STAR), which utilizes a data flow optimization strategy with thread pool technology to address the problem of low CPU utilization resulting from multiple modules and task dependencies in the InSAR processing pipeline. STAR provides a data-driven pipeline and enables concurrent execution of multiple tasks, with greater flexibility to keep the CPU busy and further improve CPU utilization through predetermined task flow. Additionally, a supercomputing-based system has been constructed for processing massive InSAR scientific big data and providing technical support for nationwide surface deformation measurement, in accordance with the framework of time series InSAR data processing. Using this system, we processed InSAR data with the volumes of 500 TB and 700 TB in 5 and 7 days, respectively. Finally we generated two maps of land surface deformation all over China.

Multithreading Based Parallel Processing for Image Geometric Coregistration in SAR Interferometry

Within the framework of multi-temporal Synthetic Aperture Radar (SAR) interferometric processing, image coregistration is a fundamental operation that might be extremely time-consuming. This paper explores the possibility of addressing fast and accurate SAR image geometric coregistration, with sub-pixel accuracy and in the presence of a complex 3-D object scene, by exploiting the parallelism offered by shared-memory architectures. An efficient and scalable processor is proposed by designing a parallel algorithm incorporating thread-level parallelism for solving the inherent computationally intensive problem. The adopted functional scheme is first mathematically framed and then investigated in detail in terms of its computational structures. Subsequently, a parallel version of the algorithm is designed, according to a fork-join model, by suitably taking into account the granularity of the decomposition, load-balancing, and different scheduling strategies. The developed parallel algorithm implements parallelism at the thread-level by using OpenMP (Open Multi-Processing) and it is specifically targeted at shared-memory multiprocessors. The parallel performance of the implemented multithreading-based SAR image coregistration prototype processor is experimentally investigated and quantitatively assessed by processing high-resolution X-band COSMO-SkyMed SAR data and using two different multicore architectures. The effectiveness of the developed multithreaded prototype solution in fully benefitting from the computing power offered by multicore processors has successfully been demonstrated via a suitable experimental performance analysis conducted in terms of parallel speedup and efficiency. The demonstrated scalable performance and portability of the developed parallel processor confirm its potential for operational use in the interferometric SAR data processing at large scales.

Investigation and modification of the inversed vortex phase field method for phase unwrapping

The paper focuses on the problem of the phase unwrapping in spaceborne remote-sensing interferometric synthesized aperture radar (InSAR) systems. Major unwrapping methods and techniques are considered and the modification of the inversed vortex phase field method of phase unwrapping for interferometric data processing of space-borne synthesized aperture radars is proposed. The modification includes the separation and unwrapping of the low-frequency phase only, and obtaining of the residual phase interferogram, which phase range does not exceed 1-2 ambiguity height values. This approach significantly reduces the number of phase residues and increases the processing speed. The other modification implies filter processing of the residual phase without phase unwrapping, which includes iterative separation of the low-frequency using the Gaussian filter and phase subtraction. This approach moves phase fringes to the relief inflection areas, and is similar to the minimum-cost flow unwrapping results. The computational complexity of the algorithm is proportional to the interferogram size and the number of the phase residues of the low-frequency phase interferogram. The accuracy of digital elevation models obtained by the algorithm was estimated using the ALOS PALSAR radar data and the reference altitude data. The results show, that the accuracy is compared with the minimum-cost flow method, but has the less computational complexity.

THE NEW TANDEM-X CHANGE DEM: SPECIFICATIONS AND INTERFEROMETRIC PROCESSING

Abstract. Since 2017, the TanDEM-X mission aims to acquire data globally to generate another (updated) DEM. This new set of acquisitions, which will be complete in 2020, has a clear temporal separation from those used for the TanDEM-X global DEM. It will therefore allow the creation of a temporally independent DEM, the so-called “TanDEM-X Change DEM” enabling the characterization of terrain changes. Since only one global coverage is being acquired, improvements in e.g. the interferometric data processing are necessary. In particular, an edited version of the existing global TanDEM-X DEM is now the "starting point" for the interferometric processing as detailed in this paper.

Long-Term Remote Monitoring of Ground Deformation Using Sentinel-1 Interferometric Synthetic Aperture Radar (InSAR): Applications and Insights into Geotechnical Engineering Practices

Development of synthetic aperture radar (SAR) technology and the dedicated suite of processing tools have aided the evolution of remote sensing techniques for various Earth Observation (EO) applications. Interferometric SAR (InSAR) is a relatively new geodetic technique which provides high-speed and reliable geographic, geologic, and hazards information allowing the prognosis of future environmental and urban planning. In this study, we explored the applicability of two differential interferometry techniques, conventional and advanced differential InSAR (A-DInSAR), for topographic mapping and long-term geotechnical monitoring by exploiting satellite data, particularly Sentinel-1 SAR data, which is publicly shared. We specifically used the open-source tools of SeNtinel Application Platform (SNAP) and Stanford Method for Persistent Scatterers (StaMPS) for interferometric data processing to implement A-DInSAR. This study presents various applications, which include generation of a digital elevation model (DEM), mapping of seismically induced displacement and associated damages, and detection and long-term monitoring of tunneling-induced ground deformation and rainfall-induced landslide. Geometric and temporal decorrelations posed challenges and limitations in the successful implementation of Sentinel-1 SAR interferometry specifically in vegetated areas. The presented results proved the validity and reliability of the exploited SAR data and InSAR techniques for addressing geotechnical engineering related problems.

The problem of quality assessing for the methods of coherence maps calculation in InSAR remote sensing of the Earth data processing

Interferometric coherence is an important indicator of the quality of interferograms obtained by synthetic aperture interferometric radars (InSAR), because the areas with low coherence are not suitable for interferometric data processing. The coherence value is used as a parameter for adaptive phase noise suppression algorithms. It can also be used for surface classification tasks. The paper investigates the problem of the coherence estimate reducing under the influence of the topographic phase slope and considers ways to reduce the impact of the slope on the estimate value. The paper presents a comparative efficiency analysis of four methods for coherence maps calculation used for the phase noise suppression on the interferograms by a spectral adaptive filter in interferometric data processing for the Earth’s remote sensing space radar ALOS PALSAR

Statistical performance of radio interferometric calibration

Calibration is an essential step in radio interferometric data processing that corrects the data for systematic errors and in addition, subtracts bright foreground interference to reveal weak signals hidden in the residual. These weak and unknown signals are much sought after to reach many science goals but the effect of calibration on such signals is an ever present concern. The main reason for this is the incompleteness of the model used in calibration. Distributed calibration based on consensus optimization has been shown to mitigate the effect due to model incompleteness by calibrating data covering a wide bandwidth in a computationally efficient manner. In this paper, we study the statistical performance of direction dependent distributed calibration, i.e., the distortion caused by calibration on the residual statistics. In order to study this, we consider the mapping between the input uncalibrated data and the output residual data. We derive an analytical relationship for the influence of the input on the residual and use this to find the relationship between the input and output probability density functions. Using simulations we show that the smallest eigenvalue of the Jacobian of this mapping is a reliable indicator of the statistical performance of calibration. The analysis developed in this paper can also be applied to other data processing steps in radio interferometry such as imaging and foreground subtraction as well as to many other machine learning problems.

Systematic preparation and processing of interferometric synthetic aperture radar data for monitoring linear transportation infrastructure

We propose a methodology for the systematic preparation and processing of interferometric synthetic aperture radar (InSAR) data for monitoring linear transportation infrastructure subject to geohazards. The methodology is applied to two RADARSAT-2 Spotlight synthetic aperture radar datasets, and three case studies in Cornwall, Eastern Ontario, Canada, are examined. An InSAR processing sequence was established and 19 SLA24 and 15 SLA74 images were used to create time-series deformation maps spanning from March 2015 to September 2016. The noise floors were ±1.5 and ±1.0 cm, for the SLA24 and SLA74 datasets, respectively. Phase unwrapping errors, atmospheric path delay, and the limited number of images were identified as the largest contributors to measurement uncertainty, which was of the same order as the ground deformation field. To improve coherence and utility of the radar images for monitoring the effects of geohazards on infrastructure, it is recommended that imagery acquisitions consider the use of small incidence angles with moderate image resolution and 6- to 12-day revisit periods.

Experimental estimation of phase noise filters efficiency for InSAR processing

The experimental study of the efficiency of interferometric data processing algorithms for synthetic aperture space radars is complicated by the fact that a quantitative result can be obtained only after the completion of the entire technological chain. The efficiency of the first stages of interferometric processing - incoherent accumulation and suppression of phase noise-has been studied on the basis of the method of inverse transformation of standards previously proposed by the authors. The results of estimation of accuracy of digital elevation model construction for ALOS PALSAR data with different base lengths and polarizations of the probing signal are presented.

QUALITY ASSESSMENT OF AN EXTENDED INTERFEROMETRIC RADAR DATA PROCESSING APPROACH

Abstract. Radar data acquisition is a reliable technology to provide base data for topographical mapping. Its flexibility and weather independency makes radar data more attractive in comparison with traditional airborne data acquisition. This advantage emplaces radar data acquisition as an alternative method for many applications including Large Scale Topographical Mapping (LSTM). LSTM i.e. larger or equal than 1 : 10.000 map scale is one of the prominent priority tasks to be finished in an accelerated way especially in third world countries such as Indonesia. The available TerraSAR-X Add on Digital Elevation Model X (TanDEM-X) Intermediate Digital Elevation Model (IDEM) from German Aerospace Center (DLR) as one useful global scientific data set however still complies with High Resolution Terrain Information (HRTI) Level 3 only. The accuracy of the end product of pairwise bi-static TanDEM-X data can be improved by some potential measures such as incorporation of Ground Control Points (GCPs) within the interferometric data processing. It is expected that the corresponding end product can fulfil HRTI Level 4 specification. From this point, we focus on the step of phase difference measurements in radar interferometry to generate elevation model with least square adjustment approach using three main parameters i.e. height reference, absolute phase offset and baseline. Those three parameters are considered to be essential within the Digital Surface Model (DSM) generation process. Therefore it is necessary to find the optimal solution within aforementioned adjustment model. In this paper we use an linearized model, as discussed in section 2.4, to process the bi-static TanDEM-X datasets and investigate how this improves the accuracy of the generated DSM. As interferometric radar data processing relies on accurate GCP data we use Indonesian Geospatial Reference System (SRGI) for our investigations. Also, we use baseline and phase offset information from TanDEM-X metadata. Subsequently, the DSM generated using Sentinel Application Platform (SNAP) desktop, is the main product used for LSTM. This product has to be assessed using check points derived from conventional airborne data acquisition using RCD-30 metric camera and the accuracy is compared with the accuracy of the IDEM. Summarized, this paper aims on an improvement of the DSM generation by adjusting main parameters through our linearized model.

The Parallel SBAS-DInSAR Processing Chain for the Generation of National Scale Sentinel-1 Deformation Time-Series

In this work we present a fully parallel and automatic interferometric processing chain, based on the well-known advanced DInSAR algorithm referred to as Parallel Small BAseline Subset (P-SBAS), for the effective and massive generation of Sentinel-1 (S-1) Interferometric Wide Swath (IWS) displacement time-series and corresponding mean deformation velocity maps of very wide areas. The strategy chosen to implement such a parallel processing chain fully benefits from the Terrain Observation with Progressive Scans (TOPS) mode characterizing the IWS acquisitions of the S-1 constellation for systematic monitoring of large land and coastal areas. The TOPS mode consists of a series of bursts that can be considered as separate images to be independently processed. Such a data structure fosters the parallelization of the S-1 IWS interferometric data processing, which can be automatically carried out through the exploitation of both high-performance distributed computing infrastructures and efficient algorithms for tackling the huge data flow provided by the S-1 constellation.To demonstrate the capability of the presented S-1 P-SBAS processing chain to deal with the huge volume of data acquired by the S-1 constellation, we process a very extended dataset acquired over the Italian territory from which we compute the mean velocity maps and associated deformation time-series.

Monitoring Alpine glacier surface deformations with GB-SAR

ABSTRACTWe present methods and results from interferometric data processing of a long-lasting survey campaign monitoring the Planpincieux glacier, located on the Italian side of the Mont Blanc, using a ground-based synthetic aperture radar (GB-SAR). Monitoring a European Alpine glacier during the winter, when the meteorological conditions are highly variable, presents some difficulties in radar data interpretation. The main issues to tackle in interferometric processing are unwrapping errors and high amplitude dispersion (DA), mainly due to the high velocity and dielectric heterogeneity of the backscattering surface. To improve the reliability of the results, a coherence-driven pixel-selection criterion for identifying the glacier area and a simple approach to reduce possible unwrapping errors in interferograms with low coherence are here proposed. The development of a new 2D polynomial regression model, as a function of elevation, for atmospheric phase screen (APS) estimation is also discussed. A comparison with the results obtained with a vision-based approach gave showed good agreement.

Radar satellites measure ice cover displacements induced by moving vehicles

Vehicles moving on floating ice modulate its surface depending on ice, bathymetric, and traffic variables. Under certain conditions, they may cause ice cover breakthroughs that jeopardize productivity, property, and life. The initiation of ice cover failure by traffic is commonly referred to as the moving vehicle problem. Scientific progress towards mitigating this problem has been limited. Subject-matter experts have argued that this is due, in part, to the absence of convenient and accurate means to measure ice cover modulations. Here we demonstrate a previously unknown capacity of radar earth observation satellites to measure the modulation of ice cover by moving vehicles with an unparalleled breadth of view, superior spatial detail and millimeter-scale vertical accuracy. This capacity derives from satellite radar technology that is experimental at present and differential interferometric radar data processing techniques. Ice modulations measured by means of conventional devices are typically described as ‘deflections’; we purposely refer to the radar satellite measurements as ‘displacements’ to signify a difference in the measurement method and result. Our findings indicate opportunities for ice road and radar earth observation stakeholders, may spark renewed interest in and progress towards mitigating the moving vehicle problem, and thus help improve the efficiency and safety of ice road transportation.

Tomographic Processing of Interferometric SAR Data: Developments, applications, and future research perspectives

Synthetic aperture radar (SAR) data processed with interferometric techniques are widely used today for environmental risk monitoring and security. SAR tomography techniques are a recent advance that provide improved three-dimensional (3-D) reconstruction and long-term deformation monitoring capabilities. This article is meant to discuss the main developments achieved in the last few years in the SAR tomography framework, with particular reference to both urban and forest scenarios. An insight on classical multipass interferometric processing is also included to summarize the importance of the technology for natural hazards monitoring and to provide the basis for the description of SAR tomography.

Edge Detection Using Real and Imaginary Decomposition of SAR Data

The objective of synthetic aperture radar (SAR) edge detection is the identification of contours across the investigated scene, exploiting SAR complex data. Edge detectors available in the literature exploit singularly amplitude and interferometric phase information, looking for reflectivity or height difference between neighboring pixels, respectively. Recently, more performing detectors based on the joint processing of amplitude and interferometric phase data have been presented. In this paper, we propose a novel approach based on the exploitation of real and imaginary parts of single-look complex acquired data. The technique is developed in the framework of stochastic estimation theory, exploiting Markov random fields. Compared to available edge detectors, the technique proposed in this paper shows useful advantages in terms of model complexity, phase artifact robustness, and scenario applicability. Experimental results on both simulated and real TerraSAR-X and COSMO-SkyMed data show the interesting performances and the overall effectiveness of the proposed method.

Robust radio interferometric calibration using the t-distribution

A major stage of radio interferometric data processing is calibration or the estimation of systematic errors in the data and the correction for such errors. A stochastic error (noise) model is assumed, and in most cases, this underlying model is assumed to be Gaussian. However, outliers in the data due to interference or due to errors in the sky model would have adverse effects on processing based on a Gaussian noise model. Most of the shortcomings of calibration such as the loss in flux or coherence, and the appearance of spurious sources, could be attributed to the deviations of the underlying noise model. In this paper, we propose to improve the robustness of calibration by using a noise model based on Student's t distribution. Student's t noise is a special case of Gaussian noise when the variance is unknown. Unlike Gaussian noise model based calibration, traditional least squares minimization would not directly extend to a case when we have a Student's t noise model. Therefore, we use a variant of the Expectation Maximization (EM) algorithm, called the Expectation-Conditional Maximization Either (ECME) algorithm when we have a Student's t noise model and use the Levenberg-Marquardt algorithm in the maximization step. We give simulation results to show the robustness of the proposed calibration method as opposed to traditional Gaussian noise model based calibration, especially in preserving the flux of weaker sources that are not included in the calibration model.

Wideband Interferometry in Synthetic Aperture Sonar

Synthetic aperture sonar (SAS) interferometry can provide very high resolution images and topographic maps of the seafloor over large swaths. Processing of interferometric data to retrieve reliable depth estimates of the seafloor is, however, nontrivial. Traditional narrowband interferometry processing relies on advanced phase unwrapping techniques, constraints, and prior knowledge to resolve phase ambiguities. These methods all have dependencies throughout the dataset, e.g., a small error in the assumption may cause a global error in the final phase estimate. For wideband systems, there are alternative techniques to estimate the absolute (or ambiguity-free) phase difference directly. We consider four different wideband interferometry techniques for direct phase difference estimation: complex cross correlation, split-spectrum algorithm, and the multichromatic approach. In addition, we develop a weighted split-spectrum algorithm, where the weights minimize the variance in the absolute phase estimate. We benchmark these techniques on simulated data and demonstrate the techniques on real data from the HISAS wideband interferometric SAS on a HUGIN autonomous underwater vehicle. We have found the following: the cross correlation technique always outperforms the other techniques in misregistrated areas caused by severe topographic changes. The split spectrum techniques are substantially faster than the cross correlation technique. The multichromatic approach has similar performance as the multiband split spectrum technique for the same choice of bands. We demonstrate that all the wideband interferometry techniques outperform a standard Goldstein's Branch cut phase unwrapping algorithm on real data from a complicated scene containing an elevated target and severe layover.

Data Processing of the Middle and Upper Atmospheric Wind Retrieval Based on the Fabry‐Perot Interferometer

Abstract:Since the Doppler shifts of airglow orauroralemission lines due to neutral winds are extremely small, it is essential to determine the center position and radius of fringes with the uppermost accuracy. In this paper, a new method is presented for the center position and radius determination of incomplete interference fringes of Fabry‐Perot Interferometer (FPI). Due to the data noise comes from FPI instrument and space environment, the data pre‐processing was firstly carried out including mean filtering and wiener weightingadaptive filtering. Then, according to the Gauss distribution of fringe intensity, interference fringe peak coordinates were derived from both radius obtained using Gaussian fitting and fringe center coordinates. Subsequently, based on the peak coordinates, new center position and radius were estimated by using circular coordinate fitting with the least square method. Finally, the center coordinate and radius were determined by using the iterative operation between Gaussian fitting and circular coordinate fitting. The new method was applied to Fabry‐Perot interferometer data processing for wind velocity estimation of the middle and upper atmosphere. The retrieval results are 96.9537 m/s (actual wind velocity ∼100 m/s) and 7.5282 m/s (actual wind velocity ∼10 m/s), lower by 2.977 m/s and 2.465 m/s, respectively. Compared with theoretical values, both of them are less than 3 m/s, which suggests a tentatively reliable and practicable method of circle center and radius determination for wind retrieval of the middle and upper atmosphere.