InSAR interferogram detail-compensating filtering method based on the stationary wavelet transform

With the rapid development of modern Interferometric Synthetic Aperture Radar (InSAR) missions, SAR images with wider geographic coverage can be used to monitor ground deformation from local to continental scale. In such a large-scale application scenario, the ocean tide loading displacement introduces a long-wavelength error, which increases with distance, from millimetre to decimetre-level in InSAR interferograms over coastal areas. Despite great efforts being made to investigate the impacts of OTL on InSAR, these works are limited to individual interferograms and seldomly used in time-series analysis. In this study, we fully explore the OTL effects on Sentinel-1 InSAR time-series data along the western coast of the UK. We adopted wavelet analysis to indicate that the OTL displacement creates periodic signals with major cycles of 15 and 64 days under the 6- and 12-day Sentinel-1 sampling rates, respectively. These periodic signals are responsible for high noise magnitude of time-series displacement up to ~1cm and ~1 cm/yr bias on estimated velocities. An example is shown in Figure 1, where large velocity bias (a1-a3) and time-series standard deviation (b1-b3) can be seen along the western coastline of non-OTL corrected deformation fields, which are considerably eliminated after OTL correction. Our further validation against GNSS observations reveals that OTL correction improves the accuracy of large-scale InSAR time-series analysis by 25%.

Ocean tide loading correction for InSAR measurements: Comparison of different ocean tide models

Sentinel-1 observation of the 2017 Sangsefid earthquake, northeastern Iran: Rupture of a blind reserve-slip fault near the Eastern Kopeh Dagh

Interferometric Synthetic aperture radar (InSAR) aided inertial navigation system (INS) is developed to introduce terrain elevation into registration, which gives out accumulated error of INS. Unwrapped InSAR interferogram plays a key role in previous InSAR/INS frameworks, while airborne and side-looking assumptions are usually made. However, phase unwrapping is time-consuming and high squint geometry with nonlinear trajectory is common for applications with InSAR/INS. In this article, a novel wrapped InSAR interferogram based positioning method for high-speed maneuverable platform is proposed. A novel back-projection based InSAR (BP-InSAR) signal model for slightly curved diving trajectory with high squint angle is firstly presented. Then process which generates phase gradient template from given DEM is presented, considering both the estimated and measured trajectories. Error analysis is also made to discuss the performance of the proposed method in single dual-channel observation. Finally, a sequential maximum likelihood estimator based on error analysis is implemented to deal with the remained uncertainty in single observation. Numerical experiments verify the proposed method and fully demonstrate the performance of it with curved trajectory and random errors in both position and attitude.

Excessive groundwater exploitation in Sirjan plain, located in south of Iran, has caused severe land subsidence. The main purpose of this paper is to determine the hydrogeological parameters of the aquifer system in Sirjan plain. To this end, the groundwater flow was simulated in both steady and transient states using the United States Geological Survey Modular Three-Dimensional Finite-Difference Groundwater Flow Model, MODFLOW. The hydraulic conductivity parameter of the aquifer system was first estimated and calibrated in the steady state by simulating the hydraulic head at 63 piezometric wells. The specific yield and recharge parameters were then calibrated in the transient state by simulating the drawdown within a period of interest, i.e. 23 September 2004 to 22 September 2005. The main innovation of this paper, however, is to use the potential of Synthetic Aperture Radar (SAR) interferometry time-series analysis in the last step of the proposed algorithm. Small Baseline Subset (SBAS) time-series analysis was applied using Envisat Advanced SAR (ASAR) images acquired from 1 June 2004 to 28 September 2010. The merit of SBAS time series is that it enables us to calculate the amount of subsidence in each period of interest while minimizing the temporal decorrelation effect. In the last step, the SBAS time-series analysis results were obtained to extract the accumulated subsidence from 19 October 2004 to 30 August 2005. The aquifer compaction due to this period was calculated in the Interbed Storage Package (IBS1) for MODFLOW in order to calibrate storage coefficient in a trial-and-error manner so as the calculated subsidence and the observed one by SAR interferometry be in a good agreement. The Root Mean Square Error (RMSE) between the simulated subsidence and the observed one is calculated as 0.019 m which is an indication of the high performance of modelling.

Coal mine subsidence hazard can be effectively evaluated by geographic information system (GIS) analysis if sufficient data is provided. It is, however, difficult to obtain ground-based data, especially in remote and less populated mining areas. In this study, we construct and validate a coal mine subsidence hazard map in Taebaek, Korea, by integration of space-borne L-band synthetic aperture radar (SAR) measurements and a fuzzy-based subsidence model. There is an approximately 15-year time interval between the radar measurements used for the subsidence hazard model and those used to validate the subsidence. A subsidence hazard map was constructed using Japanese Earth Resources Satellite (JERS-1) SAR data from the early 1990s and the subsidence model. For the coal mine subsidence hazard mapping, a certainty factor analysis was used to estimate the relative weights of four control factors influencing coal mine subsidence, and the relative weight of each factor was then integrated to produce a subsidence hazard index by a fuzzy combination operator. The predicted hazard areas were then investigated and validated by comparison with subsidence occurrences observed by Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR) interferometry in 2007–2008. The results showed a good agreement between the predicted locations vulnerable to subsidence and the actual subsidence occurrences with a prediction accuracy of about 73% and a root mean square error of about 0.35. These results demonstrate that a map produced by integration of a subsidence model and SAR interferometry can be used to predict and monitor coal mine subsidence hazards, especially in remote regions.

In Antarctica, SAR interferometry has largely been used in coastal glacial areas, while in rare cases this method has been used on the Antarctic plateau. In this paper, the authors present a digital elevation and ice flow map based on SAR interferometry for an area encompassing Talos Dome (TD) and the Frontier Mountain (FM) meteorite site in North Victoria Land/Antarctica. A digital elevation model (DEM) was calculated using a double SAR interferometry method. The DEM of the region was calculated by extracting approximately 100 control points from the Reference Elevation Model of Antarctica (REMA). The two DEMs differ slightly in some areas, probably due to the penetration of the SAR-C band signal into the cold firn. The largest differences are found in the western area of TD, where the radar penetration is more pronounced and fits well with the layer structures calculated by the georadar and the snow accumulation observations. By differentiating a 70-day interferogram with the calculated DEM, a displacement interferogram was calculated that represents the ice dynamics. The resulting ice flow pattern clearly shows the catchment areas of the Priestley and Rennick Glaciers as well as the ice flow from the west towards Wilkes Basin. The ice velocity field was analysed in the area of FM. This area has become well known due to the search for meteorites. The velocity field in combination with the calculated DEM confirms the generally accepted theories about the accumulation of meteorites over the Antarctic Plateau.

Interferometric Synthetic Aperture Radar (InSAR) is a type of active remote sensing whereby a satellite transmits electromagnetic radiation (microwaves) at the ground and measures the differential phase of the reflected signal over multiple images (or multiple antennas on a single satellite). InSAR has the potential to provide centimeter and even millimeter-scale measurements of displacement over time, but is sensitive to vegetation, topography, and atmospheric effects. We consider herein, the application of InSAR at two known landslides on the Enbridge pipeline system, and discuss the strengths, weaknesses, values, and limitations of its application in the Geohazard Management of landslides impacting pipeline ROW’s. We compare information provided at each site by InSAR (both L-band and X-band) to data derived by mapping using Light Detection and Ranging (LiDAR) or air photographs, to differential LiDAR techniques, and to data derived from subsurface measurements (slope inclinometers). In doing so we find that L-Band data can be an effective tool to establish the extent or footprint of movement (or lack of movement) at known landslide locations, extending the interpretive power of a specialist and the understanding of event magnitude, and potentially affecting the mitigation options. Further, L-Band InSAR can be used in a supporting role to pre-screen areas for active landslides along the right of way (ROW), however, data gaps, a lack of explanatory power, and considerable noise in the results mean that a user step that further considers the terrain, other sources of data, and the identified magnitude, is essential. X-Band InSAR appeared impractical for ROW monitoring where vegetation prevented coherence between images, however, X-Band InSAR was able to detect small displacements at above ground infrastructures.

Synthetic Aperture Radar (SAR) interferometry is an active remote sensing technology that uses microwaves to characterize the earth's surface. SAR interferometry allows to measure the 3D profile of the earth's surface, recover surface topography, and determine topographic displacements over time. The microwave SAR signal is usually highly distorted. Distortions can be caused by, for example, atmospheric disturbances and various characteristics of earth's surface scatterers reflectance. Compensation for these distortions is performed by filtering the phase and evaluating the degree of coherence of the original images. This is an important step to improve the accuracy of the subsequent pphase-unwrapping operation. In this paper, we investigate the use of U-net neural networks for preprocessing the SAR interferogram at various parameters of the distortion of the SAR signal. Two neural networks filter the SAR interferogram and determine the degree of coherence, respectively.

Multibaseline (MB) phase unwrapping (PU) is a key processing technique in MB interferometric synthetic aperture radar (InSAR). As one of the most popular methods, the cluster analysis (CA)-based MBPU method often suffers from the problem of low noise robustness. Therefore, the block-matching and 3D filtering (BM3D) algorithm, one of the most effective filtering methods for image denoising, is applied to improve the performance of the method. Five different filtering strategies for applying BM3D are proposed in the paper: interferogram filtering (IFF), intercept filtering (ICF), cluster number filtering (CNF), unwrapped phase filtering (UPF), and simultaneous filtering (STF). In particular, while keeping the general structure of BM3D, four different similarity measures are defined for interferograms, intercepts, clusters, and unwrapped phases to accommodate the special characteristics of different filtering objects. Experiments on synthesized and real InSAR datasets prove their feasibility and effectiveness, and the experiment results show that (1) the PU accuracy and robustness of the CA-based MBPU method can be greatly improved by adding BM3D denoising; (2) simultaneous filtering of interferograms, intercepts, cluster numbers, and unwrapped phases works best, but with the worst time complexity; (3) when filtering is performed for only one object of the CA-based MBPU method, the filtering effect of CNF and UPF is better than that of IFF and ICF; and (4), considering the three indicators of PUSR, NRSE, and time consumption, CNF and UPF should be the best choices.

A comprehensive understanding of the spatial distribution of urban settlements is significant to a series of research topics related to environmental and biological changes caused by the urbanization process. Among various technologies, Synthetic Aperture Radar (SAR) has been successfully applied in urban settlement mapping in the past two decades. Although much effort with varying degrees of success has been made in previous studies, the research work is still ongoing, and three challenges should be highlighted. First, the effect of de-speckling is usually underestimated, to the extent that the improvement of the SAR image quality is totally ignored in some studies. Second, a method that combines full Interferometric SAR (InSAR) information is as yet missing. Third, training samples are generally required to process SAR images to extract urban settlements, which is time-consuming and labor-intensive, or even impractical when classifying satellite data at the regional/global scale. To address these issues, this paper presents an automatic method for urban settlement mapping trained by multi-temporal InSAR using social media. To improve the detection performance and reduce false alarm ratio, intensity and coherence are first accurately estimated without loss of image resolution by homogeneous pixel selection and robust estimators. The homogeneous pixels will be also applied to measure urban characteristics from the geometrical prospective. After that, training samples are automatically generated from social media based on the fact that cities and urban areas are nowadays full of individual geo-referenced data such as social network data Finally, these multiple information sources will be fused to extract urban areas based on an improved one class classifier. Experimental results show that the proposed method is effective in extracting urban areas with good accuracy. This study provides a new de-speckling means to process multi-temporal InSAR and sheds new light on the applications of social media in the field of remote sensing.

Synthetic aperture radar (SAR) has been an unparalleled tool in cloudy and rainy regions as it allows observations throughout the year because of its all-weather, all-day operation capability. In this paper, the influence of Wenchuan Earthquake on the Sichuan Giant Panda habitats was evaluated for the first time using SAR interferometry and combining data from C-band Envisat ASAR and L-band ALOS PALSAR data. Coherence analysis based on the zero-point shifting indicated that the deforestation process was significant, particularly in habitats along the Min River approaching the epicenter after the natural disaster, and as interpreted by the vegetation deterioration from landslides, avalanches and debris flows. Experiments demonstrated that C-band Envisat ASAR data were sensitive to vegetation, resulting in an underestimation of deforestation; in contrast, L-band PALSAR data were capable of evaluating the deforestation process owing to a better penetration and the significant coherence gain on damaged forest areas. The percentage of damaged forest estimated by PALSAR decreased from 20.66% to 17.34% during 2009–2010, implying an approximate 3% recovery rate of forests in the earthquake impacted areas. This study proves that long-wavelength SAR interferometry is promising for rapid assessment of disaster-induced deforestation, particularly in regions where the optical acquisition is constrained.

Online Material: Additional figures and tables. On 20 May 2012, an M L 5.9 earthquake (Table 1) occurred near the town of Finale Emilia, in the central Po plain, Northern Italy (Fig. 1a). The mainshock caused seven casualties and the collapse of several historical buildings and industrial sheds. The earthquake sequence continued with diminishing aftershock magnitudes until 29 May, when an M L 5.8 earthquake occurred near the town of Mirandola, ∼12 km west‐southwest of the mainshock (Scognamiglio et al. , 2012). This second mainshock started a new aftershock sequence in this area, and increased structural damage and collapses, causing 19 more casualties and increasing to 15,000 the number of evacuees. View this table: Table 1 Largest Earthquakes ( M L>5) of the 2012 Emilia Seismic Sequence (http://iside.rm.ingv.it) Figure 1. (a) Geostructural setting of the Emilia 2012 seismic sequence area showing the vertically projected top traces of the main thrust fault planes, and the anticlinal axes, modified after (Bigi et al. , 1983; Boccaletti et al. , 2010; Picotti and Pazzaglia, 2008); white dots, aftershock epicenters. Large instrumental and historical earthquakes are also shown (http://iside.rm.ingv.it, last accessed April 2013); Rovida et al. , 2011); yellow rectangles, footprint of the COSMO‐SkyMed (CSK) and Radarsat satellite image pairs. The focal mechanisms of the two main events (from Malagnini et al. , 2012) are reported in the map. (b) Geological profiles interpreted from seismic reflection data after (Boccaletti et al. , 2010) corresponding to traces A and B in (a). Shortly after the first mainshock, the Department of Civil Protection (DPC) activated the Italian Space Agency (ASI), which provided post‐seismic Synthetic Aperture Radar (SAR) Interferometry data coverage with all four Constellation of Small Satellites for the Mediterranean Basin Observation (COSMO‐SkyMed) SAR satellites. Within the next two weeks, several SAR Interferometry (InSAR) image pairs were processed by the Istituto Nazionale di Geofisica e Vulcanologia–Space‐based Monitoring System …

Over the past three decades, synthetic aperture radar (SAR) and SAR interferometry (InSAR) have revolutionized Earth observation, allowing for geophysical monitoring of Earth's surface deformation with centimeter‐to‐millimeter precision. InSAR time series are successive maps of ground motion used for natural hazards assessment with the monitoring of processes slowly deforming the Earth's surface, such as landslides, subsidence, or slow tectonic motion. In particular, the detection of transient displacements of millimeter amplitude is of utmost importance to better capture the dynamics of active tectonic faults, for a better understanding of the underlying physics governing the earthquake cycle. However, despite tremendous advances over the last two decades, noise affecting the data, primarily due to atmospheric delay, still challenges the detection of small deformation signals in InSAR time series. Consequently, an unknown amount of slow tectonic deformation signals remains concealed within the noise, raising the need for novel methodologies for signal extraction. We introduce InSARDenoiser, a spatiotemporal attentive convolutional U‐Net, designed to extract small‐scale deformation in noisy InSAR time series. The combination of a deep spatial U‐Net with a spatiotemporal Transformer tracks transient deformation in space and follows its temporal signature through time. We develop a mathematical formulation to generate realistic multi‐scale atmospheric noise and build a synthetic data set. When applied to a real InSAR time series along the North Anatolian Fault, the method effectively extracts millimeter‐scale slow deformation. The extracted deformation aligns with independent creepmeter measurements, thereby validating our method and opening the possibility of its application to diverse tectonic settings globally.

In this paper, the study on the digital elevation model (DEM) reconstruction using Gaofen-3 satellite repeat-pass synthetic aperture radar (SAR) interferometry is presented. The coherence of the single-look complex (SLC) images and residuals of interferogram provide a powerful guarantee for interferometric processing for Gaofen-3. Baseline estimation is the key factor to generate accurate DEM with SAR interferometry. This paper uses the coarse orbit data and the precise orbit data of Gaofen-3 to carry on the baseline estimation and uses the prior coarse DEM information to refine the baseline estimation. Then, the topographic height is reconstructed with more precise spatial baseline. An experiment is conducted to verify the conclusion, and some mountain and flatten region are chosen as the study area. The results show that the DEM errors based on the prior DEM method are rapidly converged compared with the ones based on the orbit data method. Therefore, benefiting from the precise orbit data of the Gaofen-3 satellite, the accuracy of the baseline estimation can be improved further by using the method based on prior DEM information; thus, higher accuracy of DEM can be achieved so that Gaofen-3 repeat-pass data have some good characteristics, such as high imaging quality, stable spatial baseline, and high correlation, which has a good repeat-pass SAR interferometry application prospect.