Modern Synthetic Aperture Radar Research Articles

An Advanced Non-Interrupted Internal Calibration Model Based on Azimuth Modulation and Waveform Diversity for SAR Systems

Internal calibration, designed for obtaining the gain and phase errors introduced by the fluctuations of the electronic instruments, is an essential operation to guarantee the imaging performance of modern synthetic aperture radar (SAR) systems. However, in most of the current internal calibration schemes, the internal calibration processes interrupt the normal SAR data acquisition, which results in a loss of azimuth samples and degradation of the SAR image quality. To this end, an advanced non-interrupted internal calibration model is proposed, performing the internal calibration during nominal data acquisitions. The calibration signals and echoes are orthogonal and are received simultaneously. Then, they can be separated by postprocessing via the waveform diversity concept. Two schemes based on the proposed model are demonstrated via theoretical analysis and simulation experiments. The proposed model can achieve a considerable signal-to-noise ratio (SNR) and reduce the influence of calibration signals on echoes, which have some potential applications for future SAR missions.

Compressed SAR Interferometry in the Big Data Era

Modern Synthetic Aperture Radar (SAR) missions provide an unprecedented massive interferometric SAR (InSAR) time series. The processing of the Big InSAR Data is challenging for long-term monitoring. Indeed, as most deformation phenomena develop slowly, a strategy of a processing scheme can be worked on reduced volume data sets. This paper introduces a novel ComSAR algorithm based on a compression technique for reducing computational efforts while maintaining the performance robustly. The algorithm divides the massive data into many mini-stacks and then compresses them. The compressed estimator is close to the theoretical Cramer–Rao lower bound under a realistic C-band Sentinel-1 decorrelation scenario. Both persistent and distributed scatterers (PSDS) are exploited in the ComSAR algorithm. The ComSAR performance is validated via simulation and application to Sentinel-1 data to map land subsidence of the salt mine Vauvert area, France. The proposed ComSAR yields consistently better performance when compared with the state-of-the-art PSDS technique. We make our PSDS and ComSAR algorithms as an open-source TomoSAR package. To make it more practical, we exploit other open-source projects so that people can apply our PSDS and ComSAR methods for an end-to-end processing chain. To our knowledge, TomoSAR is the first public domain tool available to jointly handle PS and DS targets.

Location of Synthetic Aperture Radar Imagery via Range History

Geolocation is one of the key synthetic aperture radar (SAR) image processing procedures in SAR-based remote sensing applications. The conventional SAR imagery geolocation model is based on the range-Doppler (RD) equation, which is derived from the Fresnel approximation of SAR. Currently, there is no other rigorous geolocation model for modern high-resolution SAR; however, this approximation may not be fully applicable for various modern SAR imaging modes and algorithms. This study aims to develop a novel SAR image geolocation model based on the SAR range history (RH) equation, which is derived from the basic SAR backscattered signal property. Moreover, a new geometric calibration model is presented for RH-based location. Experiments based on real spaceborne SAR data are presented as examples, and the location accuracy of RH and RD location models are compared. The results show that the location accuracies of the RH and RD models are consistent under the existing SAR processing framework, but those for the RH model demonstrate better performance in the azimuth direction. In theory, the proposed RH model can be used for SAR image products generated from complex modes and non-Fresnel-approximation-based imaging algorithms, making it suitable for future SAR systems.

Joint Sparsity for TomoSAR Imaging in Urban Areas Using Building POI and TerraSAR-X Staring Spotlight Data.

Synthetic aperture radar (SAR) tomography (TomoSAR) can obtain 3D imaging models of observed urban areas and can also discriminate different scatters in an azimuth–range pixel unit. Recently, compressive sensing (CS) has been applied to TomoSAR imaging with the use of very-high-resolution (VHR) SAR images delivered by modern SAR systems, such as TerraSAR-X and TanDEM-X. Compared with the traditional Fourier transform and spectrum estimation methods, using sparse information for TomoSAR imaging can obtain super-resolution power and robustness and is only minorly impacted by the sidelobe effect. However, due to the tight control of SAR satellite orbit, the number of acquisitions is usually too low to form a synthetic aperture in the elevation direction, and the baseline distribution of acquisitions is also uneven. In addition, artificial outliers may easily be generated in later TomoSAR processing, leading to a poor mapping product. Focusing on these problems, by synthesizing the opinions of various experts and scholarly works, this paper briefly reviews the research status of sparse TomoSAR imaging. Then, a joint sparse imaging algorithm, based on the building points of interest (POIs) and maximum likelihood estimation, is proposed to reduce the number of acquisitions required and reject the scatterer outliers. Moreover, we adopted the proposed novel workflow in the TerraSAR-X datasets in staring spotlight (ST) work mode. The experiments on simulation data and TerraSAR-X data stacks not only indicated the effectiveness of the proposed approach, but also proved the great potential of producing a high-precision dense point cloud from staring spotlight (ST) data.

Error Analysis for Digital Beamforming Synthetic Aperture Radars: A Comparison of Phased Array and Array-Fed Reflector Systems

Modern synthetic aperture radar (SAR) systems for Earth observation from space employ innovative hardware concepts. The key idea is to digitize the output of a multielement antenna almost immediately after the receiver and to dynamically process these data either onboard the radar satellite in real time or on the ground. This article addresses the performance of such digital beamforming (DBF) systems in the presence of phase and magnitude errors in the digital channels. For this, analytic expressions for the sensitivity and range ambiguity performance are derived. These equations are kept general so that they are valid for both planar array antennas and array-fed reflector antennas. It is an important objective of this article to compare these two antenna types to each other. A major conclusion from this analysis is that direct-radiating phased arrays are inherently more susceptible to random phase and magnitude errors compared with array-fed reflector antenna-based systems. This manifests itself in a more rapid degradation of the imaging performance with phased array antennas.

Wetland Surface Water Detection from Multipath SAR Images Using Gaussian Process-Based Temporal Interpolation

Wetlands provide society with a myriad of ecosystem services, such as water storage, food sources, and flood control. The ecosystem services provided by a wetland are largely dependent on its hydrological dynamics. Constant monitoring of the spatial extent of water surfaces and the duration of flooding of a wetland is necessary to understand the impact of drought on the ecosystem services a wetland provides. Synthetic aperture radar (SAR) has the potential to reveal wetland dynamics. Multitemporal SAR image analysis for wetland monitoring has been extensively studied based on the advances of modern SAR missions. Unfortunately, most previous studies utilized monopath SAR images, which result in limited success. Tracking changes in individual wetlands remains a challenging task because several environmental factors, such as wind-roughened water, degrade image quality. In general, the data acquisition frequency is an important factor in time series analysis. We propose a Gaussian process-based temporal interpolation (GPTI) method that enables the synergistic use of SAR images taken from multiple paths. The proposed model is applied to a series of Sentinel-1 images capturing wetlands in Okanogan County, Washington State. Our experimental analysis demonstrates that the multiple path analysis based on the proposed method can extract seasonal changes more accurately than a single path analysis.

Study of Systematic Bias in Measuring Surface Deformation With SAR Interferometry

This article investigates the presence of a new interferometric signal in multilooked synthetic aperture radar (SAR) interferograms that cannot be attributed to the atmospheric or Earth-surface topography changes. The observed signal is short-lived and decays with the temporal baseline; however, it is distinct from the stochastic noise attributed to temporal decorrelation. The presence of such a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fading signal</i> introduces a systematic phase component, particularly in short temporal baseline interferograms. If unattended, it biases the estimation of Earth surface deformation from SAR time series. Here, the contribution of the mentioned phase component is quantitatively assessed. The biasing impact on the deformation-signal retrieval is further evaluated. A quality measure is introduced to allow the prediction of the associated error with the fading signals. Moreover, a practical solution for the mitigation of this physical signal is discussed; special attention is paid to the efficient processing of Big Data from modern SAR missions such as Sentinel-1 and NISAR. Adopting the proposed solution, the deformation bias is shown to decrease significantly. Based on these analyses, we put forward our recommendations for efficient and accurate deformation-signal retrieval from large stacks of multilooked interferograms.

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Highly efficient design of SDRAM‐based CTM for real‐time SAR imaging system

The amount of radar's raw echo data is usually very large. At the same time, synthetic aperture radar (SAR) imaging system needs rapid transpose efficiency to improve the real-time performance of the system. Therefore, modern real-time SAR system requires high-speed and large-capacity devices which are usually SDRAM chips to store raw echo data and to solve the corner turning problem by efficient matrix transpose method. By designing data interleaved patterns and controlling command cycles of SDRAM chips in a reasonable way, this paper presents a novel matrix transpose method which can be used to improve the efficiency of corner turning memory (CTM) for real-time SAR imaging system. After board-level verification, the efficiency of this new matrix transpose method can be greatly improved to >99%, which is greater than other typical SDRAM-based CTM design methods and is more suitable for real-time SAR imaging system.

Extended wavenumber domain algorithm for equivalent squinted linear frequency modulated continuous wave SAR imaging

Ability to acquire high-resolution, phase-preserved images is an important requirement in modern synthetic aperture radar (SAR) applications. Wavenumber domain algorithms are good choices to achieve this objective without approximation in the wavenumber reversal process. However, when squint angle gets larger, even wavenumber domain algorithms suffer from resolution and computational efficiency degradations. In order to accommodate wavenumber domain algorithms to highly squinted SAR imaging by exploiting squint minimization and two-dimensional interpolation schemes, the proposed extended wavenumber domain algorithm possesses the advantages of maximum spectrum utilization, processing efficiency, and good interpretation of the two-dimensional interpolation. It can also be easily incorporated with motion compensation without complicating the motion analysis. Simulations and real data experiments validate our proposed algorithm.

Metrics to evaluate compression algorithms for raw SAR data

Modern synthetic aperture radar (SAR) systems have size, weight, power and cost (SWAP-C) limitations since platforms are becoming smaller while SAR operating modes are becoming more complex. Thus, SAR systems are producing an ever-increasing volume of data that needs to be transferred to a ground station for processing. A compression algorithm seeks to reduce the data volume of the raw data; however, the algorithm can cause degradation and losses that may degrade the effectiveness of the SAR mission. This work addresses the lack of standardised quantitative performance metrics so that the performance of SAR data-compression algorithms can be objectively quantified. Therefore, metrics are established in two different domains, namely the data domain and the image domain. Since different levels of degradation are acceptable for different SAR applications, a trade-off can be made between the data reduction and the degradation caused by the algorithm. Due to SWAP-C limitations, there remains a trade-off between the performance and the computational complexity of the compression algorithm.

Dynamic changes on the Wilkins Ice Shelf during the 2006–2009 retreat derived from satellite observations

Abstract. The vast ice shelves around Antarctica provide significant restraint to the outflow from adjacent tributary glaciers. This important buttressing effect became apparent in the last decades, when outlet glaciers accelerated considerably after several ice shelves were lost around the Antarctic Peninsula (AP). The present study aims to assess dynamic changes on the Wilkins Ice Shelf (WIS) during different stages of ice-front retreat and partial collapse between early 2008 and 2009. The total ice-shelf area lost in these events was 2135 ± 75 km2 ( ∼ 15 % of the ice-shelf area relative to 2007). Here, we use time series of synthetic aperture radar (SAR) satellite observations (1994–1996, 2006–2010) in order to derive variations in surface-flow speed from intensity-offset tracking. Spatial patterns of horizontal strain-rate, stress and stress-flow angle distributions are determined during different ice-front retreat stages. Prior to the final break up of an ice bridge in 2008, a strong speed up is observed, which is also discernible from other derived quantities. We identify areas that are important for buttressing and areas prone to fracturing using in-flow and first principal strain rates as well as principal stress components. Further propagation of fractures can be explained as the first principal components of strain rates and stresses exceed documented threshold values. Positive second principal stresses are another scale-free indicator for ice-shelf areas, where fractures preferentially open. Second principal strain rates are found to be insensitive to ice-front retreat or fracturing. Changes in stress-flow angles highlight similar areas as the in-flow strain rates but are difficult to interpret. Our study reveals the large potential of modern SAR satellite time series to better understand dynamic and structural changes during ice-shelf retreat but also points to uncertainties introduced by the methods applied.

SAR Ground Moving Target Indication Based on Relative Residue of DPCA Processing.

For modern synthetic aperture radar (SAR), it has much more urgent demands on ground moving target indication (GMTI), which includes not only the point moving targets like cars, truck or tanks but also the distributed moving targets like river or ocean surfaces. Among the existing GMTI methods, displaced phase center antenna (DPCA) can effectively cancel the strong ground clutter and has been widely used. However, its detection performance is closely related to the target’s signal-to-clutter ratio (SCR) as well as radial velocity, and it cannot effectively detect the weak large-sized river surfaces in strong ground clutter due to their low SCR caused by specular scattering. This paper proposes a novel method called relative residue of DPCA (RR-DPCA), which jointly utilizes the DPCA cancellation outputs and the multi-look images to improve the detection performance of weak river surfaces. Furthermore, based on the statistics analysis of the RR-DPCA outputs on the homogenous background, the cell average (CA) method can be well applied for subsequent constant false alarm rate (CFAR) detection. The proposed RR-DPCA method can well detect the point moving targets and distributed moving targets simultaneously. Finally, the results of both simulated and real data are provided to demonstrate the effectiveness of the proposed SAR/GMTI method.

Joint Sparsity in SAR Tomography for Urban Mapping

With meter-resolution images delivered by modern synthetic aperture radar (SAR) satellites satellites like TerraSAR-X and TanDEM-X, it is now possible to map urban areas from space in very high level of detail using advanced interferometric techniques such as persistent scatterer interferometry and tomographic SAR inversion (TomoSAR), whereas these multi-pass techniques are based on a great number of images. We aim at precise TomoSAR reconstruction while significantly reducing the required number of images by incorporating building a priori knowledge to the estimation. In the paper, we propose a novel workflow that marries the freely available geographic information systems (GIS) data (i.e., 2-D building footprints) and the joint sparsity concept for TomoSAR inversion. Experiments on bistatic TanDEM-X data stacks demonstrate the great potential of the proposed approach, e.g., highly accurate tomographic reconstruction is achieved using six interferograms only.

Demonstration of Super-Resolution for Tomographic SAR Imaging in Urban Environment

Tomographic synthetic aperture radar (SAR) inversion, including SAR tomography and differential SAR tomography, is essentially a spectral analysis problem. The resolution in the elevation direction depends on the elevation aperture size, i.e., on the spread of orbit tracks. Since the orbits of modern meter-resolution spaceborne SAR systems, such as TerraSAR-X, are tightly controlled, the tomographic elevation resolution is at least an order of magnitude lower than in range and azimuth. Hence, super-resolution (SR) reconstruction algorithms are desired. Considering the sparsity of the signal in elevation, a compressive sensing based super-resolving algorithm, named “Scale-down by L1 norm Minimization, Model selection, and Estimation Reconstruction” (SL1MMER, pronounced “slimmer”), was proposed by the authors in a previous paper. The ultimate bounds of the technique on localization accuracy and SR power were investigated. In this paper, the essential role of SR for layover separation in urban infrastructure monitoring is indicated by geometric and statistical analysis. It is shown that double scatterers with small elevation distances are more frequent than those with large elevation distances. Furthermore, the SR capability of SL1MMER is demonstrated using TerraSAR-X real data examples. For a high rise building complex, the percentage of detected double scatterers is almost doubled compared to classical linear estimators. Among them, half of the detected double scatterer pairs have elevation distances smaller than the Rayleigh elevation resolution. This confirms the importance of SR for this type of applications.

VALIDATION OF DEMs DERIVED FROM HIGH RESOLUTION SAR DATA: A CASE STUDY ON BARCELONA

Abstract. In recent years, Synthetic Aperture Radar (SAR) data have been widely used for scientific applications and several SAR missions were realized. The active sensor principle and the signal wavelength in the order of centimeters provide all-day and all-weather capabilities, respectively. The modern German TerraSAR-X (TSX) satellite provides high spatial resolution down to one meter. Based on such data SAR Interferometry may yield high quality digital surface models (DSMs), which includes points located on 3d objects such as vegetation, forest, and elevated man-made structures. By removing these points, digital elevation model (DEM) representing the bare ground of Earth is obtained. The primary objective of this paper is the validation of DEMs obtained from TSX SAR data covering Barcelona area, Spain, in the framework of a scientific project conducted by ISPRS Working Group VII/2 "SAR Interferometry" that aims the evaluation of DEM derived from data of modern SAR satellite sensors. Towards this purpose, a DSM was generated with 10 m grid spacing using TSX StripMap mode SAR data and converted to a DEM by filtering. The accuracy results have been presented referring the comparison with a more accurate (10 cm–1 m) digital terrain model (DTM) derived from large scale photogrammetry. The results showed that the TSX DEM is quite coherent with the topography and the accuracy is in between ±8–10 m. As another application, the persistent scatterer interferometry (PSI) was conducted using TSX data and the outcomes were compared with a 3d city model available in Google Earth, which is known to be very precise because it is based on LIDAR data. The results showed that PSI outcomes are quite coherent with reference data and the RMSZ of differences is around 2.5 m.

PSInSAR Technology Research and its Application on Landslide Monitoring

Our country is one of the countries that with serious geological disasters,every year we loss nearly twenty billion yuan and hundreds of people dead because of it.How to liberate deformation monitoring from the traditional way by means of modern Synthetic Aperture Radar Interferometry (InSAR) is the responsibility of each technical workers. In this paper, we mainly introduce the recent fast-developed advanced InSAR technology -- Persistent Scatterers for SAR Interfero try PS InSAR and its application on Shuping landslide in the Three Gorges reservoir area .

Tomographic SAR Inversion by $L_{1}$ -Norm Regularization—The Compressive Sensing Approach

Synthetic aperture radar (SAR) tomography (TomoSAR) extends the synthetic aperture principle into the elevation direction for 3-D imaging. The resolution in the elevation direction depends on the size of the elevation aperture, i.e., on the spread of orbit tracks. Since the orbits of modern meter-resolution spaceborne SAR systems, like TerraSAR-X, are tightly controlled, the tomographic elevation resolution is at least an order of magnitude lower than in range and azimuth. Hence, super-resolution reconstruction algorithms are desired. The high anisotropy of the 3-D tomographic resolution element renders the signals sparse in the elevation direction; only a few pointlike reflections are expected per azimuth-range cell. This property suggests using compressive sensing (CS) methods for tomographic reconstruction. This paper presents the theory of 4-D (differential, i.e., space-time) CS TomoSAR and compares it with parametric (nonlinear least squares) and nonparametric (singular value decomposition) reconstruction methods. Super-resolution properties and point localization accuracies are demonstrated using simulations and real data. A CS reconstruction of a building complex from TerraSAR-X spotlight data is presented.

Fore and Aft Channel Reconstruction in the TerraSAR-X Dual Receive Antenna Mode

The TerraSAR-X satellite is a high-resolution synthetic aperture radar (SAR) system launched in June 2007 which provides the option to split the antenna in along-track direction and sample two physical channels separately. Modern SARs are equipped with active phased array antennas and multiple channels. In order to keep costs low, TerraSAR-X uses the redundant receiver unit for the second channel such that fore and aft channel signals are combined by a hybrid coupler to form sum and difference channel data. The dual receive antenna (DRA) mode can either be used to acquire along-track interferometric data or to acquire signals with different polarizations at the same time (Quad-Pol). Fore and aft channel reconstruction is necessary if ground moving target indication (GMTI) algorithms such as the displaced phase center antenna technique or along-track interferometry shall be applied, and in order to separate the horizontally and vertically polarized received signal components. The proposed approach uses internal calibration pulses from different calibration beams in order to estimate and compensate the hardware impact. The theoretical framework together with the results from the experimental data evaluation for the fore and aft channel reconstruction of the TerraSAR-X DRA mode are presented. The impact of the receive hardware transformation matrix estimation accuracy on errors in the reconstructed fore and aft channel image data is studied, and first examples on the GMTI capability of the TerraSAR-X DRA mode are given.

Extended wavenumber-domain synthetic aperture radar focusing with integrated motion compensation

Modern synthetic aperture radar (SAR) systems are continually developing in the direction of higher spatial resolution. This requires the usage of high range bandwidths combined with long azimuth integration intervals. High-quality SAR processing methods, which are able to deal with such sensor parameters, are necessary for focusing the raw data of such sensors. Wavenumber-domain (ω–k) processing is commonly accepted as the ideal solution to the SAR focusing problem. However, it is only applicable to spaceborne SAR data where a straight sensor trajectory is given. In the case of airborne data, wavenumber-domain processing is limited because of its inability to perform high-precision motion compensation. Here, the extended chirp scaling (ECS) algorithm has proven to be very powerful, although it has certain limitations concerning long aperture syntheses and highly squinted geometries. In the paper, a new stripmap SAR data-processing algorithm, called extended ω–k (EOK), is analytically derived. The EOK algorithm aims to combine the high focusing accuracy of the wavenumber-domain algorithm with the high-precision motion compensation of the ECS algorithm. The new EOK algorithm integrates a three-step motion compensation correction in the general formulation of the wavenumber-domain algorithm, leading to a new airborne SAR processing scheme, which is also very robust in the cases of long synthetic apertures and high squint angles. As demonstrated, it offers the possibility of processing wide-band, low-frequency airborne SAR data up to near-wavelength resolution. The performance and accuracy of the new EOK SAR data-processing algorithm are demonstrated using simulated data in different data collection scenarios and geometries as well as using interferometric data acquired by the airborne experimental SAR system of DLR at L-band (Horn, 1996; Scheiber, 1999).