Single-look Complex Synthetic Aperture Radar Research Articles

Employment of Free Packages for MT-InSAR Approaches to Verify the Subsidence Event over Maceió City, Brazil

Persistent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR) technique employs a Multi-Temporal InSAR (MT-InSAR) approach to accurately measure subsidence. This technique, a type of Differential Interferometry (DInSAR), mitigates errors that traditional DInSAR techniques cannot, including temporal and geometric decorrelation, and phase unwrapping errors. In order to verify the subsidence process in the Pinheiro neighborhood of Maceio - Brazil following a 2018 earthquake, we tested free processing packages such as SNAP-StaMPS integration. Our investigation was conducted in two stages: first, using a stack of Sentinel-1A SLCSAR (Single Look Complex-SAR) images acquired before and after the earthquake, and second, using more recent images to determine if the subsidence process is ongoing. Results from the first stage identified the area affected by subsidence and the second stage confirmed the continued presence of subsidence events. From 2017 to 2018, the subsidence process exhibited the highest displacement amplitude of -32.3 mm/year, whereas, between 2021 and 2022, the amplitude decreased to -24.09 mm/year, indicating a deceleration in the subsidence process.

Extension and Evaluation of SSC for Removing Wideband RFI in SLC SAR Images

Synthetic aperture radar (SAR), as a wideband radar system, is easily contaminated by radio frequency interference (RFI), which affects the imaging quality of SAR. The subband spectral cancellation (SSC) method and its modifications utilize the SAR single-look complex (SLC) image to realize RFI extraction and mitigation by subtracting between sub-images, which are robust and efficient for engineering applications. In the past, the traditional SSC was often applied to narrowband interference (NBI) mitigation. However, when it was used for wideband interference (WBI) mitigation, it would cause the mitigated image to lose much of its useful information. In contrast, this paper proposes an improved SSC method based on successive cancellation and data accumulation (SSC-SCDA) for WBI mitigation. First, the fast Fourier transform (FFT) is used to characterize the SAR SLC data in the frequency domain, and the average range spectrum algorithm is used to detect whether there are interference components in the SAR SLC data. Then, according to the carrier frequency and bandwidth of the RFI in the frequency domain, the subbands are divided, and a cancellation strategy is formulated. Finally, based on the successive cancellation and data accumulation technology, WBIs can be removed by using only a small percentage of the clean subbands. Based on the simulated experiments, the interference mitigation performance of the proposed method is analyzed when the interference-to-signal bandwidth ratio (ISBR) varies from 20% to 80% under different signal-to-interference-to-noise ratios (SINR). The experimental results based on WBI-contaminated European Space Agency (ESA) Sentinel-1A SAR SLC data demonstrate the effectiveness of the proposed method in WBI mitigation.

Observation and Mitigation of Mutual RFI Between SAR Satellites: A Case Study Between Chinese GaoFen-3 and European Sentinel-1A

In synthetic aperture radar (SAR) data processing, radio frequency interference (RFI) has been recognized as a challenging issue, which can significantly degrade the image quality, including amplitude, phase, and geometry, etc. Most of the RFI sources are direct waves transmitted from the ground. Recently, a new form of RFI, namely the mutual RFI (MRFI), which is a kind of scatter-wave RFI originating from the nearby ground area simultaneously illuminated by different SAR satellites, has been reported. In this paper, the signatures of MRFI are characterized, and an example case study between Chinese GaoFen-3 (GF-3) and European Sentinel-1A (S-1A) is analyzed. In order to remove the artifacts caused by MRFI on SAR images, a novel RFI detector based on spectrum energy cancellation (SEC), which has the capability of detecting MRFI, is developed. Two methods, i.e., the traditional notch filtering method and an improved eigen-subspace projection (ESP) method proposed in this paper, are employed to mitigate MRFI. The former method has robust mitigation performance with a fast processing speed, whereas the latter has an improved mitigation accuracy as well as lower side-lobes for strong scatterers. The methods are designed for cases in which the MRFI has a different radar centre frequency than the useful signal, and some of the data is free from MRFI. The conditions required for the proper use of the approach are discussed. In contrast to the conventional mitigating methods performed on the raw data domain, the proposed approach begins with the focused single look complex (SLC) SAR data. The effectiveness of the proposed approach is demonstrated on several GF-3 SLC SAR images, which were contaminated by MRFI from the S-1A.

As If by Magic: Self-Supervised Training of Deep Despeckling Networks With MERLIN

Speckle fluctuations seriously limit the interpretability of synthetic aperture radar (SAR) images. Speckle reduction has thus been the subject of numerous works spanning at least four decades. Techniques based on deep neural networks have recently achieved a new level of performance in terms of SAR image restoration quality. Beyond the design of suitable network architectures or the selection of adequate loss functions, the construction of training sets is of uttermost importance. So far, most approaches have considered a supervised training strategy: the networks are trained to produce outputs as close as possible to speckle-free reference images. Speckle-free images are generally not available, which requires resorting to natural or optical images or the selection of stable areas in long time series to circumvent the lack of ground truth. Self-supervision, on the other hand, avoids the use of speckle-free images. We introduce a self-supervised strategy based on the separation of the real and imaginary parts of single-look complex SAR images, called MERLIN (coMplex sElf-supeRvised despeckLINg), and show that it offers a straightforward way to train all kinds of deep despeckling networks. Networks trained with MERLIN take into account the spatial correlations due to the SAR transfer function specific to a given sensor and imaging mode. By requiring only a single image, and possibly exploiting large archives, MERLIN opens the door to hassle-free as well as large-scale training of despeckling networks. The code of the trained models is made freely available at https://gitlab.telecom-paris.fr/RING/MERLIN.

Efficient SAR Azimuth Ambiguity Reduction in Coastal Waters Using a Simple Rotation Matrix: The Case Study of the Northern Coast of Jeju Island

Azimuth ambiguities, or ghosts on SAR images, represent one of the main obstacles for SAR applications involving coastal monitoring activities such as ship detection. While most previous methods based on azimuth antenna pattern and direct filtering are effective for azimuth ambiguity suppression, they may not be effective for fast cruising small ships. This paper proposes a unique approach for the reduction of azimuth ambiguities or ghosts in SAR single-look complex (SLC) images using a simple rotation matrix. It exploits the fact that the signal powers of azimuth ambiguities are concentrated on narrow bands, while those of vessels or other true ground targets are dispersed over broad bands. Through sub-aperture processing and simple axis rotation, it is possible to concentrate the dispersed energy of vessels onto a single axis while the ghost signal powers are dispersed onto three different axes. Then, the azimuth ambiguities can be easily suppressed by a simple calculation of weighted sum and difference, while preserving vessels. Applied results achieved by processing TerrSAR-X SLC images are provided and discussed. An optimum weight of 0.5 was determined by Receiver Operating Characteristic (ROC) analysis. Capabilities of ship detection from the test image were significantly improved by removing 93% of false alarms. Application results demonstrate its high performance of ghost suppression. This method can be employed as a pre-processing tool of SAR images for ship detection in coastal waters.

Deriving Digital Surface Models from Geocoded SAR Images and Back-Projection Tomography

Digital surface models (DSMs) are sets of elevation data of the Earth's surface, useful for applications such as urban studies and height estimation of buildings. They can be derived from a set of synthetic aperture radar (SAR) images acquired in an interferometric or tomographic configuration. Each image acquisition is usually focused in radar geometry. In this work, we present steps required to derive DSMs from SAR single-look complex (SLC) products focused in map geometry (geocoded). We modified existing tomographic reconstruction techniques to be able to operate with geocoded SLCs and extended methods to operate with 3-D geocoded SLCs. The performance analysis showed that methods using 3-D geocoded SLC products yielded DSMs with fewer outliers and retained more information of the illuminated area, with a cost of higher computational complexity. Compressive sensing methods using 2-D geocoded SLCs can be a good alternative due to their comparatively moderate computational complexity.

Acceleration Compensation for Estimation of Along-Track Velocity of Ground Moving Target from Single-Channel SAR SLC Data

Across-track acceleration is a major source of estimation error of along-track velocity in synthetic-aperture radar (SAR) ground moving-target indication (GMTI). This paper presents the theory and a method of compensating across-track acceleration to improve the accuracy of along-track velocity estimated from single-channel SAR single-look complex data. A unique feature of the proposed method is the utilisation of phase derivatives in the Doppler frequency domain, which is effective for azimuth-compressed signals. The performance of the method was evaluated through experimental data acquired by TerraSAR-X and speed-controlled and measured vehicles. The application results demonstrate a notable improvement in along-track velocity estimates. The amount of along-track velocity correction is particularly significant when a target has irregular motion with a low signal-to-clutter ratio. A discontinuous velocity jump rather than a constant acceleration was also observed and verified through comparison between actual data and simulations. By applying this method, the capability of single-channel SAR GMTI could be substantially improved in terms of accuracy of velocity, and moving direction. However, the method is effective only if the correlation between the actual Doppler phase derivatives and a model derived from the residual Doppler rate is sufficiently high. The proposed method will be applied to X-band SAR systems of KOMPSAT-5 and -6.

The Study of Ocean Wave and Wave Power Observations by Synthetic Aperture Radar in Nearshore Waters

Wan, Y.; Zheng, C.W.; Zhang, J.; Dai, Y.; Li, L.; Qu, X., and Zhang, X., 2020. The Study of Ocean Wave and Wave Power Observations by Synthetic Aperture Radar in Nearshore Waters. In: Zheng, C.W.; Wang, Q.; Zhan, C., and Yang, S.B. (eds.),Air-Sea Interaction and Coastal Environments of the Maritime Polar Silk Roads.Journal of Coastal Research, Special Issue No. 99, pp. 9-15. Coconut Creek (Florida), ISSN 0749-0208.Based on 2 RADARSAT-2 SAR (Synthetic Aperture Radar) SLC (Single Look Complex) data and the cross-spectrum method, the distributions of significant wave heights and mean wave periods of ocean wave were inverted, and the distributions of the wave power density, which can represent the wave power, were calculated. Some comparisons between the results of the SAR and those of the ECMWF (European Center for Medium-Range Weather Forecasts) dataset were carried out, and the observation ability for the distribution characteristics of wave and wave power by the SAR was studied in detail. The results show that compared with buoys, which are single-point observations, and wave models, which are simulated observations, the SAR is an ideal tool for extensive wave observations in nearshore waters. The advantage of SAR observations is that some fine structure and microcosmic changes in wave and wave power can be found, which helps us understand the internal structure and transformation patterns of wave better. Therefore, the SAR showed extremely extensive application prospects in the field of wave observations. The contributions in this paper will provide some references for researchers aiming for ocean wave detection by remote sensing and promote the application level of SAR in the detection of wave and wave power.

On the Capabilities of the Italian Airborne FMCW AXIS InSAR System

Airborne Synthetic Aperture Radar (SAR) systems are gaining increasing interest within the remote sensing community due to their operational flexibility and observation capabilities. Among these systems, those exploiting the Frequency-Modulated Continuous-Wave (FMCW) technology are compact, lightweight, and comparatively low cost. For these reasons, they are becoming very attractive, since they can be easily mounted onboard ever-smaller and highly flexible aerial platforms, like helicopters or unmanned aerial vehicles (UAVs). In this work, we present the imaging and topographic capabilities of a novel Italian airborne SAR system developed in the frame of cooperation between a public research institute (IREA-CNR) and a private company (Elettra Microwave S.r.l.). The system, which is named AXIS (standing for Airborne X-band Interferometric SAR), is based on FMCW technology and is equipped with a single-pass interferometric layout. In the work we first provide a description of the AXIS system. Then, we describe the acquisition campaign carried out in April 2018, just after the system completion. Finally, we perform an analysis of the radar data acquired during the campaign, by presenting a quantitative assessment of the quality of the SLC (Single Look Complex) SAR images and the interferometric products achievable through the system. The overall analysis aims at providing first reference values for future research and operational activities that will be conducted with this sensor.

The 2017 Noneruptive Unrest at the Caldera of Cerro Azul Volcano (Galápagos Islands) Revealed by InSAR Observations and Geodetic Modelling

An unrest event occurred at the Cerro Azul volcano, Galápagos Islands, South America, in March 2017, leading to significant surface deformation on the southern Isabela Island, without eruption or surface rupture. We collected single-look complex synthetic aperture radar (SAR) images sensed by the Sentinel-1A satellite, obtaining eight differential interferograms, of which four showed extensive surface displacement during the co-unrest period. Geodetic data indicated that the unrest continued from 18 March to 25 March, reaching a negative peak displacement of −32.9 cm in the caldera and a positive peak displacement of 41.8 cm on the south-east plain in the line-of-sight direction. A joint magma source deformation model, consisting of a Mogi source below the caldera and a sill source south-east of the caldera, was inverted by the Markov chain Monte Carlo method combined with the Metropolis–Hasting algorithm, acquiring the best fit with the four interferograms. The magma transport mechanism of the event was explained by magma overflowing from the compressive Mogi to the tensile sill source, resulting in the observed “∞”-shaped deformation fields. Additionally, we investigated previous events with eruption rifts and lava lakes in 1979, 1998, and 2008, and proposed a potential hazard of tectonic volcanic activity for further volcanic susceptibility research in the Cerro Azul area.

Automatic Ship Detection Using the Artificial Neural Network and Support Vector Machine from X-Band Sar Satellite Images

In this paper, an automatic ship detection method using the artificial neural network (ANN) and support vector machine (SVM) from X-band SAR satellite images is proposed. When using machine learning techniques, the most important points to consider are (i) defining the proper input neurons and (ii) selecting the correct training data. We focused on generating two optimal input data neurons that (i) strengthened ship targets and (ii) mitigated noise effects by image processing techniques, including median filtering, multi-looking, etc. The median filter and multi-look operations were used to reduce the background noise, and the median filter operation was also used to remove ships in an image in order to maximize the difference between the pixel values of ships and the sea. Through the root-mean-square difference calculation, most ship targets, even including small ships, were emphasized in the images. We tested the performance of the proposed method using X-band high-resolution SAR images including COSMO-SkyMed, KOMPSAT-5, and TerraSAR-X images. An intensity difference map and a texture difference map were extracted from the X-band SAR single-look complex (SLC) images, and then, the maps were used as input neurons for the ANN and SVM machine learning techniques. Finally, we created ship-probability maps through the machine learning techniques. To validate the ANN and SVM results, optimal threshold values were obtained by using the statistical approach and then used to identify ships from the ship-probability maps. Consequently, the level of recall achieved was greater than 90% in most cases. This means that the proposed method enables the detection of most ship targets from X-band SAR images with a reduced number of false detections from negative effects.

Adaptive Ship Detection for Single-Look Complex SAR Images Based on SVWIE-Noncircularity Decomposition.

In this paper, we present an adaptive ship detection method for single-look complex synthetic aperture radar (SAR) images. First, noncircularity is analyzed and adopted in ship detection task; besides, similarity variance weighted information entropy (SVWIE) is proposed for clutter reduction and target enhancement. According to the analysis of scattering of SVWIE and noncircularity, SVWIE-noncircularity (SN) decomposition is developed. Based on the decomposition, two components, the high-noncircularity SVWIE amplitude (h) and the low-noncircularity SVWIE amplitude (l), are obtained. We demonstrate that ships and clutter in SAR images are different for h detector and h detector can be effectively used for ship detection. Finally, to extract ships from the background, the generalized Gamma distribution (GD) is used to fit h statistics of clutter and the constant false alarm rate (CFAR) is utilized to choose an adaptive threshold. The performance of the proposed method is demonstrated on HH polarization of Alos-2 images. Experimental results show that the proposed method can accurately detect ships in complex background, i.e., ships are close to small islands or with strong noise.

Antenna Phase Center Calibration for Array InSAR System Based on Orthogonal Subspace

The array InSAR system obtains a three-dimensional image of an observed scene using a combination of pulse compression and synthetic and real aperture techniques. However, Antenna Phase Center (APC) errors can occur within a practical array InSAR system, which thus degrades the imaging quality in a height direction. The aim of this paper is to improve calibration problems occurring with APC errors. The effect of APC errors is analyzed, and a calibration method based on the orthogonal subspace principle is proposed that utilizes SAR Single Look Complex (SLC) to obtain the noise subspace through eigenvalue decomposition. The subspace orthogonal principle is then used to solve the APC positions of multiple channels simultaneously. In addition, a calibration scheme for the APC position is presented for application with an array InSAR system. The effectiveness of the proposed calibration method is verified using simulations and experimental results.

Implementation of RISAT-1 Hybrid Polarimetric Decomposition Techniques and Analysis Using Corner Reflector Data

With recent advances in polarimetry, Synthetic Aperture Radar (SAR) with Hybrid–polarity architecture, a demonstration of compact polarimetry enabled larger swath coverage, reduced PRF and SAR system complexity as compared to fully polarimetric systems. The first Hybrid Polarimetric Space-borne SAR in Earth Observation orbit, India’s Radar Imaging Satellite (RISAT-1) is a new-fangled gateway to remote sensing user community for land and oceanic applications. In response to a right-circular polarized transmitted signal, based on the derived stokes vectors, Stokes parameters are estimated to produce several useful quantitative measures for generating polarimetric decomposed image. m-delta, m-chi and m-alpha polarimetric decomposition methods along with suitable weighting functions in terms of three principal components are implemented which maps Stokes parameters to RGB image space for representing odd bounce, even bounce and volume scattering targets. Various RISAT-1 Hybrid Fine Resolution Stripmap Single-Look Complex SAR datasets acquired over deployed corner reflectors at calibration site, Shadnagar have been considered over which different hybrid polarimetric decomposition techniques are implemented using in-house developed software. Further analysis produced encouraging results with standard point targets like dihedral and trihedral corner reflectors against distributed targets in the same scene to demonstrate the scattering mechanisms as per their characteristics when interacted with a polarized signal were presented in this paper.

Recovery of Partially Corrupted SAR Images by Super-Resolution Based on Spectrum Extrapolation

The problem of chirped synthetic aperture radar (SAR) systems is the high vulnerability of the received information to electromagnetic (EM) attacks. This letter proposes a valid recovery solution for SAR single-look complex images that are corrupted by noncoherent EM noise covering only the higher frequency spectrum. The solution consists of, first, exporting the spectrum damages that occur in the native data and, second, focusing only the survived spectrum information at lower resolution. The recovery of the original image is done by super-resolution signal processing based on spectrum extrapolation and implemented by convex programming.

Use of Doppler Parameters for Ship Velocity Computation in SAR Images

This paper deals with the exploitation of Doppler centroid measurements for ship velocity estimation from focused single-look complex synthetic aperture radar (SAR) images. An algorithm is presented, which can be used as a discrimination tool to reduce the false alarm rate of standard adaptive threshold detectors and to complement the ship detection task with velocity estimation. The outputs are the indication of the presence of a moving target and the estimate of its slant range velocity. After a review of its theoretical background, algorithm features and performance are verified by application to TerraSAR-X data. The proposed method performs robust Doppler spectrum derivation for candidate ship targets and background pixels. The presented results show that the estimated radial velocity is in very good agreement (5% root-mean-square deviation) with that resulting from the azimuth offset method. Rejection of bright sea features and azimuth ambiguities is also demonstrated, taking advantage of the performed velocity analysis.

Use of Satellite SAR for Understanding Long-Term Human Occupation Dynamics in the Monsoonal Semi-Arid Plains of North Gujarat, India

This work explores the spatial distribution of monsoonal flooded areas using ENVISAT C-band Advanced Synthetic Aperture Radar (ASAR) in the semi-arid region of N. Gujarat, India. The amplitude component of SAR Single Look Complex (SLC) images has been used to estimate the extent of surface and near-surface water dynamics using the mean amplitude (MA) of monsoonal (July to September) and post-monsoonal (October to January) seasons. The integration of SAR-derived maps (seasonal flooding maps and seasonal MA change) with archaeological data has provided new insights to understand present-day landscape dynamics affecting archaeological preservation and visibility. Furthermore, preliminary results suggest a good correlation between Mid-Holocene settlement patterns and the distribution and extension of seasonal floodable areas within river basin areas, opening interesting inroads to study settlement distribution and resource availability in past socio-ecological systems in semi-arid areas.

Radargrammetry for Digital Elevation Model Generation Using Envisat Reprocessed Image and Simulation Image

The digital elevation model (DEM) is one of the most important sources used for Earth surface analysis because of its various applications and its usability in subsequent studies. A DEM can be generated with interferometric synthetic aperture radar (InSAR) and radargrammetry techniques from synthetic aperture radar (SAR) images with orbital separation between them as the baseline. The on-board radar imaging system records both phase and intensity information of the backscattered signals. Radargrammetry is based on the disparity between two intensity images which is less affected by temporal and atmospheric decorrelation while InSAR uses the phase differences between two images. However, SAR intensity images may have low spatial resolution compared to optical images that are used in the photogrammetric DEM generation and the original SAR image is degraded by speckle noise. This leads to low accuracy of radargrammetric DEMs. It is necessary to develop methods focusing on original SAR images to improve the accuracy of radargrammetric DEM generation. In this letter, we propose that the single look complex (SLC) SAR image reprocessing method can be utilized to improve the image quality of radargrammetric DEMs. Simulated noise-free SAR images are used to generate radargrammetric DEMs for the purpose of comparison. Three SLC SAR images are selected for image quality improvement in each track. Other images in the same track are matched and processed to one reference image. The SAR simulation images are generated based on the reference image in each track. Therefore, the products in the same track have the same meta-information. Each DEM product is compared with conventional radargrammetric DEMs. This study shows that DEMs generated from re-processed SAR images are more accurate than radargrammetric DEMs from single-pair SAR images. Also, the accuracy of radargrammetric DEM produced with the proposed method is close to DEMs generated with noise-free simulated SAR images.

Despeckling and Information Extraction From SLC SAR Images

This paper presents an information extraction and image enhancement technique using single-look complex (SLC) synthetic aperture radar data. The novelty of this method is the proposed complex-domain despeckling stage. Tikhonov-like optimization is used for minimizing the cost function, which consists of a Gauss-Markov random field (GMRF) prior. The GMRF model is used for texture modeling. The texture parameters of the GMRF are estimated using the evidence maximization framework. The experimental results showed that despeckled SLC images have well-preserved textural features, structures, and point scatterers. The phase of the reconstructed image is well preserved and provides good-quality interferograms of high-resolution spotlight images.

Effect of detection on spatial resolution in synthetic aperture radar imagery and mitigation through upsampling

The complex-valued image output from a synthetic aperture radar (SAR) processor possesses full spatial resolution defined by the sensor. Typically, this image is either power detected or magnitude detected before it is subjected to further analysis. This paper consists of the study of the effect of detection on spatial resolution in complex-valued SAR imagery and the mitigation of this effect through upsampling. Furthermore, an algorithm for upsampling focused SAR imagery is presented. The proposed algorithm is general and it is designed to account for deviations from zero-Doppler encountered in the Spotlight imaging mode. It is shown that power and magnitude detections, respectively, degrade the spatial resolution by factors of two and greater. To mitigate this effect, the complex-valued SAR image should be upsampled appropriately. The results are demonstrated on real-world single-look complex SAR imagery from Radarsat-2.