Increasing application demands are driving the need for future spaceborne synthetic aperture radar (SAR) systems with high resolution and continuous ultrawide swath capabilities. Azimuth multichannel staggered SAR, which integrates variable pulse repetition interval (PRI) and multichannel techniques, presents a promising solution. However, the resulting nonuniform sampling invalidates conventional frequency-domain reconstruction algorithms and increases signal processing complexity. To address this challenge, this paper proposes a uniform reconstruction scheme based on phase center adjustment (PCA). By introducing a phase center variation, the scheme compensates for nonuniform components to achieve equivalent uniform sampling during data acquisition. The PRI design criterion is established to minimize the maximum PCA value and provide the allowable range of the initial PRI. Furthermore, activation strategies for both transmit and receive antenna elements are defined to jointly achieve the required PCA. Simulation results validate the effectiveness of the proposed scheme.

The synthetic aperture radar (SAR) is a crucial tool for maritime observation, with ships being the main targets at sea. Detecting these ships is foundational for other downstream tasks, making the study of ship detection algorithms highly significant. Currently, ship detection primarily relies on deep learning algorithms, and training neural networks requires a large amount of data. In various maritime observation tasks, identifying defocused ships is particularly important. However, current databases lack defocused ship data, and SAR ship image generation algorithms are not yet mature. Therefore, this letter proposes generating defocused ship data using the existing ship data. First, we introduce the signal model of defocused ships and the causes of defocusing. Next, we describe the generation of defocused ships with nonuniform rotation by introducing motion errors through resampling. We also explain the generation of defocused ships with translational motion using initial phase compensation and envelope shift. Finally, expansion experiments using spaceborne SAR data demonstrate the effectiveness of our method.

Synthetic aperture radar (SAR), capable of ultra-wide swath imaging, serves as a vital tool for ocean surveillance. Ship detection in SAR imagery holds significant importance for maritime security and traffic management. Current ship detection algorithms exhibit excessive computational complexity, and both conventional and deep learning-based approaches struggle to achieve rapid detection in ultra-wide swath SAR imagery acquired by next-generation SAR systems. This paper proposes a rapid adaptive anchor framework for ship detection. First, the detection criterion statistics are decomposed into multiple prefix sum matrices, ensuring minimal computational complexity for anchor boxes of arbitrary sizes. Second, the image is quadrant-divided, with detection criterion statistics computed separately to determine ship presence in each quadrant. Third, quadrants containing ships undergo iterative quadrant division, while ship-free quadrants are annotated using differential matrices. Finally, the initial anchor boxes are systematically shifted to repeat the second and third steps across the entire image, ultimately generating a sea clutter mask. The computational complexity of the proposed ship detection framework remains equivalent to that of loading the full SAR image, introducing negligible additional overhead to existing systems. The framework is validated using empirical SAR datasets, demonstrating enhanced computational efficiency without compromising detection accuracy.

Range ambiguity is a critical factor degrading the high-resolution and wide-swath (HRWS) imaging performance of spaceborne synthetic aperture radar (SAR), arising primarily from the antenna sidelobe characteristics. Recently, blind source separation (BSS) methods have shown promise in mitigating range ambiguity. However, existing studies have mainly focused on the determined scenario. In contrast, underdetermined cases are often more prevalent in practical settings. To address this gap, this article proposes a novel range ambiguity suppression scheme specifically designed for the underdetermined BSS (UBSS) scenario. Point and distributed targets simulation based on Sentinel-1 system is conducted to verify its effectiveness. The results indicate that for the point target imaging performance of two channels, peak sidelobe ratio (PSLR) and integrated sidelobe ratio (ISLR) are improved by an average of 6.62 and 9.47 dB, respectively. In the distributed target case, the separation and recovery of the echo signals in the target region achieve an average similarity (pixel, structure, and cosine metrics) exceeding 94.27%, and demonstrate robustness at signal-to-noise ratios above 25 dB. These findings provide insight into the feasibility of UBSS-based strategies for range ambiguity suppression and offer valuable reference points for future investigations involving single-channel implementations.

The Hongtu-1 (HT-1) synthetic aperture radar (SAR) system is the first spaceborne single-pass multibaseline (MB) interferometric SAR (InSAR) system based on four HT-1 SAR satellites flying in a cartwheel formation. This setup includes three secondary satellites that function as receive-only units to create a compact multistatic SAR system. The primary objective of the HT-1 mission is to generate a consistent global digital elevation model (DEM) at 1:50000 scale. In addition, the HT-1 mission will feature novel SAR imaging technology demonstrations. On March 30, 2023, four HT-1 satellites were successfully launched and started to provide spaceborne radar data services to users. This article provides a detailed description of the HT-1 MB InSAR system, including the SAR performance, the cartwheel formation designed for multistatic SAR data collection with desired baselines, and the uninterrupted synchronization link. The interferometric performance is thoroughly analyzed. With the recorded data, imaging and interferometric processing procedures are introduced, and the capabilities of single-pass MB InSAR DEM generation are demonstrated. Compared with those of ICESAT, the height errors are less than 2 m in flat terrain and less than 5 m in mountainous terrain. Moreover, the resolution and swath of multisatellite mosaic imaging are 3 m and 80 km, respectively. The repeat-pass differential InSAR measurement for surface deformation monitoring is also included.

Synthetic aperture radar (SAR) is a crucial technology in marine surveillance. Moving ships are always defocused in SAR images, which affects the following identification process. Inverse SAR (ISAR) technology has been employed to focus moving targets in SAR images, typically involving two steps: translation motion compensation and rotation motion compensation. Range envelope alignment is generally considered a well-established method for translation motion compensation. In rotation motion compensation, current approaches, however, rely on signal parameter estimation and optimization algorithms, which lack robust methods. This article presents a rotational motion compensation framework, named “azimuth envelope alignment,” which extends the range envelope alignment to the azimuth direction. First, a novel concept of “azimuth envelope” is introduced, and the process of azimuth envelope alignment is discussed. Then, a specific method is presented: the use of the iterative adaptive approach (IAA) and resampling for implementation of “azimuth envelope alignment.” This method first estimates the alignment parameter (AP) using IAA, followed by achieving alignment through resampling. This method exploits the high-precision estimation capability of the IAA while avoiding its sparse estimation results, enabling the robust, nonsparse refocusing of moving targets. Finally, simulations and experimental data validate the effectiveness of the proposed algorithm. The azimuth envelope alignment framework introduced in this article can serve as a general approach for moving target imaging, similar to range envelope alignment.

This article proposes a generalized compact polarimetry (GCP) mode along with two optimal polarization mode selection parameters to address the challenges of polarization mode selection in classification tasks across diverse scenarios. Theoretically, we conduct an in-depth analysis of the differences between circular and linear transmit polarizations, demonstrating their fundamental equivalence in terms of information content. For the first time, we propose that different classification tasks require different optimal polarization modes, and the optimal transmit polarization mode may lie in the elliptic polarization domain of synthetic aperture radar (SAR) systems rather than traditional circular compact polarimetric (CP) or linear dual-polarization (DP) modes. The proposed approach is validated using full-polarimetric SAR data from San Francisco and Hainan, showing that the optimal elliptical polarization mode achieves classification accuracies that are 2% to 42% higher than those of traditional CP or DP modes for certain categories, and performs comparably to full polarization. This improvement in accuracy stems from the interaction between the transmit polarization and the target scene, rather than advancements in classification algorithms. Using the two proposed parameters, the overall and category-specific classification performance of GCP modes can be effectively evaluated, enabling the identification of the optimal polarization mode for a given task. These findings provide significant insight into the design of future polarimetric SAR systems and offer new perspectives and directions for mission planning and mode selection for on-orbit satellites.

Synthetic aperture radar (SAR) tomography (TomoSAR) technology effectively provides precise three-dimensional (3D) forest vertical structure information. However, conventional TomoSAR methods require abundant acquisitions for accurate 3D reconstruction, which is time-consuming and low-efficiency for forest vertical structure mapping. To address these limitations, this paper proposes a novel micro-stack TomoSAR imaging approach utilizing four images, referred to as the double iterative adaptive residual approach (DIARA). The DIARA innovatively combines iterative inner-outer adaptive spectral estimation and residual optimization to enhance both processing efficiency and accuracy. For validation purposes, both simulated and airborne TomoSAR experiments are analyzed at P- and L-band. The P-band results from the BorTomoSAR campaign indicate that the DIARA acquires higher accuracy for estimating forest height than the conventional methods, i.e., improvingR2from 0.443 to 0.628, mean absolute error (MAE) from 0.779 m to 0.625 m, mean absolute percentage error (MAPE) from 4.629% to 3.680%, and root mean square error (RMSE) from 0.941 m to 0.771 m. Additionally, the performance is further validated by the TropiSAR P-band campaign, which confirms the robustness of the proposed DIARA in high-canopy, complex forest environments. Furthermore, the L-band results from HaiTomoSAR campaign demonstrate that the DIARA method successfully detects the weak ground scatterers beneath dense forest canopies, which validates its super-resolution capability in vertical structure reconstruction.

Airborne Synthetic Aperture Radar (SAR) Tomography (TomoSAR) technology facilitates the extraction of three-dimensional (3D) information of target scatterers. However, phase screens induced by radar platform trajectory errors causes TomoSAR defocusing, which adversely affects the accuracy of the forest vertical structure retrieval. Additionally, the phase screens exhibit space-variant characteristics, which significantly degrade the calibration performance estimated by traditional phase gradient autofocus (PGA). This paper proposes an improved phase calibration method synthesizing unconstrained optimization model and weighted PGA (WPGA), which effectively address the above challenges. Firstly, the SAR data stack is segmented into multiple subareas by assuming that phase screens are space-invariant within each small area, which reduces complexity and improves computational efficiency. Secondly, a WPGA method synthesizing the scatterer heights derived from an unconstrained optimization model is proposed to estimate the phase screens. Simulation experiments are conducted to validate the effectiveness of the proposed phase calibration method. Furthermore, the full-polarization SAR data stacks acquired by the BorTomoSAR campaign are used for tomographic focusing analysis. Experimental results demonstrate that the proposed method accurately estimates the phase screens at the P, L, and S bands, providing a efficient solution for the forest vertical structure retrieval.

The Hongtu-1 (HT-1) Synthetic Aperture Radar (SAR) constellation is the first in-orbit spaceborne single-pass multi-baseline interferometric SAR (InSAR) system. The system has the ability to conduct high-resolution earth observation and high-precision, high-efficiency terrain surveying. The highest resolution of the system is better than 0.5 m, and it has a 1:50000 scale global digital elevation model (DEM) and digital surface model (DSM) surveying capability. This paper provides a basic introduction to the HT-1 constellation, and demonstrates the advantages of single-pass multi-baseline InSAR results and its advantages over steep area.

Interferometric Synthetic Aperture Radar (InSAR) shift measurement plays a key role in image coregistration and absolute phase measurement and has significant applications in the InSAR processing workflow. However, the current shift measurement algorithms are limited by the relative bandwidth of the SAR system, resulting in low resolution and accuracy. SSENet is a recent InSAR shift measurement approach that utilizes deep learning to address these issues to some extent. This paper proposes a calibration method for SSENet, which introduces a lightweight neural network designed to refine the marginally biased output shifts. Furthermore, we demonstrate the performance of the refined SSENet algorithm and its potential in assisting phase unwrapping using LSAR-01 bistatic Synthetic Aperture Radar (SAR) data.

Synthetic aperture radar (SAR) is important for marine surveillance. The image resolution of SAR is obtained via coherent integration. However, with increasing coherent processing intervals (CPIs), the focusing quality of maneuvering ships in images will worsen, impacting target recognition. Defocusing is caused mainly by the change in the image projection plane (IPP) of the maneuvering ship and the change in the effective rotation vector (ERV). In this paper, an improved ISAR method is used to focus the ships in SAR images. First, the factors influencing the instantaneous imaging position are analyzed. Then, a new method based on data resampling for maneuvering ship imaging with a long CPI is proposed. This method consists of two main steps: ERV estimation and data resampling. The change in the ERV is estimated by using the instantaneous imaging position of all the scatterers, and the ship echo is resampled according to the estimation of the time-varying ERV. After resampling, we can obtain the echo from a constant rotating ship and can ensure a well-focused ship by using the traditional range-Doppler algorithm. This new method overcomes the restriction of ERV variation. Thus, a longer CPI can be used for imaging, while the IPP remains stable. Finally, simulation and measured data are used to verify the effectiveness of the proposed algorithm.

Polarimetry synthetic aperture radar (SAR) interferometry (PolInSAR) has been well-established for forest height estimation. However, employing mono-frequency SAR data for PolInSAR tree height inversion presents inherent limitations, posing challenges to ensure inversion accuracy. This article presents a novel method for the inversion of vegetation parameters using dual-frequency (DF) four-stage PolInSAR, aiming to address the limitations observed in mono-frequency inversion. By leveraging the differential penetration of vegetation across distinct frequency bands, this method facilitates the derivation of more precise volume-only coherence and ground phase information. Applying the DF four-stage PolInSAR method to a substantial dataset of simulation results identifies the optimal band combination as P- and L-band. Moreover, the band combination that yields the most significant enhancement in accuracy is determined to be L- and S-band. These simulation results inform the design of the DF full-polarization SAR system. Subsequently, airborne SAR data are acquired using this L- and S-band full-polarization airborne SAR system over the Saihanba Forest Farm in Hebei, China. Ground-based LiDAR measurements serve as reference values for the comparison of PolInSAR inversion results. The DF four-stage PolInSAR method has a 5.46% improvement in the inversion accuracy of airborne SAR data. Both simulation and airborne SAR data inversion outcomes demonstrate a significant enhancement in forest height inversion accuracy achieved through the DF four-stage PolInSAR method compared to the mono-frequency approach.

The dual-frequency (DF) synthetic aperture radar (SAR) in lunar orbit, using L and S bands, is the only full-polarization (FP) SAR. It explores different layers of the moon, relying on the unusual rotational symmetry for polarimetric calibration. Addressing prior issues in polarimetric evaluation, this study tackles the challenges of an expanding dataset (now exceeding 900 scenes) and the necessity to evaluate polarimetric distortion (PD) values in the range direction. A novel polarimetric evaluation framework is introduced, enhancing existing methods. First, a scheme is proposed to eliminate low-quality areas in the range direction based on antenna isolation specifications, validated theoretically with simulated and real data. Second, more than 900 DFSAR scenes, downloaded prior to April 25, 2023, are utilized for evaluation, surpassing previous limitations and enhancing accessibility for users. By evaluating the L-band data, the image calibration results acquired by DFSAR between September 19, 2019, and December 4, 2020, have high accuracy and can be prioritized for lunar applications. In addition, we recommend that researchers pay attention to the issue of changing data quality for releases around March 2021.The amount of data in the S-band with the L-S-joint mode is small, and we provide the availability results directly. Third, the study applies the evaluated results to lunar polarization research, contributing to lunar exploration through polarimetric parameters. This comprehensive framework ensures accurate data utilization, addressing the evolving needs of lunar exploration with improved methodologies and expanded datasets.

Stereo-radargrammetric shift estimation is an important part of interferometric synthetic aperture radar (InSAR) data processing. However, the presence of residual topographical phase poses a challenge to achieving accurate coherent shift estimation in future high-resolution InSAR measurement tasks. In this work, we present a two-stage multi-baseline InSAR stereo-radargrammetric shift joint estimation approach. Our proposed method reduces the influence of the residual topographical phase, even in cases where no prior information is available or with low resolution prior digital elevation models (DEMs). In addition, a topography model based on Brownian motion is used to analyze the effect of the residual topographical phase on the accuracy of the shift estimation.

Range ambiguity suppression is a key technical challenge for synthetic aperture radar (SAR) systems. Waveform diversity technology is a potential solution due to its low system complexity. In this letter, a 2-D method based on orthogonal nonlinear frequency modulation (NLFM) waveform and azimuth phase coding (APC) for range ambiguity suppression is proposed. This approach not only suppresses range ambiguity energies instead of dispersing them, but also works for multiple consecutive orders of range ambiguity energies. The imaging processing and range ambiguity suppression performance are described in detail. In addition, the range-ambiguity-to-signal radio (RASR) is analyzed, and the simulation results for the point target and distribution scenarios are given to verify the effectiveness and practicality of the proposed scheme.

The innovative bistatic synthetic aperture radar (BiSAR) mission LuTan-1 (LT-1) uses a noninterrupted synchronization scheme to achieve high-precision phase synchronization. While radio frequency interference (RFI) is a major factor in deteriorating phase synchronization performance. To present the effect of RFI on the synchronization phase clearly, the characteristics of RFI in the synchronization link are described in detail, and a precise analytic expression between the amplitude, frequency, and phase of RFI and synchronization phase error is established. Furthermore, a novel pulse-compression-based notch (PCN) method is proposed to eliminate the phase error introduced by RFI. In the proposed method, the saturated distortion signals resulting from strong interferences are detected and discarded by the distribution features of their modes. Then, inspired by contrary thinking, the synchronization signal after pulse compression is notched instead of RFI. A fast missing data iterative adaptive approach (Fast MIAA) is performed to recover the gaped RFI signal and remove it from the compressed signal. Finally, the correct synchronization phases can be extracted from the peak positions of the remaining signals. Experimental results derived from using simulated and real synchronization data of the LT-1 system validate the performance of the proposed RFI mitigation method.

The interferometric synthetic aperture radar (InSAR) image shift measurement technique is of great significance in processing high-precision digital elevation model (DEM) generation and deformation measurements. It can be used in steps such as image fine coregistration, interferometric phase unwrapping and absolute phase calibration in the InSAR processing flow without an external DEM. However, the shifts estimated by current methods are of low resolution and have high measurement noise, which may have adverse impacts on subsequent applications. In this paper, a lightweight, high-resolution and low-noise interferometric stereo-radargrammetric shift estimation network (SSENet) is proposed to solve the aforementioned problems. It introduces deep learning technology to the InSAR shift estimation task for the first time. We propose forming multiscale 3D coherence coefficient cubes by projecting the shift values of the images onto the third dimension and then using a 3D convolutional network for multiscale fusion and encoding, followed by decoding with linear layers. In addition, a dataset generation and augmentation scheme based on real data is designed for model training and evaluation. Several sets of real SAR images from different regions of the world were used to evaluate SSENet. Compared with the typical coherent cross-correlation approach, SSENet reduces the mean absolute error of the estimated shifts by approximately 79% while improving the resolution by a factor of 4×4, making it possible to restore the absolute interferometric phase. Finally, we demonstrate a stitching strategy for processing large-scale SAR images and discuss the multiple potential uses of SSENet in the InSAR processing chain.

In an azimuth multichannel synthetic aperture radar (SAR) system operating in sliding spotlight mode or Terrain Observation by Progressive Scans (TOPS) mode, due to the varying Doppler centroid and the greater processed Doppler bandwidth, the conventional channel phase error estimation algorithms that tailored to stripmap mode will fail and unable to be directly employed. To address these issues, a deramping-based method is proposed in this letter, which extends the conventional methods to sliding spotlight and TOPS cases. By multiplying the signals of each channel with the corresponding deramp phase, the problem of varying Doppler centroid is solved, and its resulting greater Doppler bandwidth is reduced. Therefore, the spectral properties of the signals are the same as those in the stripmap case, and the conventional channel phase error estimation methods can be applied subsequently. Estimation and imaging results of the simulation data and the real data of GaoFen-3 (GF-3) operating in dual-channel sliding spotlight mode demonstrate the effectiveness of the proposed method.

In previous publications, it is generally believed that the rotated buildings in built-up areas can induce polarization orientation angles, which cause overestimation of the volume scattering contribution. However, we found that the rotation effect of the double-bounce scattering wall-ground structures cannot be accurately expressed by an orientation angle. In this letter, dihedrals are used to simulate the wall-ground structures, and a novel technique for inversion of rotation angles in built-up areas is proposed. This letter is committed to directly inverting rotation angles rather than orientation angles. Then the rotation angles are compensated into the measured scattering matrix instead of deorienting the coherence matrix. C-band GF-3 and L-band ALOS-2 polarimetric data of San Francisco are used to verify the effectiveness of the proposed inversion technique.

Nonlinear frequency modulation (NLFM) signal can construct the signal’s power spectral density to reduce sidelobes without loss of signal-to-noise ratio. LuTan-1 (LT-1) is an L-band spaceborne synthetic aperture radar mission which is launched in the beginning of 2022, and a high-precision NLFM signal generator is developed in LT-1. However, the existing model, i.e., the traditional frozen ionosphere model, can not accurately describe ionospheric dispersion effects faced by the NLFM signal due to the non-linear characteristic of the instantaneous frequency. Thus, an extended model is established in this paper to describe ionospheric dispersion effects of the NLFM signal. Then, the differences of ionospheric dispersion effects on the NLFM and linear frequency modulation signals are compared. Afterwards, a method that embedded into the focusing procedure is proposed, which aims to eliminate ionospheric dispersion effects for the NLFM signal. Finally, the hardware-in-the-loop simulations of point targets and distributed targets are performed to verify the proposed method. The method proposed in this paper is used in the ground processing system of LT-1.

The 3-D structure of forests is an important indicator for evaluating forest health and can provide data support for ecological monitoring and protection. Synthetic aperture radar (SAR) tomography (TomoSAR) is an important technology for forest structure estimation using the multibaseline (MB) SAR data stacks. The spectral estimators, such as the Capon method, are usually used to estimate the 3-D reflectivity along elevation direction. In this letter, a modified Capon (M-Capon) method is proposed to improve the estimated profiles of ground and canopy scatterers in the elevation direction, including the estimation accuracy and resolution. This method combines the ideas of the CLEAN algorithm with the Capon method and uses iteration to improve the resolution and accuracy of the estimation. The effectiveness of the M-Capon method is demonstrated using the simulated data and the MB SAR data acquired by the P-band airborne SAR system over the Saihanba Forest Farm in Hebei, China.

Distributed synthetic aperture radar (SAR) has various applications, including multi-angle imaging, SAR tomography, and interferometric SAR. However, these applications are affected by several factors, especially clock synchronization. Recently, an innovative push-to-talk (PTT) scheme was proposed to tackle the clock synchronization problem. As the PTT scheme can directly obtain the time and frequency deviations of the ultra-stable oscillators (USOs), it gives the possibility to achieve the clock synchronization of distributed SAR in real-time. Therefore, this paper builds a real-time framework of the PTT scheme. Compared with the previous clock synchronization module (CSM), an estimation module and a control module are added in this framework to fabricate an improved CSM (ICSM). The time and frequency deviations obtained by the ICSM are used to directly correct the GPS disciplined rubidium clocks. Based on the ICSM, some simulations are performed. The results indicate that the established real-time framework can achieve high precision time and frequency synchronization. In addition, we discuss the system implementation.

In an azimuth multichannel synthetic aperture radar (SAR) system, the unavoidable channel error will result in virtual targets or azimuth ambiguity, which significantly degrades the quality of high-resolution and wide-swath (HRWS) SAR image. Calibration of the imbalances between channels has been an important topic. First, to present the channel error effect on the final image clearly, a precise relation between the amplitudes of virtual targets and amplitude–phase error is established by matrix trace, which applies to the case of a certain amplitude–phase error. In addition, the total amplitude of all targets represented by the${L^{1}}$-norm reaches the minimum when there is no channel error. Based on this principle, an image-domain least${L^{1}}$-norm method is proposed to estimate the phase error between channels. By utilizing the focused high signal-to-noise ratio (SNR) images, the proposed algorithm achieves high estimation accuracy. Moreover, no redundant channel is required in the proposed algorithm compared with the subspace-based method. Finally, the effectiveness of the proposed channel error effect analysis and calibration method is validated by both the simulated and real SAR data.

Multichannel SAR system operating in the sliding spotlight mode has the capacity of imaging with very high resolution as requested by future spaceborne synthetic aperture radar (SAR) mission; hence, it has been attracted more and more attention in the SAR community. In a multichannel sliding spotlight SAR system with$N$channels, the overall Doppler bandwidth of the data acquired by each channel is greater than$N \cdot \text {PRF}$; thus, the preprocessing algorithms (channel mismatch calibration and signal reconstruction) are no more available. To address this issue, a full-aperture imaging method for azimuth multichannel sliding spotlight mode is proposed in this article. First, the raw data of each channel need to be preprocessed based on a deramping operation, which can obtain an unambiguous Doppler spectrum. Afterward, the algorithm proposed for single-channel sliding spotlight mode can be performed. Finally, to avoid possible back-folded SAR images, postprocessing after focusing is demanded in some cases. The imaging results of the data acquired by GaoFen-3 (GF-3) are given for the first time in this article, which proves the effectiveness of the proposed imaging method and demonstrates the capacity of high-resolution wide-swath imaging of the multichannel sliding spotlight mode.

Polarimetric synthetic aperture radar (SAR) has been extensively used in various remote sensing applications. In this article, a unified framework is designed to compare the classification performance of different polarimetric systems, which include quad-polarimetric (QP), compact-polarimetric (CP), and dual-polarimetric (DP). To avoid problems, such as the lack of uniform standards in feature extraction, the classification algorithm is directly based on the statistical characteristics of the coherency/covariance matrix and is implemented by extending the Wishart mixture model (WMM). The GF-3 data set in San Francisco and the AIRSAR agricultural data set in Flevoland are used in the experiment, and the following conclusions are generated. QP can achieve the highest classification accuracy in all classification tasks. When distinguishing three typical classes (water, urban, and vegetation) with very different scattering characteristics, the performance of different polarimetric systems is similar, and QP has only a slight advantage. For classification tasks of different classes with similar scattering characteristics, CP performs better in agricultural scenes, and the overall accuracy (OA) is only reduced by 3%–4% compared with QP. DP performs better in urban scenes, and OA is only reduced by 1%–3% compared with QP. These conclusions can provide guidance for future payloads’ design and the choice of polarimetric operation mode for existing multi-polarimetric SAR systems to achieve the purpose of giving full play to the advantages of different polarimetric systems.

One of the challenges of the low-frequency spaceborne synthetic aperture radar (SAR) is that propagation through the ionosphere will introduce nonnegligible errors in the final SAR product. In the low-frequency bistatic SAR (BiSAR) system, the ionosphere will degrade the imaging performance and cause nonnegligible phase errors in the single-pass SAR interferometry application, which results in undesired errors of digital elevation model (DEM). In this article, a method that embedded into the focusing procedure is proposed, which aims to estimate and remove ionospheric effects on L-band spaceborne BiSAR system. First, the impacts of ionospheric effects on the BiSAR system are demonstrated, including the deterioration of imaging performance and the geometric distortion. Then, a method is proposed to correct ionospheric effects. Afterward, the simulations, including point targets and distributed targets, are carried out to verify the effectiveness of the proposed method. The imaging results and the DEM reconstruction results show that the proposed method can effectively estimate and remove ionospheric effects on spaceborne BiSAR systems.

Synthetic aperture radar (SAR) is an advanced ground-observing remote sensing technology, and high-resolution wide-swath (HRWS) imaging has always been the goal of SAR. In general, an improved azimuth resolution requires a large pulse repetition frequency (PRF), resulting in high system requirements. To overcome this difficulty, azimuth multichannel technology has gradually developed, achieving HRWS imaging and decreasing the PRF by increasing the spatial sampling, i.e., increasing the number of antenna subapertures in the azimuth. This article proposes a theoretical architecture that generates multiple virtual receiving apertures in the azimuth rather than real apertures. The virtual receiving apertures are formed by multiplying the azimuth signals by linear phase histories provided by vortex beams carrying different orbital angular momentum (OAM) modes. Vortex beams with different OAM modes have different oblique phase wavefronts, so virtual receiving positions are generated in the along-track direction. This approach aims to reduce the PRF without increasing the azimuth real receiving aperture. Simulation results demonstrate the effectiveness and limitations of the method. Finally, to overcome the inherent limitations of the method, two possible implementation schemes are proposed.

Interferometric synthetic aperture radar (InSAR) is an efficient tool for global large-scale digital elevation model (DEM) generation. However, for steep terrain, the current approaches cannot stably reconstruct valid DEM products from a single-baseline InSAR image pair without an external reference DEM due to the influence of shadow/layover geometries, phase noise, and the Itoh condition limitation in the phase unwrapping (PU) process. In this article, a novel stereo-radargrammetry-assisted PU (SAPU) approach with no need for external auxiliary information is proposed to eliminate the constraint of the Itoh condition by exploiting the internal stereo-radargrammetric shifts. The method reduces the phase gradient in the interferogram and guides the PU process with automatically selected tie points. Notably, the current stereo-radargrammetry approaches will deteriorate to an incoherent state in steep terrain, hampering the reliability and accuracy of SAPU. Accordingly, we also propose an adaptive weighted subwindow-coherent stereo-radargrammetric shift estimation (AWS-CSE) method to improve the accuracy of subpixel shifts by introducing local topographic phase consistency in coherence estimation. We quantitatively validate the performance of the proposed methods based on the L-SAR 01 simulation data and three pairs of repeat-pass single-baseline Advanced Land Observing Satellite (ALOS) phased array type L-band synthetic aperture radar (PALSAR) images from different areas, comparing the results with those of various traditional and deep-learning-based PU methods. The findings suggest that the proposed methods can generate accurate DEMs from single-baseline measurements in steep terrain while avoiding additional data acquisitions.

The ionosphere is a significant source of the phase dispersion on the low frequency radar signal for spaceborne synthesis aperture radar (SAR). LuTan-1 (LT-1) is an innovative spaceborne bistatic SAR (BiSAR) operated in L-band, which will be launch in 2021. Studying the impacts of ionospheric effects on spaceborne single-pass imaging and interferometry is a necessary work to support the ground processing system of LT-1. In this paper, The geometric distortions caused by the ionosphere, including the offsets of the SAR image and Digital Elevation Model, are analyzed firstly. Then the deterioration of the imaging performance caused by the quadratic phase error and the cubical phase error is analyzed, and simulation results of the point target are given to verify. Finally, a framework of ionospheric effects correction for BiSAR is proposed.

Azimuth ambiguity degrades the quality of synthetic aperture radar (SAR) images and leads to the increase of false alarm rate in ground moving target indication (GMTI). Due to the existing azimuth ambiguity, suppression methods do not remove the first-order ambiguity completely and ignore the ambiguity above first order as well, moving target detection is affected by residual ambiguity. Hence, a novel method to suppress first-order and higher order azimuth ambiguities for the dual-channel SAR/GMTI is proposed in this letter. First, the displaced phase center antenna (DPCA) technique is applied to suppress clutter. Then, estimate the local azimuth ambiguity-to-signal ratio (LAASR) to find out the area affected by ambiguity. Finally, an inpainting algorithm is improved to patch the ambiguity area. The proposed method can remove the ambiguity almost completely. Moreover, the method is verified using the GaoFen-3 SAR dual-channel complex image data, and the result shows that the false alarm of moving target detection is degraded without reducing the detection rate.

Synthetic aperture radar (SAR) ship detection is an important part of remote sensing applications. With the development of computer vision, SAR ship detection methods based on convolutional neural network (CNN) can directly perform end-to-end detection of near-shore ship targets. However, CNN-based methods are prone to generate false targets on land areas, especially when using a rotatable bounding box (RBox) for detection. Therefore, how to reduce the false alarm rate becomes a key direction in research for SAR ship detection. In this letter, the problem of negative sample intraclass imbalance in the training stage of CNN-based detection methods is pointed out for the first time, which is considered to be an important reason for the excessive false alarm rate in the land area. Then, a method is proposed to reduce the false targets generated in the land area by CNN-based detection methods. First, an RBox-based model is proposed as the basic architecture for detection. Then, a new loss function is adopted to guide the model to balance the loss contribution of different negative samples during the training stage. The experimental results prove that the proposed method can effectively reduce the false alarm rate of the model and boost the performance of CNN-based detection methods.

The challenging problem to realize high-resolution and wide-swath (HRWS) synthetic aperture radar (SAR) imaging is the ambiguity suppression in the azimuth and range directions. According to the spatial angle difference of each ambiguity component, the current technical approach is to design the spatial filter for achieving the ambiguity suppression based on the 2-D multichannel system. Along with the increasing of HRWS imaging requirements, the number of system channels also gradually increase and further result in the complex structure design of the phased array antenna system. Meanwhile, the traditional phased array antenna cannot effectively control the direction of the transmit beampattern in range. Unlike the traditional phased array, frequency diverse array (FDA) employs a small-frequency increment across the whole array elements and forms the range-angle-dependent S-shaped transmit beampattern, which can be utilized to separate the different range ambiguous region. Considering the above-mentioned characteristics and the range periodicity problem of transmit beampattern, this letter devises a scheme for spaceborne SAR HRWS imaging mode in the view of transmit beampattern utilizing 2-D planar array, i.e., the FDA in azimuth for removing the range nonperiodicity ambiguity and the conventional phased array in elevation for removing the range periodicity ambiguity. Simulation results have been presented to validate the effectiveness of the proposed scheme.

The bistatic synthetic aperture radar (BiSAR) plays an important role in remote sensing. However, the deviation between the two oscillators in BiSAR systems will cause a residual modulation of the echo signal. Therefore, the phase synchronization is an important issue that must be addressed in the BiSAR system. An advanced noninterrupted phase synchronization scheme is used for LuTan-1. The synchronization pulses are exchanged immediately after the ending time of the radar echo receiving window and before the starting time of the next pulse repetition interval, which will not interrupt the normal SAR operation. In order to evaluate the accuracy of the phase synchronization scheme, the model of phase synchronization is introduced at first. The hardware design and processing flow of LuTan-1 are introduced in detail. An innovative internal calibration strategy is also described. Then, the test data acquired by the ground validation system are analyzed to verify the effectiveness of the phase synchronization scheme. The signal-to-noise ratio (SNR) and the synchronization rate are the two most important factors to influence the accuracy in phase synchronization. The conclusions have guiding significance for the synchronization module design of LuTan-1 and the future BiSAR system.

This paper demonstrates the first experiment of Gaofen-3 (GF-3) satellite operated in dual-channel sliding spotlight mode. First, the raw data of two channels need to be preprocessed, including channel error correction and signal reconstruction, so that it can be focused by the subsequent conventional single-channel sliding spotlight mode imaging methods. After imaging processing, to avoid the possible back-folded SAR image, it is necessary to carry out postprocessing step. The imaging result of the data acquired by GF-3 is first given in this paper, it proves the capacity of achieving high-resolution wide-swath of multi-channel sliding spotlight mode, and the effectiveness of the processing method.

For ultra-high resolution spaceborne spotlight synthetic aperture radar data processing, an imaging algorithm is proposed in this paper, which takes into account the variation of the effective velocity caused by the curved orbit. First, an airborne SAR one-step motion compensation (MOCO) method is performed to compensate for the errors introduced by the curved orbit. Then, an azimuth decompression filter is utilized to separate the rang-focusing and the azimuth-focusing after a modified Stolt interpolation. Finally, a residual range cell migration correction (RCMC) and the azimuth compression are implemented successively, considering the range dependence of the effective velocity. Simulation results confirm the effectiveness of the proposed algorithm.

Mining induced subsidence seriously damages the ecological environment and may cause casualties. Therefore, the rapid and reliable monitoring is particularly important. However, due to severe noise and dense fringes, traditional InSAR methods often severely underestimate the deformation rate. Here, we propose a new processing flow and develop two deep convolutional neural networks for fast detection and phase unwrapping of local subsidence cones. The proposed method is applied to Datong City, Shanxi Province, which is rich in mining activates. The processing results verify the reliability of the method.

For synthetic aperture radars (SARs), an alternate transmitting mode based on waveform diversity is widely discussed for suppressing range-ambiguity in many letters, because it is easy to implement and there is no need to improve the pulse repetition frequency (PRF). These studies mainly focus on the discussion of pseudo-orthogonal waveform design, such as up-down chirp waveforms and orthogonal-frequency-division-multiplexing (OFDM) waveforms; however, the effect on imaging caused by waveform diversity is ignored. This letter, for the first time, provides a demonstrative derivation of imaging for the alternate transmitting mode, which will deepen the understanding of this mode and be helpful for the future research. In this letter, we point out that transmitting different waveforms will introduce a phase-amplitude periodic modulation in the azimuth domain; furthermore, it will cause the aliasing of the azimuth spectrum. In addition, the simulation experiment is performed for verifying the correctness of the theoretical analysis.

Phase synchronization is one of the key issues that must be addressed for the bistatic synthetic aperture radar (BiSAR) system. LuTan-1 (LT-1) is an innovative spaceborne BiSAR mission based on the use of two radar satellites operating in the L-band to generate the global digital terrain models in the bistatic interferometry mode. An advanced synchronization scheme is used for the LT-1 system. The synchronization pulses are exchanged immediately after the ending time of the radar echo-receiving window and before the starting time of the next pulse-repetition interval. Therefore, it cannot interrupt the normal SAR data acquisition, further improving the synchronization accuracy and avoiding the data missing effect. In this letter, a robust phase-error estimation and compensation method is proposed to improve the accuracy of the synchronization by using the Kalman filter. The test data acquired from the ground validation system of the LT-1 synchronization module are used to demonstrate the feasibility of the proposed scheme. The results validate the effectiveness of the proposed scheme and prove the promise for its future application in LT-1.

Azimuth ambiguity greatly affects the image quality and application of synthetic aperture radar (SAR). Several azimuth ambiguity suppression methods have been proposed; however, these methods cannot remove the ambiguity completely and only the first-order ambiguity has been considered. Hence, in this letter, a novel method based on Wiener filtering and local azimuth ambiguity-to-signal ratio (AASR) estimation for N-order azimuth ambiguity suppression is proposed. The Wiener filtering is used to attenuate the ambiguity energy. Then the original image and the filtered image are combined to estimate local AASR, which is used to identify the ambiguity pixel. Finally, the ambiguity can be removed via interpolation. Through this method, the N-order ambiguity energy can also be suppressed to a lower level, and simultaneously, the consistency, resolution, and signal-to-noise ratio of an SAR image are maintained. Furthermore, in order to verify the practicability of the proposed method, it has been tested on the GaoFen-3 image and TerraSAR-X image.

LuTan-1 (LT-1), i.e., TwinSAR-L, mission is an innovative spaceborne bistatic synthetic aperture radar (SAR) mission that is based on two satellites operating at L-band with flexible formation flying, which is planned to launch in 2020. The primary objective of LT-1 is to generate a highly accurate global digital elevation model (DEM). Beyond that, LT-1 will serve for the demonstration of some state-of-the-art technologies in radar field and some applications, such as biomass inversion, disaster forecasting, and climate and environmental monitoring, and phase synchronization is a technical challenge in realizing the highly accurate topography and deformation measurements. The pulsed alternate synchronization scheme, which is proposed to solve the synchronization problem of TerraSAR-X add-on for Digital Elevation Measurements (TanDEM)-X, is an efficient and accurate method. However, it interrupts the normal work of the TanDEM-X, accordingly flowing a series of problems. For LT-1, a novel synchronization scheme, in which the phase synchronization signal is exchanged by virtue of a time slot between radar signals, is applied. Thus, the working efficiency and synchronization accuracy can further be improved. Furthermore, the performance prediction and phase synchronization experiment for this synchronization scheme is presented, which verifies the feasibility of the proposed scheme. Finally, in order to guarantee the transmission quality of the synchronization signal, the illumination combinations and gain variation of the synchronization antenna in an orbital period are detailed.

LuTan-1 [(LT-1), i.e., TwinSAR-L] mission is an innovative spaceborne bistatic synthetic-aperture radar (BiSAR) mission focusing mainly on differential interferometry, which will be launched in 2020. This article introduces some important aspects of the LT-1 mission for the first time, including the formation configuration, imaging mode, application scenarios, and the efficient baselines between the master satellite and the slave satellite. To realize the high accurate topography and deformation measurements, a wide swath BiSAR focusing algorithm with phase reserving ability should be developed. This article proposes a modified bistatic range-Doppler algorithm based on 2-D principle of stationary phase spectrum, which reduces the phase error introduced by the root term expansion and has an excellent focus performance and a good phase reserving ability. Finally, the spaceborne bistatic simulation experiments using orbital parameters and imaging mode of the LT-1 mission, including point targets and scene targets, are utilized to illustrate the validity and accuracy of the proposed algorithm.

LuTan-1 (LT-1) refers to an innovative mission of spaceborne bistatic synthetic aperture radar (BiSAR) adopting the phase synchronization scheme of pulse exchange, which is scheduled to be launched in 2020. In this mission, the multipath effect caused by the reflection of satellites and Doppler frequency shift resulting from the relative motion between satellites constitute two critically latent factors deteriorating the performance of the phase synchronization scheme. This article details a loss factor to evaluate the fading of synchronization signals under the reflection of smooth and rough surfaces and estimates the influence of Doppler frequency shift on synchronization signals. Furthermore, the multipath channel impulse response is modeled, and the correlation characteristics of multipath channel are described by the coherent bandwidth and coherent time. In addition, two simulation experiments based on the system parameters of LT-1, are implemented to assess the influence of time-invariant and -varying multipath effects on the phase synchronization scheme. A scale experiment is also executed to quantify the influence of multipath effect on phase synchronization, and its result demonstrates that the phase synchronization scheme of LT-1 can not only achieve full coverage of space and backup capability of pulse exchange but also meet the application requirements under multipath effect.

Digital beamforming (DBF) with Scan-On-Receive (SCORE) in elevation is a powerful technique for spaceborne synthetic aperture radar (SAR) to achieve high-resolution wide-swath (HRWS) images. DBF in spaceborne SAR should be implemented in real-time to reduce the volume of the downloaded data. Current digital beamformers in SAR systems usually use phase shift instead of a time delay network to ensure real-time implementation. However, conventional phase shift DBF SAR with narrow array bandwidth is not applicable for broadband signals. The beam steering deviation as a function of frequency is obvious for signals with large fractional bandwidth, which appears as a phenomenon of frequency dispersion. If the signal bandwidth is wider than the array bandwidth, frequency dispersion can significantly deteriorate the signal-to-noise ratio (SNR) and must be well relieved. This article analyzes the frequency dispersion in DBF SAR and proposes an innovative DBF-SCORE scheme with specially designed scalloped beam. The proposed digital scalloped beamforming (DSBF) scheme is effective in relieving the influence of frequency dispersion in DBF SAR. Theoretical analysis and simulation results validate the effectiveness of the proposed scheme. The proposed DSBF scheme gives practical solution to the problem of frequency dispersion in broadband DBF SAR, which makes DBF-SCORE a more practical technique for future spaceborne HRWS SAR.

The phase synchronization is one of the key issues to be addressed for the bistatic synthetic aperture radar system. In this paper, an advanced non-interrupted phase synchronization scheme is proposed. Both satellites are equipped with four synchronization antennas for a mutual exchange of synchronization pulses, which are transmitted rightly after the radar signal transmitting and before the echo receiving. Therefore, it can not interrupt the normal SAR data acquisition, which can further improve synchronization accuracy and avoid the data missing effect. The ground validation system for TwinSAR-L synchronization module is described in detail. The results are also evaluated to demonstrate feasibility of the proposed scheme.

A novel imaging algorithm is presented in this paper for focusing the very-high resolution spaceborne synthetic aperture radar (SAR) data of spotlight mode. First, the first step of two-steps processing approach (TSPA) is used to get a high azimuth sampling rate which is higher than PRF for the raw data. Then, the "fast-time" effect of stop-and-go approximation is corrected in 2-D frequency domain. Finally, the back-projection algorithm (BPA) is used to correct the error introduced by the curved orbit and the "slow-time" effect of stop-and-go approximation. The simulation results confirm that the proposed method has good performance. The spaceborne SAR data acquired by Gaofen-3 SAR systems demonstrate the feasibility of the proposed method.

Synthetic aperture radar (SAR) tomography (TomoSAR) is well established for three-dimensional (3D) reconstruction in urban buildings. There are many methods usually require a large data stacks for a reliable reconstruction and very few methods to handle small data stacks. This paper proposes a new processing method for 3D reconstruction of tall buildings using extremely small data stacks with large baseline interval. In this method, it is necessary to select persistent scatterers (PSs) with high signal-to-noise ratio (SNR) as reference points and estimate the elevation ambiguity issue by using the geometric relationship of SAR imaging. This paper presents the experimental results and shows a well performance of the proposed method in 3D reconstruction of tall buildings with simulated data and SAR data acquired by Chinese spaceborne SAR GF-3 satellite.

The GaoFen-3(GF-3) satellite is the first full-polarized synthetic aperture radar (SAR) imaging satellite of china, which was launched in August 2016. This paper obtained the geographic information based on the actual data from GF-3 satellite. Range-Doppler Model is used to process GF-3 data to verify the different between the position accuracy after correction and the practical positioning accuracy. The difference provides a certain reference for domestic satellites to promote actual positioning accuracy.

TwinSAR-L (Terrain Wide-swath INterferometric L-band SAR) is an innovative spaceborne bistatic SAR mission, whose primary objectives are achieving a global DTM dataset with high accuracy and observing the land deformation using differential InSAR technology within short revisit time.This paper presents a general methodology for the end-to-end raw data simulation for the spaceborne interferometry SAR systems, which requires correct terrain-mapped phase preserving, including the monostatic and the distributed systems. This paper describes the raw data generation method and its application in TwinSAR-L development phase.

In the multichannel synthetic aperture radar (SAR) systems, unavoidable channel errors will significantly degrade the performance of the ambiguity suppression. To address this problem, a channel phase error estimation method is proposed in this letter. First, the multichannel echo model and the weighted backprojection algorithm are introduced. The channel phase errors are then estimated by maximizing the image intensity using the gradient descent method. The simulation results confirm that the proposed method has higher accuracy and robustness than the orthogonal subspace method. The airborne SAR data acquired by a four-channel strip map C-band airborne SAR system demonstrate the feasibility of the proposed method.

Highly squint synthetic aperture radar (SAR) increases the flexibility and aspect-information of observation but poses several challenges to frequency-domain algorithms. The back-projection (BP) algorithm is recognized to produce the best results for highly squint mode but at high computational expense. Several fast BP algorithms have been developed to enhance the efficiency of the BP integral. The Cartesian factorized BP (CFBP) algorithm has been proposed to improve the performance. However, CFBP is ineffective in the highly squint case because its range model (RM) is imprecise. In this letter, we propose a modified CFBP (MCFBP) algorithm for highly squint spotlight SAR imaging. We employ a transformed Cartesian coordinate system to treat the transceiver mode as approximately side-looking mode. According to the transformed coordinate system, we propose a modified RM (MRM) to reduce the range error. Based on the MRM, we derive modified image spectrum compression steps and the Nyquist sampling rate of the subaperture image. MCFBP inherits CFBP's accuracy and efficiency advantages. Results of simulations and experiments performed by the X-band airborne SAR system with a maximum bandwidth of 1.2 GHz validate the improved performance of the proposed algorithm relative to BP and fast factorized BP.

Bistatic and multistatic synthetic aperture radar (SAR) can greatly improve system performance in various aspects, especially in achieving high-resolution wide-swath (HRWS) images. However, the specific system structure may lead to a higher degree of range ambiguities. This letter studies the structure of multistatic multiple elevation beams (MMEB) system which achieves HRWS images with the drawback of more serious range ambiguities compared with the conventional SAR which must be suppressed to get an acceptable system performance. The character that the receivers form, a prospective distributed multichannel system in azimuth, can give a guide in estimating the strong range ambiguities and remove them. An innovative method in estimating and removing (not just smearing) the strong range ambiguities based on this character is proposed. Theoretical analysis and experimental results show the effectiveness of the method. The proposed method considerably improves the range ambiguous performance of the MMEB system without using digital beamforming or pulse coding and makes full use of the system character, and thus improves the applicability of the system.

A backprojection (BP) algorithm is recognized as an ideal method for high-resolution synthetic aperture radar (SAR) imaging. Several fast BP algorithms have been developed to enhance the efficiency of the BP integral. The Cartesian factorized BP (CFBP) algorithm is proposed recently to avoid massive interpolations and improve the performance. However, integrating autofocus techniques with the CFBP has not been discussed. In this letter, an autofocus CFBP algorithm is proposed to compatibly combine the autofocus processing within the CFBP. After modifying the spectrum compression step in the CFBP, the approximate Fourier transformation (FT) relationship between the modified compensated subaperture images and the corresponding range-compressed phase history data in the Cartesian coordinate is revealed. The phase error is obtained by the multiple aperture map drift method, and the singular value decomposition total least square method is combined to improve the estimate robustness. Employing the range blocking method, the range variance of the phase error is compensated. The proposed algorithm inherits the advantages of the CFBP. Experiments performed by the X-band airborne SAR system with a maximum bandwidth of 1.2 GHz validate the proposed approaches.

Since the echo characteristics of moving targets are different from the stationary scene, the traditional reconstruction algorithm, i.e., the reconstruction filter algorithm, is not applicable. In this paper, a novel reconstruction approach of the moving target for a multichannel in azimuth high-resolution wide-swath (HRWS) synthetic aperture radar (SAR) system, modified from the traditional reconstruction filter algorithm, is proposed. The core of this method is to solve the problem of reconstruction filter mismatch by adapting the motion parameters of the moving target. Compared with the reconstruction filter algorithm, this method can not only achieve the accurate reconstruction of the stationary scene, but also reconstruct the spectrum of the moving target with nice performance. The simulated results of point targets are shown to confirm the effectiveness of the proposed method.

Coastline extraction with high accuracy from wide-swath synthetic aperture radar (SAR) imagery is still a challenging problem due to speckle, sea state condition, and land type. This letter presents a novel approach for coastline extraction from SAR images with complex scenarios and large geograp hical coverage, using the combination of modified K-means method and adaptive object-based region-merging mechanism (MKAORM). First, a modified K-means method is used to produce initial oversegmentation for the following region-merging stage. Second, an adaptive and coarse-fine object-based region-merging scheme using subregion classification is exploited to extend the automatically selected “sea” seed and “land” seed, respectively, to extract the final coastline. MKAORM can reduce the high computation cost of coastline extraction from wide-swath SAR imagery while keeping high accuracy. More than 93% of the detected coastlines lie within 2-pixel distance in comparison with the manually traced coastlines in experiments taken on Sentinel-1A (S1A) IW (Interferometric Wide-swath) mode SAR imagery.

This paper addresses some important aspects for the spaceborne/stationary bistatic synthetic aperture radar (SAR) (SS-BiSAR) imaging with the transmitter, TerraSAR-X, operated in staring spotlight (ST) mode. With the large integration time reaching 7.5 s and the azimuth steering span reaching ± 2.2°, several significant effects occur, including troposphere delay, precision phase and time synchronization, the curved orbit effect, azimuth spectrum aliasing problem, and efficient frequency domain focusing algorithm. To circumvent the main effects, corresponding solutions are proposed, including a precise synchronization strategy with troposphere delay correction based on the direct signal from the transmitter and a modified and integrative bistatic polar format algorithm (PFA). This paper covers the theoretical development, implementation, and analysis of the SS-BiSAR PFA based on 2-D fast Gaussian gridding nonuniform fast Fourier transform with wavefront curvature correction. Furthermore, the high-resolution ST-mode SS-BiSAR image processed by the proposed algorithm is acquired, and the differences of scattering behaviors between monostatic and bistatic SAR images are analyzed in detail.

This paper proposes a synchronization algorithm for bistatic synthetic aperture radar (BiSAR) imaging in a spaceborne/stationary configuration. In real bistatic systems, synchronization errors are generally introduced into the received data. Additionally, the lack of precise imaging parameters, such as the position of the transmitter and the accurate sampling time, could affect the imaging quality greatly. Fortunately, the image could be well focused by the proposed algorithm in the case of lack of the accurate position of a transmitter and the sampling time. First, a preprocessing step is employed to remove synchronization errors through matching an echo signal with a direct signal. Then, a modified chirp scaling factor containing an error phase term is constructed, and the accurate position of the transmitter and the sampling time can be acquired by the phase extraction of the direct signal and the searching method based on contrast optimization. After that, the corresponding imaging process can be implemented. Finally, the proposed algorithm is validated by the simulation and experimental results, where TerraSAR-X is used as the illuminator.