Synthetic Aperture Radar Imaging Geometry Research Articles

Airborne Platform Three-Dimensional Positioning Method Based on Interferometric Synthetic Aperture Radar Interferogram Matching

As the demand for precise navigation of aircraft increases in modern society, researching high-precision, high-autonomy navigation systems is both theoretically valuable and practically significant. Because the inertial navigation system (INS) has systematic and random errors, its output information diverges. Therefore, it is necessary to combine them with other navigation systems for real-time compensation and correction of these errors. The SAR matching positioning and navigation system uses synthetic aperture radar (SAR) image matching for platform positioning and compensates for the drift caused by errors in the inertial measurement unit (IMU). Images obtained by SAR are matched with digital landmark data, and the platform’s position is calculated based on the SAR imaging geometry. However, SAR matching positioning faces challenges due to seasonal variations in SAR images, the need for typical landmarks for matching, and the lack of elevation information in two-dimensional SAR image matching. This paper proposes an airborne platform positioning method based on interferometric SAR (InSAR) interferogram matching. InSAR interferograms contain terrain elevation information, are less affected by seasonal changes, and provide higher positioning accuracy and robustness. By matching real-time InSAR-processed interferograms with simulated interferograms using a digital elevation model (DEM), three-dimensional position information about the matching points has been obtained. Subsequently, a three-dimensional positioning model for the platform has bene established using the unit line-of-sight vector decomposition method. In actual flight experiments using an FMCW Ku-band Interferometric SAR system, the proposed platform positioning framework demonstrated its ability to achieve precise positioning in the absence of signals from the global navigation satellite system (GNSS).

Suppressive Interference Suppression for Airborne SAR Using BSS for Singular Value and Eigenvalue Decomposition Based on Information Entropy

Suppressive interference is a common interference signal for synthetic aperture radar (SAR) that can seriously affect the target identification and imaging results of SAR. This paper proposes a method for suppressing suppressive jamming using blind source separation (BSS) for singular value and eigenvalue decomposition based on information entropy. First, we developed an airborne SAR imaging geometry model and a suppressive interference signal mixing model. Next, we perform blind signal separation of the interfered mixed signal by means of BSS based on singular value and eigenvalue decomposition. Then, we image the different signals we have extracted. Finally, we extract the features of the image domain for the separated signals and set the information entropy threshold by the difference of information entropy to identify the jamming signal and the source signal and obtain the source signal. This method uses eigenvalue and singular value decomposition for BSS and extracts the image domain features of the signal after BSS by information entropy and identifies the source signal by information entropy thresholding. This method compensates for the uncertainty in the decomposition of the signal by means of BSS. The signal loss is minimal and the similarity of the separated signal and the original signal is very high. Simulated and measured data demonstrate the feasibility of this algorithm.

SAR Image Matching Based on Local Feature Detection and Description Using Convolutional Neural Network

Feature detection is a vital step for the image registration process whose target is the misalignment correction among images to increase the convergency level. Deep learning (DL) in remote sensing has become a worldwide sensation. Despite its huge potential, DL has not reached its intended target concerning the applications of Synthetic Aperture Radar (SAR) images. In this study, we focus on matching SAR images using a Convolutional Neural Network. The big challenge in this study is how to modify a pretrained Visual Geometry Group model based on the multispectral dataset to act as a SAR image feature detector where it does not require any prior knowledge about the nature of the SAR feature. Since SAR images have different characteristics from optical images such as SAR dynamic range and imaging geometry, some problems arise and should be considered during the matching process. Despite all these difficulties, results demonstrate the robustness of the registration process where it can provide descriptors that preserve the localization data of features. Also, the proposed approach provides reasonable results compared to the state-of-the-art methods and outperforms the correlation approach and ORB descriptor under scaling. In addition, it may be considered an end-to-end image matching tool for SAR images, although the calculations of fine matching parameters are included.

Multi-Electromagnetic Jamming Countermeasure for Airborne SAR Based on Maximum SNR Blind Source Separation

Synthetic aperture radar (SAR) may be attacked by multifarious kinds of the active-jamming, which will lead to the ineffectiveness of SAR in complex electromagnetic environment. In this paper, a novel multi-electromagnetic jamming counter-measure for the airborne SAR is proposed based on maximum signal-to-noise ratio (SNR) blind source separation. Firstly, the imaging geometry and multi-component mixed signal model of airborne SAR are established. Then, based on multi-component mixed signal matrix, a blind source separation (BSS) method based on the maximum SNR is proposed to separate the real target echo signal from the multi-electromagnetic jamming signals. After real target echo signal identification, the high resolution images of the interested target area can be achieved by the corresponding SAR imaging method. The simulated and measured data results are present to prove the feasibility and effectiveness of the proposed method.

SAR Image Simulation of Complex Target including Multiple Scattering

We present a GPU-based computation for simulating the synthetic aperture radar (SAR) image of the complex target. To be more realistic, we included the multiple scattering field and antenna pattern tracking in producing the SAR echo signal for both Stripmap and Spotlight modes. Of the signal chains, the computation of the backscattering field is the most computationally intensive. To resolve the issue, we implement a computation parallelization for SAR echo signal generation. By profiling, the overall processing was identified to find which is the heavy loading stage. To further accommodate the hardware structure, we made extensive modifications in the CUDA kernel function. As a result, the computation efficiency is much improved, with over 224 times the speed up. The computation complexity by comparing the CPU and GPU computations was provided. We validated the proposed simulation algorithm using canonical targets, including a perfectly electric conductor (PEC), dielectric spheres, and rotated/unrotated dihedral corner reflectors. Additionally, the targets can be a multi-layered dielectric coating or a layered medium. The latter case aimed to evaluate the polarimetric response quantitively. Then, we simulated a complex target with various poses relative to the SAR imaging geometry. We show that the simulated images have high fidelity in geometric and radiometric specifications. The decomposition of images from individual scattering bounce offers valuable exploitation of the scattering mechanisms responsible for imaging certain target features.

A Fast Back-Projection SAR Imaging Algorithm Based on Wavenumber Spectrum Fusion for High Maneuvering Platforms

In this paper, a fast back-projection (BP) synthetic aperture radar (SAR) imaging algorithm based on wavenumber spectrum fusion is proposed for high maneuvering platforms with high squint angles and high dive angles. When the platforms have nonlinear trajectory errors, the trajectory errors will introduce significant spectrum displacement, which varies with the target position. If the influence of trajectory error is not considered, the trajectory’s deviation from the ideal SAR imaging geometry will degrade the focusing performance. The dive angle will further enlarge the influence on SAR focusing. In this paper, the center function of the spatially variable sub-aperture wavenumber spectrum is derived, firstly, which is the basis for reasonably setting the oversampling rate. Then, the accurate spectrum fusion method is proposed, which is efficiently implemented by fast Fourier transform (FFT) and circular shifting. The proposed algorithm is called high-squint and high-dive accelerated factorized back-projection (HSHD-AFBP). Compared to the commonly used spectrum fusion method, HSHD-AFBP not only improves the imaging quality, but also expands the image width. Finally, the effectiveness of the proposed algorithm is verified by simulation data.

Adaptive Antenna Pattern Notching of Interference in Synthetic Aperture Radar Data Using Digital Beamforming

Radio Frequency Interference (RFI) is a growing problem in Synthetic Aperture Radar (SAR) systems as scientific motivations push the radars to lower frequencies and as more wireless services share the frequency spectrum. New, advanced SAR instruments, such as NASA’s EcoSAR, DBSAR and DLR’s Tandem-L mission, employ a multichannel architecture capable of Digital Beamforming (DBF). Radars with DBF are capable of notching the antenna pattern in specific directions, which can be utilized to suppress RFI on board or in post-processing. A well-researched beamformer for this purpose is the Minimum Variance Distortionless Response (MVDR) Beamformer. However, the number of interferers that can be removed through notching is limited by the number of receive channels. It is therefore essential to adaptively change the antenna pattern notching throughout the image in time and frequency for the best results with a given number of receive channels. In this paper, we present several methods to achieve this notching by making use of the spatial SAR signal distribution in range time, range frequency, azimuth time and azimuth Doppler that is inherent to the SAR imaging geometry. Because this distribution is time-variable and yet predictable, it can be used to improve the angle of arrival estimation of the RFI and the adaptive notching. The presented methods can be applied to a Digital Beamforming (DBF) SAR signal with multiple channels in elevation and/or in azimuth. Simulations show that the proposed methods increase the ability to notch out-of-swath interference from multiple directions and lessen the impact on in-swath interference. The improvement of the interferometric coherence for a single-pass interferogram acquired by NASA’s EcoSAR system (capable of DBF in elevation) is demonstrated. The removal of periodic RFI artifacts is achieved.

High-Resolution Wide-Swath SIMO and MIMO SAR Imaging Using Short-Term Shift Orthogonal Code

Conventional single antenna synthetic aperture radar (SAR) has a conflict between wide imaging swath width and fine cross-range resolution. Single input multiple output (SIMO) and multiple input multiple output (MIMO) radars are the two methods to overcome this limitation. In order to separate the signals of MIMO radar, the codes are expected to be orthogonal for all delays. This is impossible in SAR. Due to the special imaging geometry of SAR, short-term shift orthogonal (STSO) codes were proposed to be used in MIMO SAR. This letter compares the SIMO SAR structure and MIMO SAR structure using STSO codes. We draw a conclusion that the number of receivers of MIMO structure is less than that of SIMO structure.

RESEARCH ON A DEM COREGISTRATION METHOD BASED ON THE SAR IMAGING GEOMETRY

Abstract. Due to the systematic error, especially the horizontal deviation that exists in the multi-source, multi-temporal DEMs (Digital Elevation Models), a method for high precision coregistration is needed. This paper presents a new fast DEM coregistration method based on a given SAR (Synthetic Aperture Radar) imaging geometry to overcome the divergence and time-consuming problem of the conventional DEM coregistration method. First, intensity images are simulated for two DEMs under the given SAR imaging geometry. 2D (Two-dimensional) offsets are estimated in the frequency domain using the intensity cross-correlation operation in the FFT (Fast Fourier Transform) tool, which can greatly accelerate the calculation process. Next, the transformation function between two DEMs is achieved via the robust least-square fitting of 2D polynomial operation. Accordingly, two DEMs can be precisely coregistered. Last, two DEMs, i.e., one high-resolution LiDAR (Light Detection and Ranging) DEM and one low-resolution SRTM (Shutter Radar Topography Mission) DEM, covering the Yangjiao landslide region of Chongqing are taken as an example to test the new method. The results indicate that, in most cases, this new method can achieve not only a result as much as 80 times faster than the minimum elevation difference (Least Z-difference, LZD) DEM registration method, but also more accurate and more reliable results.

Wavelet-Based Topographic Effect Compensation in Accurate Mountain Glacier Velocity Extraction: A Case Study of the Muztagh Ata Region, Eastern Pamir

Glaciers in high mountain regions play an important role in global climate research. Glacier motion, which is the main characteristic of glacier activity, has attracted much interest and has been widely studied, because an accurate ice motion field is crucial for both glacier activity analysis and ice avalanche prediction. Unfortunately, the serious topographic effects associated with the complex terrain in high mountain regions can result in errors in ice movement estimation. Thus, according to the different characteristics of the results of pixel tracking in the wavelet domain after random sample consensus (RANSAC)-based global deformation removal, a wavelet-based topographic effect compensation operation is presented in this paper. The proposed method is then used for ice motion estimation in the Muztagh Ata region, without the use of synthetic-aperture radar (SAR) imaging geometry parameters. The results show that the proposed method can effectively improve the accuracy of glacier motion estimation by reducing the mean and standard deviation values from 0.32 m and 0.4 m to 0.16 m and 0.23 m, respectively, in non-glacial regions, after precisely compensating the topographic effect with Advanced Land Observing Satellite–Phased Array-type L-band Synthetic Aperture Radar (ALOS–PALSAR) imagery. Therefore, the presented wavelet-based topographic effect compensation method is also effective without requiring the SAR imaging geometry parameters and has the potential to be widely used in the accurate estimation of mountain glacier velocity.

Fusing Meter-Resolution 4-D InSAR Point Clouds and Optical Images for Semantic Urban Infrastructure Monitoring

Using synthetic aperture radar (SAR) interferometry to monitor long-term millimeter-level deformation of urban infrastructures, such as individual buildings and bridges, is an emerging and important field in remote sensing. In the state-of-the-art methods, deformation parameters are retrieved and monitored on a pixel basis solely in the SAR image domain. However, the inevitable side-looking imaging geometry of SAR results in undesired occlusion and layover in urban area, rendering the current method less competent for a semantic-level monitoring of different urban infrastructures. This paper presents a framework of a semantic-level deformation monitoring by linking the precise deformation estimates of SAR interferometry and the semantic classification labels of optical images via a 3-D geometric fusion and semantic texturing. The proposed approach provides the first “SARptical” point cloud of an urban area, which is the SAR tomography point cloud textured with attributes from optical images. This opens a new perspective of InSAR deformation monitoring. Interesting examples on bridge and railway monitoring are demonstrated.

Nonlinear Model for InSAR Baseline Error

Synthetic aperture radar (SAR) interferometric baseline parameters form important input for SAR interferometry. In this paper, a nonlinear error model is established for the SAR interferometric baseline and parameterized as a polynomial based on the natural nonlinearity of the orbit of a satellite. Unlike conventional models, the proposed model takes into account the nonlinear part of the baseline error. A theoretical derivation is performed based on the imaging geometry of interferometric SAR, and the results of the analysis show that the parameters of the nonlinear baseline error model can be obtained from the relationship between the orbit, the nominal baseline, the baseline error, and the residual interferogram phase. A sample data set from the Japanese Earth Resources Satellite-1 (JERS-1) L-band SAR is used to validate the proposed model, and the results indicated that the compensation of the residual interferogram phase of the test data is superior to that provided by conventional models.

Spatial resolution analysis of low frequency ultrawidebeam–ultrawideband synthetic aperture radar based on wavenumber domain support of echo data

Compared with the traditional narrowbeam–narrowband (NB) synthetic aperture radar (SAR), the large fractional signal bandwidth and wide azimuth beamwidth of the low frequency (LF) ultrawidebeam–ultrawideband (UWB) SAR induce serious coupling between the azimuth and range wave numbers of the echo data, which might influence the behavior of the spatial resolution in the SAR imaging. The spatial resolution considering the system wave number coupling and radar squint angle for the LF UWB SAR system is proposed based on the wave number domain support (WDS) of the echo data. First, the SAR imaging geometry is built and then the spatial wave number of the echo data for the LF UWB SAR is analyzed. Second, based on the WDS of the echo data, the spatial resolution of the LF UWB SAR is derived. Moreover, the narrowing/broadening factor defined as the ratio of the proposed spatial resolution to the traditional spatial resolution is presented, which indicates the effects of the fractional bandwidth and associated integration angle as well as the squint angle on the spatial resolution. Finally, simulation results are given to prove the correctness of the theory analysis and validity of the proposed spatial resolution.

Research on Spatial Resolution of One-Stationary Bistatic Ultrahigh Frequency Ultrawidebeam–Ultrawideband SAR Based on Scattering Target Wavenumber Domain Support

Compared with the traditional one-stationary bistatic narrowbeam-narrowband (NB) synthetic aperture radar (SAR), the coupling between the azimuth and range wavenumbers for the one-stationary bistatic ultrahigh frequency (UHF) ultrawidebeam-ultrawideband (UWB) SAR is much larger due to the large fractional signal bandwidth and wide antenna beamwidth, which may affect the behavior of spatial resolutions. Considering the wavenumber coupling, the more accurate spatial resolutions for the one-stationary bistatic UHF UWB SAR are proposed based on the wavenumber domain support of the scattering target in this paper. First, the one-stationary bistatic SAR imaging geometry is provided, and then the spatial wavenumber of the scattering target for the one-stationary bistatic UHF UWB SAR is analyzed. Second, based on the spatial wavenumber spectrum, spatial resolutions of the one-stationary bistatic UHF UWB SAR are derived in detail. In addition, the squint angles of radars are considered in the derivation of the spatial resolutions. Besides, the narrowing/broadening factors defined as the ratio of the proposed spatial resolutions to traditional spatial resolutions are presented, which describe the effects of the fractional bandwidth and associated integration angle on the spatial resolutions. Finally, simulation results are given to prove the correctness and validity of the proposed resolutions.

High sidelobe effects on interferometric coherence for circular SAR imaging geometry

The coherence is a measure for the accuracy of the interferometric phase, and the synthetic aperture radar (SAR) interferometric coherence is affected by several sources of the decorrelation noise. For the circular SAR (CSAR) imaging geometry, the system response function is in the form of the Bessel function which brings a high sidelobe, and the high sidelobe of CSAR will be an important factor influencing the interferometric coherence. The effect of the high sidelobe on the coherence is analyzed and deduced. Based on the interferometric characteristics of the slight difference in the viewing angles and the potential pixel offset in the interferometric SAR (InSAR) images, a relation between the radar impulse response and the coherence loss function is derived. From the relational model, the coherence loss function due to the high sidelobe of CSAR is then deduced, and compared with that of the conventional SAR. It is shown that the high sidelobe of CSAR focusing signal will severely affect the baseline decorrelation and coregistration decorrelation. Simulation results confirm the theoretical analysis and quantitatively show the baseline and coregistration decorrelation degradation due to the high sidelobes of CSAR.

Correction of Positional Errors and Geometric Distortions in Topographic Maps and DEMs Using a Rigorous SAR Simulation Technique

In the history of surveying and mapping, a large volumes of topographic maps and digital elevation models have been created at various scales throughout the world. However, positional errors and geometric distortions may exist in the topographic contour maps and their derived DEMs due to inaccurate ground control and poor navigation techniques in the early years. In this paper, we present a new technique to detect and correct positional errors and geometric distortions in topographic data based on rigorous Synthetic Aperture Radar (SAR) image simulation and mathematical modeling of SAR imaging geometry. Our method has been successfully applied to two USGS topographical data sets in Antarctica. Using Radarsat SAR imagery, positional errors of these two data sets have been reduced from 5 km to 200 m and from 200 m to 50 m, respectively.