SAR Processing Research Articles

Monitoring land subsidence in the Peshawar District, Pakistan, with a multi-track PS-InSAR technique.

Peshawar is one of the most densely populated cities of Pakistan with high urbanization rate. The city overexploits groundwater resources for household and commercial usage which has caused land subsidence. Land subsidence has long been an issue in Peshawar due to insufficient groundwater removal. In this research, we employ the persistent scatterer interferometry synthetic aperture radar (PS-InSAR) technique with Sentinel-1 imaging data to observe the yearly land subsidence and generate accumulative time-series maps for the years (2018 to 2020) using the SAR PROcessing tool (SARPROZ). The PS-InSAR findings from two contiguous paths are combined by considering the variance over the overlapping area. The subsidence rates in the Peshawar are from -59 to 17 mm/yr. The results show that subsidence is -28.48 mm/yr in 2018, the subsidence reached -49.02 mm/yr in 2019, while in 2020, the subsidence reached -49.90 mm/yr. The findings indicate a notable rise in land subsidence between the years 2018 and 2020. Subsidence is predicted in the research region primarily due to excessive groundwater removal and soil consolidation induced by surficial loads. The correlation of land subsidence observations with groundwater levels and precipitation data revealed some relationships. Overall, the proposed method efficiently monitors, maps, and detects subsidence-prone areas. The utilization of land subsidence maps will enhance the efficiency of urban planning, construction of surface infrastructure, and the management of risks associated with subsidence.

EyeRADAR-Dam: Integration of MT-InSAR with monitoring technologies in a pilot monitoring system for embankment dams

Dams are one of the larger infrastructures of human civilization in the 21st century and subjected to increasing safety monitoring in the frame of global change. The quick development of SAR interferometry and processing capabilities allow near-real time monitoring of dams and associated areas and infrastructures. A pilot project for the surveillance and management of embankment dams, EyeRADAR-Dam has been developed in an industry-academia collaboration, supported by scientific validation. The potential of EyeRADAR-Dam lies in the integration via API of monitoring data and SAR time series dating from 1992 to present. The processed SAR data is input to the platform, together with relevant monitoring data (climate, topography, geotechnics, hydrology, flows…). A two-scale approach allows monitoring the entire reservoir, with slopes and infrastructures as a whole while simultaneously monitoring in detail critical infrastructures and dam body. EyeRADAR-Dam is part of a new generation of management platforms that integrate space data in daily operations.

Pengaruh Koreksi Ionosfer Terhadap Pergeseran Vertikal Pada Citra Satelit ALOS-PALSAR

Interferometric Synthetic Aperture Radar (InSAR) is an effective technique for changing the earth's surface with wide coverage and high accuracy. However, the accuracy of InSAR can be affected by wave propagation activity in the atmosphere. The ionospheric medium in the atmosphere contains free electrons which cause unstable waves to produce bias. Therefore, it is necessary to correct the ionosphere in SAR image processing so that it does not contain bias. This study examines the effect of ionospheric correction on vertical deformation in ALOS-PALSAR satellite imagery. The data used includes satellite image data and GNSS. Image data is processed by considering the presence or absence of ionospheric correction so that the results can be analyzed to determine the effect of the ionosphere on SAR images. GNSS data is processed by a static method to produce coordinates that can be used as a reference for validating the results of SAR image processing. The results of SAR and GNSS image processing are then visualized in the form of a vertical deformation map to facilitate the analysis of the results. The results showed that the direction of the vertical deformation in the ionospheric corrected satellite image was consistent with the direction of the vertical deformation in the GNSS. This indicates that the ionospheric correction affects the vertical deformation in the satellite image. In the process of vertical changes, there are obstacles, namely the vertical point reference has not been used as a validation of the results. In addition, there are no other satellite image references to compare the results of the ALOS-PALSAR satellite image processing.

Rapid Mapping of Slow-Moving Landslides Using an Automated SAR Processing Platform (HyP3) and Stacking-InSAR Method

The increasing number of landslide hazards worldwide has placed greater demands on the production and updating of landslide inventory maps. As an important data source for landslide detection, interferometric synthetic aperture radar (InSAR) data processing is time-consuming and also requires specialized knowledge, which severely hinders its widespread application. At present, a new cloud-based online platform, i.e., Alaska Satellite Facility’s Hybrid Pluggable Processing Pipeline (ASF HyP3) was developed for massive SAR data processing. In this study, combining the HyP3 online platform and Stacking-InSAR method, we constructed a new easy-to-use processing chain for rapidly identifying slow-moving landslides over large areas. With this processing chain, a total of 923 interferometric pairs covering an area of over 1800 km2 were processed within a few hours (about 4 to 5 h). A total of 81 slow-moving landslides were immediately detected and mapped using Stacking-InSAR method, of which 65 landslides were confirmed by previous studies and 16 landslides were newly detected. Results show that the new processing chain can greatly improve the efficiency of wide-area landslide mapping and is expected to serve as an effective tool for rapid updating the existing landslide inventories and contribute to the prevention and management of geological hazards.

Benefits of fully focused SAR altimetry to coastal wave height estimates: A case study in the North Sea

Estimating the three geophysical variables significant wave height (SWH), sea surface height, and wind speed from satellite altimetry continues to be challenging in the coastal zone because the received radar echoes exhibit significant interference from strongly reflective targets such as sandbanks, sheltered bays, ships etc. Fully focused SAR (FF-SAR) processing exhibits a theoretical along-track resolution of up to less than half a metre. This suggests that the application of FF-SAR altimetry might give potential gains over unfocused SAR (UF-SAR) altimetry to resolve and mitigate small-scale interferers in the along-track direction to improve the accuracy and precision of the geophysical estimates.The objective of this study is to assess the applicability of FF-SAR-processed Sentinel-6 Michael Freilich (S6-MF) coastal altimetry data to obtain SWH estimates as close as possible to the coast.We have developed a multi-mission FF-SAR processor and applied the coastal retracking algorithm CORALv2 to estimate SWH. We assess different FF-SAR and UF-SAR processing configurations, as well as the baseline Level-2 product from EUMETSAT, by comparison with the coastal, high-resolution SWAN-Kuststrook wave model from the Deltares RWsOS North Sea operational forecasting system. This includes the evaluation of the correlation, the median offset, and the percentage of cycles with high correlation as a function of distance to the nearest coastline. Moreover, we analyse the number of valid records and the L2 noise of the records. The case study comprises five coastal crossings of S6-MF that are located along the Dutch coast and the German coast along the East Frisian Islands in the North Sea.We observe that accurate and precise SWH records can be estimated in the nearshore zone within 1–3 km from the coast using satellite SAR altimetry. We find that the FF-SAR-processed dataset with a Level-1b posting rate of 140 Hz shows the greatest similarity with the wave model. We achieve a correlation of ∼0.8 at 80% of valid records and a gain in precision of up to 29% of FF-SAR vs UF-SAR for 1–3 km from the coast. FF-SAR shows, for all cycles, a high correlation of greater than or equal to 0.8 for 1–3 km from the coast. We estimate the decay of SWH from offshore at 30 km to up to 1 km from the coast to amount to 26.4% ± 3.1%.

Ship velocity estimation in SAR images using multitask deep learning

SAR satellites are used for monitoring ships worldwide. Moving ships are Doppler shifted by an amount proportional to their velocity. An offset between the ship and its wake is then produced during SAR processing. We present a novel automatic method for calculating the ship velocity. The method relies on multitask deep learning to estimate the offset and ship heading. From these parameters, the ship velocity can be obtained. A convolutional neural network is trained using a coupled loss function. The loss function allows both parameters to be estimated at the same time. We show the methods’ effectiveness for ships in Sentinel-1 SAR images. For this purpose, a large dataset of 30,000 AIS annotated SAR ship images is collected. These images have 20 × 22 m pixel resolution and ships do not have a clear wake. The AIS provides the true ship velocity and allows the method to be evaluated. As a result, we can determine the ship speed with an accuracy of 1.1 m/s. The offset disappears near the azimuth direction of the SAR image. Yet, our method is reliable except for ships sailing within 2.5 degrees of the azimuth direction.

Mapping of Mean Deformation Rates Based on APS-Corrected InSAR Data Using Unsupervised Clustering Algorithms

Different interferometric approaches have been developed over the past few decades to process SAR data and recover surface deformation, and each approach has advantages and limitations. Finding an accurate and reliable interval for preparing mean deformation rate maps (MDRMs) remains challenging. The primary purpose of this paper is to implement an application consisting of three unsupervised clustering algorithms (UCAs) for determining the best interval from SAR-derived deformation data, which can be used to interpret long-term deformation processes, such as subsidence, and identify displacement patterns. Considering Port Harcourt (in the Niger Delta) as the study area, it was essential to remove the sources of error in extracting deformation signals from SAR data, spatially ionospheric and tropospheric delays, before using UCAs to obtain its characteristics and real deformation data. Moreover, another purpose of this paper is to implement the advanced integration method (AIM) for atmospheric phase screen (APS) correction to enhance deformation signals obtained through different SAR processing approaches, including interferometric SARs (two-pass interferometry, InSAR) and multitemporal interferometry SARs (n-pass interferometry, DInSAR; permanent scatterer interferometry (PSI); and small baseline subset (SBAS)). Two methods were chosen to evaluate and find the best technique with which to create an MDRM: The first one was to compare the signals corrected by the AIM and the vertical component of the GPS station, which showed the AIM providing 58%, 42%, and 28% of the matching with GNSS station outputs for InSAR, PSI, and SBAS, respectively. Secondly, similarity measures and Davies–Bouldin index scores were implemented to find an accurate and reliable interval in which the SBAS technique with the unsupervised K-medians method has been chosen. Based on GNSS vertical deformation in a 500 m radius around the station, the SBAS K-medians technique expressed up to 5.5% better deformation patterns than the map of SAR processing techniques.

A Functional Model for Determining Maximum Detectable Deformation Gradients of InSAR Considering the Topography in Mountainous Areas

The maximum detectable deformation gradients (MDDG) for interferometric synthetic aperture radar (InSAR) technology is important for the selection of SAR images and processing algorithms to perform accurate slope displacement monitoring, which is strongly influenced by terrain factors in mountainous areas. In this paper, a functional model is proposed to determine the MDDG of InSAR with respect to arbitrary slope gradients/aspects and wavelengths. Based on this model, regional MDDG characteristics are explored and compared in Mao County, Sichuan Province, China. The MDDG distribution regarding on Sentinel-1, ALOS-2/PALSAR-2 and TerraSAR-X SAR satellite data using arbitrary slope gradient/aspect are derived. Furthermore, the MDDG from variable satellites for three different bands (X/C/L-band) are compared and the influence factors with respect to the wavelength and resolution on MDDG are discussed. The proposed model is helpful in selecting of SAR data or processing algorithms based on calculated MDDG, in the meanwhile, it has significant implications on the understanding and analyzing real slope displacement monitored by InSAR regarding on different SAR images in mountainous areas.

Accelerated Back-Projection SAR Processor on Arbitrary Elliptical Imaging Grid With Azimuth Spectrum Unfolding

Accelerated Back-Projection SAR Processor on Arbitrary Elliptical Imaging Grid With Azimuth Spectrum Unfolding

A Frequency Diverse Array SAR Processing Framework Based on the Segmented Phase Code Waveform for HRWS Imaging

A Frequency Diverse Array SAR Processing Framework Based on the Segmented Phase Code Waveform for HRWS Imaging

Wind‐Wave Attenuation in Arctic Sea Ice: A Discussion of Remote Sensing Capabilities

AbstractWind‐generated waves strongly interact with sea ice and impact air‐sea exchanges, operations at sea, and marine life. Unfortunately, the dissipation of wave energy is not well quantified and its possible effect on upper ocean mixing and ice drift is still mysterious. As the Arctic is opening up and wave energy increases, the limited amount of in situ observations is a clear limitation to our scientific understanding. Both radar and optical remote sensing has revealed the frequent presence of waves in ice, and could be used more systematically to investigate wave‐ice interactions. Here we show that, in cloud‐free conditions, Sentinel‐2 images exhibit brightness modulations in ice‐covered water, consistent with the presence of waves measured a few hours later by the ICESat‐2 laser altimeter. We show that a full‐focus SAR processing of Sentinel‐3 radar altimeter data also reveals the presence and wavelengths of waves in sea ice, within minutes of Sentinel‐2 imagery. The SWIM instrument on CFOSAT is another source of quantitative evidence for the direction and wavelengths of waves in ice, when ice conditions are spatially homogeneous. In the presence of sea ice, a quantitative wave height measurement method is not yet available for all‐weather near‐nadir radar instruments such as altimeters and SWIM. However, their systematic colocation with optical instruments on Sentinel‐2 and ICESat‐2, which are less frequently able to observe waves in sea ice, may provide the empirical transfer functions needed to interpret and calibrate the radar data, greatly expanding the available data on wave‐ice interactions.

Detection of sinkhole activity in West-Central Florida using InSAR time series observations

Sinkhole activity in Florida is a major hazard for people and property. Its increasing frequency is often related to an accelerated use of ground-water and land resources in the region. In this work, we use a combined approach of radar interferometry and spatial clustering analysis over three selected sites in West-Central Florida to identify localized deformation that may be caused by sinkhole activity. The region of West-Central Florida is a densely active sinkhole region, where sinkholes tend to be small and land cover is mixed resulting in variable interferometric coherence that complicates Interferometric Synthetic Aperture Radar (InSAR) surveys. In this work, we present a combined methodology implementing InSAR and a Density-Based Spatial Clustering Analysis (DBSCAN) algorithm to detect unknown sinkhole activity and to alert to possible precursors of sinkhole collapse. The data used for the study consist of acquisitions from three TerraSAR-X frames covering time spans of ~1.7 and 2.5 years with spatial resolutions ranging from 25 cm up to 1 m. We applied the Persistent Scatterer Interferometry (PSI) technique using the Stanford Method for Persistent Scatterers (StaMPS) and confirmed the observed deformation signals by also processing the data using the SAR PROcessing tool (SARPROZ). Results show several areas of localized subsidence, from which the cluster with highest rates for each site was selected for detailed inspection. Locations of selected clusters were found in buildings with sizes ranging from 300 m2 to nearly 2000 m2, with subsidence trends ranging from −3 to −6 mm/yr. Results were compared with in-situ observations such as ground penetrating radar (GPR), electrical resistivity tomography (ERT) surveys, visual structural inspection and public county archive documents to help as ground truthing; subsiding locations were found to be related to sinkhole presence or development.

Exploiting Chirp Rate Estimation Methods to Improve Image Formation Quality of Synthetic Aperture Radar

A common assumption in SAR image formation and processing algorithms is that the chirp rates of the transmitted and received radar signals are exactly the same. Dechirp processing is also done based on this common assumption. In real scenarios, the chirp rate of the received signal is different from that of the transmitted signal due to several reasons. In case the difference between the chirp rates of the transmitted and received signals is obvious, the demodulation and compression of the received pulse are not carried out precisely and defocusing the targets and the output images of the SAR processor results. In the present paper, a new technique is proposed to improve the image formation quality of SAR by exploiting chirp rate estimation methods. Based on the proposed technique, the chirp rate of the received signal is estimated, and then, dechirp is carried out by using a time-reversed complex conjugate filter constructed based on the estimated chirp rate. In this stage, the existing chirp rate estimation algorithms can be used. The quality of the output image is assessed using PSLR as a quantitative criterion and the average number of point target extension pixels along the azimuth direction. Simulation results indicated that the smaller the average number of point target extension pixels along with azimuth and the higher the PSLR average is, the better the output image quality would be. Therefore, output images obtained from the proposed method by exploiting chirp rate estimation algorithms would have a better quality with a higher PSLR average (14.1 and 13.6) and also the lower average number of point target extension pixels along the azimuth directions (2.1 and 4.9) than the common method with PSLR average (8.3) and an average number of point target extension pixels along the azimuth direction (7.1).

LANDSLIDES MONITORING WITH TIME SERIES OF SENTINEL-1 IMAGERY IN YEN BAI PROVINCE-VIETNAM

Abstract. This paper presents an application of PS-InSAR method for determining landslide displacement velocity in Van Yen district, Yen Bai province, Vietnam. The used tools for processing data is a combination of two free software, SNAP 7.0 and STaMPS 4.1. With 27 Sentinel-1A images in descending direction acquired from 11th January 2019 to 1st March 2021, the landslide displacement values were calculated and exported. There were locations in which landslides correctly appeared, such as Lang Thip, Xuan Tam, Chau Que Ha, Phong Du Thuong communes and along provincial road 151. Landslide rate is determined from SAR image series with average value less than 16.5 mm/y in places with high terrain and steep slope. The distribution of permanent scatter (PS) points for landslides often appeared along the road slopes, especially the inter-communal and inter-provincial roads that have not been reinforced with structural mitigation measures. In 2013 a field survey was conducted by the Vietnam Institute of Geosciences and Mineral Resources for this area which was used to validate the results from SAR processing. Landslide velocity charts at certain landslide sites were derived. The current study demonstrated the feasibility of the method as well as the usage of Sentinel-1 data for land deformation monitoring in the mountainous area.

Results of the Dragon 4 Project on New Ocean Remote Sensing Data for Operational Applications

This paper provides an overview of the Dragon 4 project dealing with operational monitoring of sea ice and sea surface salinity (SSS) and new product developments for altimetry data. To improve sea ice thickness retrieval, a new method was developed to match the Cryosat-2 radar waveform. Additionally, an automated sea ice drift detection scheme was developed and tested on Sentinel-1 data, and the sea ice drifty capability of Gaofen-4 geostationary optical data was evaluated. A second topic included implementation and validation of a prototype of a Fully-Focussed SAR processor adapted for Sentinel-3 and Sentinel-6 altimeters and evaluation of its performance with Sentinel-3 data over the Yellow Sea; the assessment of sea surface height (SSH), significant wave height (SWH), and wind speed measurements using different altimeters and CFOSAT SWIM; and the fusion of SSH measurements in mapping sea level anomaly (SLA) data to detect mesoscale eddies. Thirdly, the investigations on the retrieval of SSS include simulations to analyse the performances of the Chinese payload configurations of the Interferometric Microwave Radiometer and the Microwave Imager Combined Active and Passive, SSS retrieval under rain conditions, and the combination of active and passive microwave to study extreme winds.

SectorInsights.com—The Future of SAR Processing is in the Cloud

SectorInsights.com—The Future of SAR Processing is in the Cloud

Sentinel1の干渉SAR処理による2020年８月ベイルート爆発事故の被害調査

Sentinel1の干渉SAR処理による2020年８月ベイルート爆発事故の被害調査

A Fully Automatic Algorithm for Editing the TanDEM-X Global DEM

The spaceborne mission TanDEM-X successfully acquired and processed a global Digital Elevation Model (DEM) from interferometric bistatic SAR data at X band. The product has been delivered in 2016 and is characterized by an unprecedented vertical accuracy. It is provided at 12 m, 30 m, and 90 m sampling and can be accessed by the scientific community via a standard announcement of opportunity process and the submission of a scientific proposal. The 90 m version is freely available for scientific purposes. The DEM is unedited, which means that it is the pure result of the interferometric SAR processing and subsequent mosaicking. Residual gaps, resulting, e.g., from unprocessable data, are still present and water surfaces appear noisy. This paper reports on the algorithms developed at DLR’s Microwaves and Radar Institute for a fully automatic editing of the global TanDEM-X DEM comprising gap filling and water editing. The result is a new global gap-free DEM product at 30 m sampling, which can be used for a large variety of scientific applications. It also serves as a reference for processing the upcoming TanDEM-X Change DEM layer.

A Bistatic Analytical Approximation Model for Doppler Rate Estimation Error from Real-Time Spaceborne SAR Onboard Orbit Determination Data

Real-time spaceborne bistatic SAR imaging could significantly reduce the whole processing time and can enhance the spaceborne SAR mission availability. Onboard real-time SAR imaging relies on the Doppler parameters estimated from the real-time onboard orbit determination system (OODS) measurement, whose accuracy level is not comparable to the orbit ephemeris data in ground-based SAR processing. The investigation of the impact of error in real-time OODS measurements on bistatic SAR image quality is necessary, and it can help to clarify the key parameter limits of the real-time OODS. The monostatic analytical approximation model (MonoAAM) for spaceborne SAR reduces simulation complexity and processing time compared to the widely used numerical simulation method. However, due to the different configurations between spaceborne bistatic and monostatic SAR, simply applying the MonoAAM on spaceborne bistatic SAR cannot guarantee the desired result. A bistatic analytical approximation model (BiAAM) for Doppler rate estimation error from real-time OODS measurement in real-time spaceborne bistatic SAR imaging is proposed for characterizing the estimation error. Selecting quadratic phase error (QPE) as an evaluation variable, the proposed BiAAM model can provide QPE estimation results for each position of the satellite in its orbit and the maximum QPE estimation for the whole orbit, while revealing the different process of OODS measurement error transferring to QPE in spaceborne bistatic SAR. The correctness and reliability of BiAAM are evaluated by comparing the result with a Monte Carlo numerical simulation method. With the supporting result from BiAAM, the concept and early-stage development of a real-time onboard bistatic SAR imaging mission could be possibly benefited.

An Optimized Filtering Method of Massive Interferometric SAR Data for Urban Areas by Online Tensor Decomposition

The filtering of multi-pass synthetic aperture radar interferometry (InSAR) stack data is a necessary preprocessing step utilized to improve the accuracy of the object-based three-dimensional information inversion in urban area. InSAR stack data is composed of multi-temporal homogeneous data, which is regarded as a third-order tensor. The InSAR tensor can be filtered by data fusion, i.e., tensor decomposition, and these filters keep balance in the noise elimination and the fringe details preservation, especially with abrupt fringe change, e.g., the edge of urban structures. However, tensor decomposition based on batch processing cannot deal with few newly acquired interferograms filtering directly. The filtering of dynamic InSAR tensor is the inevitable challenge when processing InSAR stack data, where dynamic InSAR tensor denotes the size of InSAR tensor increases continuously due to the acquisition of new interferograms. Therefore, based on the online CANDECAMP/PARAFAC (CP) decomposition, we propose an online filter to fuse data and process the dynamic InSAR tensor, named OLCP-InSAR, which performs well especially for the urban area. In this method, CP rank is utilized to measure the tensor sparsity, which can maintain the structural features of the InSAR tensor. Additionally, CP rank estimation is applied as an important step to improve the robustness of Online CP decomposition - InSAR(OLCP-InSAR). Importing CP rank and outlier’s position as prior information, the filter fuses the noisy interferograms and decomposes the InSAR tensor to acquire the low rank information, i.e., filtered result. Moreover, this method can not only operate on tensor model, but also efficiently filter the new acquired interferogram as matrix model with the assistance of chosen low rank information. Compared with other tensor-based filters, e.g., high order robust principal component analysis (HoRPCA) and Kronecker-basis-representation multi-pass SAR interferometry (KBR-InSAR), and the widespread traditional filters operating on a single interferometric pair, e.g., Goldstein, non-local synthetic aperture radar (NL-SAR), non-local InSAR (NL-InSAR), and InSAR nonlocal block-matching 3-D (InSAR-BM3D), the effectiveness and robustness of OLCP-InSAR are proved in simulated and real InSAR stack data. Especially, OLCP-InSAR can maintain the fringe details at the regular building top with high noise intensity and high outlier ratio.