The key importance of radar remote sensing for civil applications has been recognized for decades, and enormous scientific and technical developments have been carried out to further improve SAR ...sensors and SAR data processing.<false,>In recent years, SAR satellite constellations, consisting of two or more satellites, are becoming the “new normal” in SAR remote sensing. The present availability of SAR sensor constellations, such as Cosmo SkyMed, TerraSAR-X/TanDEM-X, and the new Copernicus sensors Sentinel-1A and 1B, supply a continuous stream of imagery with a unique short revisit cycle of only six days. Together with many more operational and planned SAR satellite systems, such as Geo-Fen 3 or NASA ISRO SAR (NISAR), this unprecedented amount of high-quality SAR data is suitable for a variety of applications, provided proper data processing methodology are applied.<false,>In "Advances in SAR: Sensors, Methodologies, and Applications" advancements in the field of hardware, software, and applications are presented, covering a wide range of topics.<false,>
In this paper we present the activities performed at the Microwaves and Radar Institute of the German Aerospace Center (DLR) to derive global forest/non-forest classification mosaics from ...interferometric synthetic aperture radar (InSAR) data acquired by the TanDEM-X mission. The data have been collected between 2011 and 2016 in bistatic stripmap single polarization (HH) mode, with the main goal of generating a consistent, timely, and highly accurate 3D representation of the global terrain’s surface (digital elevation model, DEM). The global data set of quicklook images, which represent a spatially averaged version of the original full resolution data at a ground independent pixel spacing of 50m×50m, was used as input, in order to limit the computational burden. For classification purposes, several observables, systematically provided by the TanDEM-X system, can be exploited, such as the calibrated amplitude, the digital elevation model (DEM), and the interferometric coherence. Among the several factors contributing to a coherence degradation in InSAR data, the so-called volume correlation factor quantifies the coherence loss due to volume scattering phenomena, which typically occur in presence of vegetation. This quantity is directly derived from the interferometric coherence and used as main indicator for the identification of vegetated areas. For this purpose, a fuzzy multi-clustering classification approach, which takes into account the geometry and acquisition configuration, is applied to each acquired scene separately. A certain variability of the interferometric coherence at X band was observed among different forest types, mainly due to changes in forest structure, density, and tree height, which led to an adjustment of the algorithm settings depending on the considered type of forest. The identification of additional information layers, such as urban settlements or water areas, is also discussed, and the procedure for mosaicking of overlapping acquisitions (two at global scale, up to ten over mountainous terrain, forests, and desert regions) to improve the classification accuracy is detailed. The resulting global forest/non-forest map was validated using external reference information as well as with other existing classification maps and an overall agreement was observed that often exceeds 90%. Finally, examples for high-resolution (at 12m×12m) forest maps and potentials for deforestation monitoring over selected regions are presented as well, demonstrating the unique capabilities offered by the TanDEM-X bistatic system for a broad range of geoinformation services and scientific applications. The global TanDEM-X forest/non-forest map presented in this paper will be made available to the scientific community for free download and usage.
•A method to identify forested areas from TanDEM-X interferometric data is proposed.•A global forest/non-forest map at a resolution of 50m is derived.•Potentials for high-resolution (12m) forest mapping and monitoring are presented.
Atmospheric effects represent one of the major error sources of repeat-pass Interferometric Synthetic Aperture Radar (InSAR), and could mask actual displacements due to tectonic or volcanic ...deformation. The tropospheric delays vary both vertically and laterally and can be considered as the sum of (i) a vertically stratified component highly correlated with topography and (ii) a turbulent component resulting from turbulent processes in the troposphere varying both in space and time. In this paper, we outline a framework to routinely use pointwise GPS data to reduce tropospheric effects on satellite radar measurements. An Iterative Tropospheric Decomposition (ITD) model is used and further developed to separate tropospheric stratified and turbulent signals and then generate high-resolution correction maps for SAR interferograms. Cross validation is employed to assess the performance of the ITD model and act as an indicator to users of when and where correction is feasible. Tests were carried out to assess the impact of GPS station spacing on the ITD model InSAR correction performance, which provides insights into the trade-off between station spacing and the achievable accuracy. The application of this framework to Sentinel-1A interferograms over the Southern California (USA) and Southern England (UK) regions shows approximately 45–78% of noise reduction even with a sparse (~50–80km station spacing) GPS network and/or with strong and non-random tropospheric turbulence. This is about a 50% greater improvement than previous methods. It is believed that this framework could lead to a generic InSAR atmospheric correction model while incorporating continuous and global tropospheric delay datasets, e.g. numerical weather models.
•A framework is proposed to routinely use GPS for InSAR atmospheric correction.•45–79% improvements of InSAR displacements with both sparse and dense GPS networks.•Performance indicators to inform model usability for better quality control.•Impact of station spacing on the model performance is evaluated.
Previous studies have shown that the decrease of temporal interferometric synthetic aperture radar (InSAR) coherence could be exploited to detect the appearance of floodwater in urban areas. However, ...as of today, approaches based on this principle only make use of single co-polarization images for identifying the presence of floodwater in the double-bounce feature. In this study, we take advantage of both co- and cross-polarization images to detect significant decreases of the multitemporal InSAR coherence in order to enhance the mapping of floodwater in urban areas. We consider that not only double-bounce scattering, but also multiple-bounce may occur in urban areas depending on how the building facades are oriented with respect to the synthetic aperture radar (SAR) sensor's line of sight. The Sentinel-1 (S-1) mission is particularly well suited for applying and testing this kind of approach due to the systematic availability of dual-polarization data. Using as a test case, the widespread flooding in the city of Houston, USA, caused by Hurricane Harvey in 2017, we demonstrate that the proposed methodology leads to an increase of the accuracy of the urban flood maps from 75.2% when only using the VV polarization, to 82.9% when using the dual polarization information.
This paper presents scattering power decomposition images of fully polarimetric synthetic aperture radar (SAR) data for disaster monitoring. Utilization of fully polarimetric data can derive full ...color images with red-green-blue color coding, red for the double-bounce power, green for the volume scattering power, and blue for the surface scattering power, for which each color brightness corresponds to the magnitude. Since disaster events cause the changes of each scattering power, it becomes straightforward for everyone to recognize the changes of the color in the polarimetric decomposed images provided time series data sets are made available. After applying the four-component scattering power decomposition to fully polarimetric image data sets acquired with the Advanced Land Observing Satellite (ALOS) Phased-Array-type L-band SAR (PALSAR), several images are presented for natural disaster monitoring of volcanic activity, snow accumulation, landslides, and tsunami effects caused by great earthquakes. It is seen in the polarimetric decomposition images that the surface scattering power becomes predominant in most disaster areas compared to those in normal situations.
Phase unwrapping (PU) is among the most critical tasks in synthetic aperture radar (SAR) interferometry (InSAR). Due to the presence of noise, the interferogram usually presents phase ...inconsistencies, also called residues, which imply a nonunivocal solution. This work investigates the PU problem from a semantic segmentation perspective by exploiting convolutional neural network (CNN) models. In particular, by exploiting a popular deep-learning architecture, we introduce the interferometric coherence as an input feature and analyze the performance increase against classical methods. For the network training, we generate a variegated data set by introducing a controlled number of phase residues, and considering both synthetic and real InSAR data. Eventually, we compare the proposed method to state-of-the-art algorithms on synthetic and real InSAR data taken from the TanDEM-X mission, obtaining encouraging results.
Synthetic aperture radar (SAR) images are often contaminated by a multiplicative noise known as speckle. Speckle makes the processing and interpretation of SAR images difficult. We propose a ...deep-learning-based approach called, image despeckling convolutional neural network (ID-CNN), for automatically removing speckle from the input noisy images. In particular, ID-CNN uses a set of convolutional layers along with batch normalization and rectified linear unit activation function and a componentwise division residual layer to estimate speckle and it is trained in an end-to-end fashion using a combination of Euclidean loss and total variation loss. Extensive experiments on synthetic and real SAR images show that the proposed method achieves significant improvements over the state-of-the-art speckle reduction methods.
This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent ...interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports.
Measurements of present‐day surface deformation are essential for the assessment of long‐term seismic hazard. The European Space Agency's Sentinel‐1 satellites enable global, high‐resolution ...observation of crustal motion from Interferometric Synthetic Aperture Radar (InSAR). We have developed automated InSAR processing systems that exploit the first ~5 years of Sentinel‐1 data to measure surface motions for the ~800,000‐km2 Anatolian region. Our new 3‐D velocity and strain rate fields illuminate deformation patterns dominated by westward motion of Anatolia relative to Eurasia, localized strain accumulation along the North and East Anatolian Faults, and rapid vertical signals associated with anthropogenic activities and to a lesser extent extension across the grabens of western Anatolia. We show that automatically processed Sentinel‐1 InSAR data can characterize details of the velocity and strain rate fields with high resolution and accuracy over large regions. These results are important for assessing the relationship between strain accumulation and release in earthquakes.
Plain Language Summary
Satellite‐based measurements of small rates of motion of the Earth's surface made at high spatial resolutions and over large areas are important for many geophysical applications including improving earthquake hazard models. We take advantage of recent advances in geodetic techniques in order to measure surface velocities and tectonic strain accumulation across the Anatolia region, including the highly seismogenic and often deadly North Anatolian Fault. We show that by combining Sentinel‐1 Interferometric Synthetic Aperture Radar (InSAR) data with Global Navigation Satellite System (GNSS) measurements we can enhance our view of surface deformation associated with active tectonics, the earthquake cycle, and anthropogenic processes.
Key Points
We produce high‐resolution horizontal and vertical velocity and strain rate fields for Anatolia from Sentinel‐1 InSAR and GNSS observations
Velocity gradients indicate shear strain accumulation along the North and East Anatolian Faults and extension across western Anatolia
InSAR data are critical for capturing high‐resolution details of the velocity and strain rate fields
Among the fastest sinking cities globally, the metropolitan area of Mexico City is the target of an unprecedented satellite investigation based on over 300 Sentinel-1 Synthetic Aperture Radar (SAR) ...Interferometric Wide swath mode scenes acquired in 2014–2020. Two-pass differential Interferometric SAR (InSAR) and the parallelized Small BAseline Subset (SBAS) repeat-pass InSAR approach provide a complete account of spatial patterns, long-term trend and present-day settlement rates affecting the city. The 3D deformation field reveals that foremost is the role of the vertical velocity VU, with peaks of −38.7 cm/year in Nezahualcóyotl, −32.0 cm/year in Gustavo A. Madero and Venustiano Carranza, and −39.1 cm/year in Iztapalapa. Settlement at the metropolitan Cathedral in Cuauhtémoc is ongoing at up to −8.8 cm/year, consistently with the last six decades. Volcanic edifices mark ground stability “islets” inside the main subsidence bowls. East-west rates are limited, except for some horizontal strain (up to ±5 cm/year) within the subsidence bowls. Comparison with surface geology and geotechnical zoning confirms that aquitard compaction is the predominant process. The power relationship between VU cm/year and the thickness of lacustrine clay deposits HC m is: VU=176∗HC1.8. The 2019–2020 deformation scenario shows that subsidence still involves most of Nezahualcóyotl, with −3.0 cm/month VU. A well-defined long-term deformation behaviour comes out from 2014–2019 InSAR time series and comparison with 2008–2020 GPS data. RMSE of 0.9 cm is found at MMX1 station deployed within the lacustrine unit, and 0.6 cm at TNGF station onto hard volcanic rocks. The sharpest subsidence gradients and angular distortions β in 2017–2019 (up to over 1/400, i.e. 0.14°) – thus the greatest vulnerability to surface faulting and cracking as a consequence of large tensile stress in the soil caused by differential settlement – are found at the foothills of Sierra de Santa Catarina, Peñón del Marqués, Cerro Chimalhuachi, Peñón de los Baños and Sierra de Chichinautzin, where the transition unit is narrower (or absent). Faults and cracks develop where β > 1/3000, i.e. 0.03%, in 2017–2019. The observed width of the influence zone (i.e. damage band), where β is still significant to induce damage, is 250 m. Differential settlement and surface faulting could compromise the serviceability of housing and utility infrastructure. It is estimated that over 457,000 properties and 1.5 million inhabitants of the Valley of Mexico Metropolitan Area (ZMVM) are in zones at high to very high surface faulting risk, mainly in Iztapalapa, Tláhuac, Chimalhuacán and Chalco. Increased flood exposure due to formation of topographic depressions involves over 751,000 properties and ~2.7 million inhabitants of the ZMVM, mainly in Nezahualcóyotl, Tláhuac, Venustiano Carranza, Iztapalapa, Gustavo A. Madero and Ecatepec de Morelos. These municipalities are often hit by catastrophic floods.
•Over 300 Sentinel-1 SAR scenes are used to monitor land subsidence in Mexico City.•InSAR reveals vertical displacement rates as high as −39 cm/year in 2014–2020.•Urban infrastructure is vulnerable to cracking, as angular distortions reach 1/400.•1.5 million inhabitants live in zones at high to very high risk of surface faulting.•Increased flood exposure due to subsidence involves over 751,000 properties.
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