Synthetic Aperture Radar (SAR) tomography is an essential method for acquiring data used in the reconstruction of urban three-dimensional (3D) models. With advancements in SAR technology, polarization has been integrated into tomography for accurately identifying and locating target structures. Recent studies focus on independently enhancing the ability of compressed sensing (CS) or spectral estimation methods to utilize polarimetric data. Since urban areas contain both small-scale man-made objects and large-scale homogeneous or distributed targets, using either of these two methods alone cannot balance accuracy with the preservation of 3D details. To address this, this study presents an adaptive polarimetric SAR (PolSAR) tomography method based on scattering mechanism classification, which leverages the strengths of both CS and spectral estimation techniques. In this study, we focus on the inversion of a single scatterer, without any attempt to separate multiple scatterers. The proposed method classifies pixels into four categories based on their scattering mechanisms, typically corresponding to manmade and distributed targets. The processing algorithms for CS and spectral estimation are then selected automatically based on the classification. For the pixels processed using spectral estimation, an adaptive window is also employed to compute the covariance matrix. Furthermore, the method employs an optimal polarization basis projection technique to effectively utilize polarization information during the tomography process. Experimental results show that this approach significantly improves both reconstruction accuracy and structural preservation.

Spaceborne Synthetic Aperture Radar (SAR) Along-Track Interferometry (ATI) serves as a primary approach to measure the Total Surface Current Vector (TSCV) of the ocean. However, single spaceborne SAR systems are constrained by limited observation perspectives, leading to challenges in measuring two-dimensional (2D) TSCV. To address the 2D TSCV inversion issue, a bistatic SAR that utilizes Geosynchronous SAR (GEO SAR) as the radiation source and Low Earth Orbit SARs (LEO SARs) as passive receivers is proposed. This bistatic SAR configuration outperforms existing bistatic SAR ATI systems in terms of cost efficiency as well as inter-satellite synchronization simplicity. For the GEO-LEO SAR system, we develop new interferometric signal models and processing methods to enable one-dimensional (1D) and 2D TSCV estimation. Secondly, to enhance measurement performance, a system configuration optimization algorithm that considered both imaging and ATI performances is applied. Optimization results demonstrate that a larger GEO SAR elevation angle improves estimation accuracy. Finally, based on the currently operating L-band GEO SAR system LSAR4-01, optimization and full-link ATI simulations are conducted, verifying the effectiveness of the optimization algorithm and highlighting the potential design of LEO SAR orbits for high accuracy ATI observations. Simulations achieve an imaging resolution of <100 m and a 2D TSCV inversion bias of 0.1 m/s. The influence of errors on simulation accuracy is also discussed in depth.

Space-surface bistatic synthetic aperture radar (SS-BSAR) system consists of a spaceborne synthetic aperture radar (SAR) transmitter and ground-based receivers. This system has the advantage of multiple angles for observation, which can improve imaging capability and deformation measurement dimensions by differential interferometric SAR (D-InSAR). With the increasing number of spaceborne SARs and various corresponding working modes, processing SS-BSAR data with public ephemeris (generally inaccurate) and unknown signal parameters is significant for the full use of illuminators. This noncooperative status will result in interferometric phase errors in repeat- pass SS bistatic InSAR (SS-BInSAR), leading to deterioration of deformation retrieval accuracy. To address this, this article focuses on the method and processing of noncooperative repeat-pass SS-BInSAR. First, a repeat-pass SS-BSAR interferometric model was established. Based on this, the impacts of time synchronization errors and orbit errors on repeat-pass interferometric phase are modeled. Furthermore, an end-to-end compensation approach is proposed for accurate interferometric processing. This approach includes accurate estimation for signal parameters, interferometric phase error elimination, and digital elevation models (DEMs) fusion recovered from multiple observations. Finally, a repeat-pass SS-BInSAR experiment utilizing the Chinese Lutan-1 as the transmitter is carried out to verify our methods. The results show a centimeter-level accuracy of deformation measurement by a single InSAR pair, indicating a great potential of SS-BInSAR in deformation retrieval.

Polarimetric Synthetic Aperture Radar (PolSAR) holds significant utility in classification through exploiting polarization information. However, many sensors are dual-pol, and their capabilities of classification are limited by the absence of polarization information. This paper focuses on dual-pol classification, proposing a two-step dual-band classification strategy for the Wishart classifier. The first step involves leveraging two bands to category the strong scattering pixels. And the second step entails optimizing the classification of weak scattering pixels using interferometry coherence. We use practical data from the C and L bands, employing Support Vector Machine (SVM) as a comparative experiment to validate the effectiveness of our strategy. The total accuracy and Kappa of the Wishart classifier finally increase by 3.92% and 0.05, respectively, indicating the effectiveness of our strategy. In addition, the second step also independently improves the performance of SVM, suggesting its versatility. This study can offer a novel perspective for dual-pol and multi-band classification.

Polarimetric weather radar measurements vary nonlinearly with changes in radar frequency and scanning elevation. In comparative observation experiments between Ku-band weather radar and CINRAD/SA radar, there were systematic errors in the polarimetric data of the two radars, the reason was the difference of frequency and elevation angles of them. Because the observation data of the ground-based Ku-band weather radar is small, it cannot cover most of the precipitation. So the T-matrix method was used to simulate the differential reflectivity factors of the Ku-band and S-band radars at different elevation angles, and the BP neural network was trained based on the simulation data to realize the conversion of the differential reflectivity factor from S-band to Ku-band to correct the systematic errors. Using the measured data, it was verified that the BP neural network can correct the systematic errors between the differential reflectivity factors of the two radars, improve the data consistency of them, and provide possibility for data fusion of S-band and Ku-band weather radars.

Computerized Ionospheric Tomography (CIT) serves as a crucial method for ionospheric monitoring, playing a significant role in space environment surveillance and earthquake prediction. Existing CIT techniques are mostly based on the Global Navigation Satellite System (GNSS) system, constrained by the distribution of receivers. CIT based on geosynchronous SAR (GEO SAR) presents a solution by leveraging Persistent Scatterer (PS) points within the scene. However, current CIT techniques using GEO SAR typically utilize PS points from a single SAR image. This paper introduces a novel approach – a GEO SAR-based long-term joint multi-image CIT method. This method enhances the exploitation of satellite data, offers a broader range of observation angles, and improves tomography accuracy. Finally, through a CIT experiment involving three GEO SAR images, the electron density distribution is derived with a time resolution of 10 minutes. The results align with the International Reference Ionosphere (IRI) data, validating the feasibility and advantages of the proposed method. It is noteworthy that the proposed method caters to the tomography observation mode of a single satellite and is adaptable to each satellite within a satellite formation.

Alternate transmission and alternate reception (ATAR) mode, as a crucial polarization mode, enables a single channel to be utilized in dual-polarization weather radars, which reduces the weight, power, and cost of the radar. The ATAR mode has a broad application based on high-altitude platforms with limited load and power. The interval between co-polarization signals is twice the pulse repetition time (PRT) in the ATAR mode. Thus, the Nyquist velocity interval is half of the one existing in the traditional radars. This hampers the application of the ATAR mode in severe weather observations. In this letter, a velocity de-aliasing algorithm is proposed, where lag-1 cross-correlations between adjacent radials are utilized to calculate the velocity estimates. This extends the Nyquist velocity interval of the ATAR model twofold compared to that of the traditional algorithm which uses lag-1 autocorrelations between adjacent radials. In addition, the constraints of the proposed algorithm are analyzed. The proposed algorithm is validated via X-band PWR data, and the results show that compared to the true value, the biases of the dealiased velocities in the ATAR mode are within ±1m/s for 96.9% of the range bins. This method is also suitable for use with the alternate transmission and simultaneous reception (ATSR) mode.

Spaceborne synthetic aperture radar differential interferometry (D-InSAR) can measure large-scale surface deformation. When the deformation is negligible, the interferometric phase can be used to estimate the differential tropospheric delay (DTD). Nevertheless, a single satellite can only obtain the integrated DTD along the line-of-sight direction. Spaceborne simultaneous multiangle synthetic aperture radar (SSMA-SAR) observes a scene by multiangle spaceborne SAR satellites at the same time. It can measure integrated DTD from different viewing angles by D-InSAR, which will help realize the tomographic inversion of differential tropospheric refractivity (DTR) to obtain its spatial 3-D distribution. However, the inversion performance is sensitive to phase errors in interferograms, troposphere conditions, and system configurations. To address these issues, this article establishes an SSMA-SAR tropospheric tomography model, analyzes the error sources in the inversion, and proposes an optimization configuration design method for SSMA-SAR tropospheric tomography based on the nondominated sorting genetic algorithm II (NSGA-II). The simulation results show that SSMA-SAR has good potential to achieve high-accuracy 3-D DTR, with more than 70% improvement with the optimized configuration. Within the north latitude range of 0°–55°, the system can obtain high-precision 3-D DTR measurements and achieves submillimeter integrated tropospheric delay accuracy in the zenith direction with subkilometer resolution.

Owing to the independence of the specific differential phaseKDPfrom attenuation and miscalibration, it is often applied in attenuation estimation (i.e. estimation of the specific attenuationAfromKDP). However, the measured differential phase ΨDPincludes the backscattering differential phase δhvand noise, decreasing the accuracy ofKDPestimation. Additionally, considering that the coefficient γ of theA-KDPempirical relation is sensitive to temperature and drop size distribution, there is also concern over ana priorifixed value for γ. Especially at higher-frequency bands such as X-band, largeKDPcan amplify the biases caused by inaccurate γ. AHR and ZPHI are methods for estimating phase and attenuation respectively. Their opposite inputs and outputs create a strong coupling between the two methods, allowing both methods to simultaneously reduce the impact of contaminated ΨDPon estimation. Meanwhile, their high resolution makes it possible to conditioning γ adaptively at range resolution scale according to different rainfall situations. In this work, a method that combines the individual and common characteristics of AHR and ZPHI is proposed to improve the accuracy of phase and attenuation estimation by adaptively conditioning γ at high resolution, as well as reducing the biases of δhvand noise. The improved effects of this method are assessed with a typical storm event observed by a polarimetric X-band weather radar in Netherlands, and its performances are further evaluated with simulations comprehensively. The results show that this method can improve accuracy of both phase and attenuation estimation significantly, especially at largeKDP.

This paper proposes an enhanced framework for reconstructing three-dimensional (3-D) building model from airborne tomographic synthetic aperture radar (TomoSAR) point clouds which involves four crucial steps: progressive facade detection, extraction of roof points, extraction of roof outlines and reconstruction. Firstly, an efficient and robust building facade detection is undertaken by adopting a progressive detection method. Then, to extract roof points, the region growing procedure is improved by utilizing an adaptive neighborhood and robust normals of points. Subsequently, the α-shape algorithm is executed and enhanced through Delaunay triangulation network to extract fine outline points. Finally, roof outlines are regularized, and the building models get reconstructed. The proposed approach is tested using airborne TomoSAR point clouds in the Emei area located in the Sichuan province of China. The results show that the proposed method can reconstruct 3-D building models with better shapes compared to classical methods.

Space-surface bistatic synthetic aperture radar (SS-BSAR) system has the advantage of diverse observation angles due to flexible receiving configuration, thus it plays an important role in SAR multi-angle imaging and three-dimensional deformation retrieval. In this paper, based on the LuTan-1 SAR launched in 2022, we present a SS-BSAR interferometry experiment. First, the SS-BSAR system implementation and some experiment parameters are shown. Second, we introduce the SS-BSAR synchronization scheme and its imaging methods applied in this experiment, and establish a SS-BSAR dual-antenna interferometry model. Finally, we present the imaging and interferometry results of our SS-BSAR system and analyze the experimental result. This is the first result of LuTan-1 SS-BSAR interferometry, which demonstrates the ability for remote sensing observation applications based on the SS-BSAR system.

Geosynchronous Synthetic Aperture Radar(GEO SAR) has become a hot spot because of short revisit time and wide coverage. Compared with single satellite, distributed GEO SAR provides rich observation angles which makes high-accuracy three-dimensional(3D) deformation retrieval possible. However, there are significant differences in the resolution and texture of Interferometric Synthetic Aperture Radar(InSAR) image at different observation angles, which will lead to reduced accuracy of 3D deformation retrieval. In terms of problems above, Pseudo-CycleGAN is proposed in this paper based on phase unwrapping Deep Neural Network(DNN) and CycleGan. It can improve the accuracy of 3D deformation retrieval through texture assimilation of interferogram with high phase accuracy.

Designing repeat ground track (RGT) synthetic aperture radar (SAR) constellations for achieving rapid revisits over key areas is essential to employ spaceborne differential interferometric synthetic aperture radar (D-InSAR) technology in Earth observation missions such as geological disaster monitoring and prediction. In this paper, the features of average revisit time (ART) maps are first introduced and investigated, and then an efficient and resource-friendly approach to calculate the ART of constellations is proposed. On this basis, a systematic method for designing an RGT constellation is provided, incorporating lookup-table-based optimization. Once the requirements of the expected RGT constellation, the incident angle of sensors on the constellation, and the orbital elements of the seed satellite in the constellation are given, the range of the optimal inclination and longitude of the ascending node (LAN) of the seed satellite can be found and then the entire constellation is determined. The proposed method enhances the efficiency of revisit time analysis and avoids the repeated modeling when the observation requirements change. Therefore, it is applicable not only prior to launch but also guides orbital maneuvering to adjust constellation configuration for an effective response to sudden disasters, etc. Finally, multiple RGT constellation design tasks are presented to demonstrate the proposed method.

With the advantages of high spatial resolution, short repeat-pass cycle, and large observation area in Ka-band distributed geosynchronous (GEO) synthetic aperture radar (SAR) systems, its interferometry (InSAR) measurement can fast retrieve high-resolution and high-accuracy digital elevation model (DEM). However, compared to low-orbit systems, it is more difficult and costly for distributed GEO SAR systems to perform tight formation flying at such a high orbit altitude, and, therefore, atmospheric effects, which bring non-stationary and non-Gaussian phase errors, should be taken into account in its repeat-pass InSAR. To address the problems above, a spatial-temporal joint particle filter-based method (ST-PF) for DEM generation by distributed GEO InSAR is proposed in the paper. The proposed ST-PF method is validated under several stationary and non-stationary atmospheric conditions through simulation experiments, and high-accuracy and high-resolution DEMs are obtained. Moreover, the ST-PF method can withstand severe non-linearity in interferometric phases, which result from a relatively small ambiguity height in Ka-band. It is also tested that the mean square error (MSE) of the retrieved DEM is consistent with the posterior Cramer-Rao bound (pCRB) of the estimation problem, showing the validity and the accuracy of the proposed ST-PF method. With various processing parameters, errors, and scene types tested, the method shows good robustness in different conditions.

S-band spaceborne synthetic aperture radar (SAR) systems were relatively rare since most launched missions worked in L-, C- or X-bands. An S-band spaceborne SAR should have a better interferometric coherence and penetration capability compared to a C-band spaceborne SAR, which is not fully known. In this paper, we analyze the interferometric coherence based on NovaSAR-1 data in a case in Peth, Australia. Typical regions of interest (ROIs) were selected and the performance was compared to C-band Sentinel-1 data. The results show that S-band spaceborne SARs have better penetration towards shallow and sparse vegetation regions and higher temporal coherence than C-band systems.

The errors of time and frequency synchronization in bistatic synthetic aperture radar (SAR) can have serious impacts on SAR images, if not compensated well. In this paper, we analyze the influence of time synchronization error on bistatic geosynchronous SAR (GEO SAR) imaging. First, the GEO SAR echo signal model under the influence of time synchronization error is established, through which the impacts of the constant, linear and random time synchronization errors on imaging are analyzed theoretically. Second, the theoretical analyses were verified by simulations. In addition, through simulations, the influences of the time synchronization error on low earth orbit SAR (LEO SAR) and GEO SAR are compared. The results show that the impacts of the constant, linear and random terms on GEO SAR are the same, greater and slighter compared with those on LEO SAR, respectively. This study provides a theoretical basis for future research on the time synchronization technology of multi static GEO SAR.

Traditional interferometric synthetic aperture radar (InSAR) is based on broadside looking geometry and parallel tracks. With the increase of the orbit height in spaceborne SAR and the development of SAR constellations, InSAR data of a region can be acquired in complex geometry, especially squint beam steering and unparallel tracks. For the sake of optimal InSAR system design and data processing, it is necessary to model the geometric decorrelation in complex geometry. This letter derives an accurate analytical model of geometric decorrelation of SAR interferometric pairs for general SAR observation geometry. Nonidentity of impulse responses and nonorthogonal sidelobes are the main features hindering the model derivation in the complex geometry case. An impulse response-fitting method is proposed, where nonorthogonal bases are adopted to suit the features and, thus, accurately analyze the geometric decorrelation. Simulation results verify the analytical model. It is found that unparallel tracks will introduce an extra geometric decorrelation factor. Compared to cases of parallel tracks, unparallel tracks always worsen the geometric decorrelation and cannot be neglected.

A GEOsynchronous Spaceborne-Airborne Bistatic Synthetic Aperture Radar (GEO SA-BSAR) system has been proved to be a significant tool for moving targets monitoring. Due to the special geometry model of the GEO SA-BSAR system, there is a complex relative movement between the moving target and the bistatic radar, leading to an additional phase modulation of the echo and further, causing moving targets to be smeared in the SAR image. Recently, Deep Neural Network (DNN) shows great potential in rapid image recovery. However, most image recovery methods based on DNN concentrate on the whole image, which limits the imaging performance of sparse targets. In this letter, we propose a DNN framework with similarity constraints for GEO SA-BSAR moving target imaging. This DNN-based method optimizes the cosine similarity of azimuth signals between the ground-truth image and the predicted image in the loss function to recover the azimuth position and focusing characteristics of the sparse targets. Extensive experimental results prove that the proposed model can quickly obtain GEO SA-BSAR moving target images with small training datasets compared with some counterparts.

Doppler weather radar observations of strong convective weather phenomena, e.g., tornadoes and supercells, showed that their Doppler spectra could deviate from the Gaussian shape. Classical method for identification of non-Gaussian spectral signal (NGSS) is based on spectral width and high-order spectral moments (HOSM). The DFT processing is needed to obtain the power spectrum density to calculate HOSMs, increasing the computation complexity. Besides, power, mean velocity, and spectral width are often obtained using autocorrelation method, i.e., pulse-pair-processing (PPP) method which is generally considered to be the most efficient estimator, where a Gaussian spectrum is assumed. Thus, a bias in the mean velocity and spectral width will occur if the Doppler spectrum deviates from the Gaussian shape. In this article, a generalized PPP (GPPP) method, which calculates HOSMs using autocorrelation function directly, is proposed. It outputs two additional identification parameter products, including skewness and kurtosis, which can directly identify NGSS and contribute to select the appropriate spectral moment estimation algorithm for Gaussian-or-not spectral signal. Compared with the classical method, the NGSS identification based on the GPPP method has a lower computation complexity since the DFT processing is avoided and has a better performance especially for low signal-to-noise ratio conditions. Besides, the clustering algorithms including expectation maximization and K-means algorithms are investigated for the adaptive spectral moment estimation of NGSS for the first time. The numerical simulation experiments and verifications based on real weather radar data of an actual supercell are implemented to confirm the feasibility and superiority of proposed method.

Geosynchronous Synthetic Aperture Radar (GEO SAR) has advantages of a short revisit time and large coverage for the scene of interest, so lots of theories and analysis toward the GEO SAR have been developed. However, GEO SAR systems may generate radio frequency interference (RFI) to a low earth orbit SAR (LEO SAR), causing a decrease in Signal-to-Interference-plus-Noise Ratio (SINR) of SAR images. In order to evaluate the GEO-to-LEO RFI effect on imaging, we deduce the formulas of the RFI power and image SINR, and verify them by comparing them with numerically evaluated results from simulated images. Based on the formulas, we evaluate SINRs of LEO SAR images for different bistatic scattering coefficients. The results show that when the target forms a specular bistatic scattering geometric relationship with a GEO SAR and a LEO SAR, LEO SAR image quality is poor, with a SINR worse than 5 dB, but the RFI effects can be neglected in other cases.

Coherence-based synthetic aperture radar (SAR) tomography (TomoSAR) exploits the complex coherences of SAR images to achieve 3-D imaging. Utilizing two-sensor spaceborne SAR formation flying to realize coherence-based TomoSAR has attracted increasing attention because temporal decorrelation-free interferograms can be constructed; therefore, TomoSAR has excellent potential for inverting the vertical structures of natural scenes such as forests and glaciers. However, low earth orbit (LEO) TomoSAR is disadvantaged by limited data and nonuniform sampling in the elevation direction. Geosynchronous (GEO) TomoSAR can overcome these limitations owing to its short revisit time of no more than 24 h. For the first time, this article discusses coherence-based TomoSAR exploiting GEO SAR formation flying. The benefits of GEO-formation coherence-based TomoSAR, including the low cost of slave satellites, rich data sets, and uniform sampling, are noted. The key problems of system design, including the formation design and data acquisition, are discussed. A formation design method based on the minimum along-track baseline is proposed that can realize uniform elevation sampling. The geometric correlation of a general SAR observation geometry is derived; on this basis, an optimal data acquisition method based on the optimal height measurement Cramer-Rao lower bound (CRLB) is proposed. Finally, the performance of GEO-formation coherence-based TomoSAR is analyzed; in particular, the ambiguity height in the altitude direction, the Rayleigh resolution in the altitude direction, and the theoretical optimal geometric correlation are evaluated. Finally, computer simulations validate the proposed formation design method, data acquisition scheme, and performance analysis formula.

Multistatic geosynchronous synthetic aperture radar (GEO SAR) utilizes multiple satellites' transmitted and received signals simultaneously to generate multiple phase centers (PCs) and to reduce the synthetic aperture time and the power budget. Two key problems of this SAR system are the design of the PC array configuration to satisfy the resolution requirements and the suppression of the grating lobes that are inherently introduced by the PC array. The resolution of the SAR can be analyzed using generalized ambiguity functions (GAFs). However, the current research on the multistatic SAR resolution and grating lobes is mostly based on specified configurations and numerical simulation methods, which cannot establish the relationship between the system parameters and the resolution. This article proposes an analytical analysis method for the multistatic GEO SAR GAF-based on the array spatial ambiguity function (ASAF). First, gradient analysis is used to obtain analytical expressions for ASAF and the multistatic SAR GAF. On this basis, an analytical resolution expression is obtained, and an orbital element design method that considers the Earth's rotation is proposed. In addition, the lobe positions are analytically expressed based on the geometry, and the grating lobes are suppressed by designing the optimal integration time such that the null depth of the velocity ambiguity function (VAF) coincides with the grating lobe positions. Finally, simulation results at various positions for various orbit types demonstrate the accuracy of the GAF approximation formula and the satisfactory performance of the optimal time expression in suppressing the grating lobes.