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Fast factorized back-projection (FFBP) is a classical fast time-domain technique that has garnered significant success in spotlight synthetic aperture radar (SAR) signal processing. The algorithm's efficiency has been extended to stripmap SAR through integral aperture determination and full-aperture data block processing while retaining its computational efficiency. However, the above method is only operated in the azimuth direction, and the computing efficiency needs to be urgently improved in the actual processing process. This paper proposes a fast factorized back-projection algorithm for stripmap SAR imaging based on range block division. The echo data are divided into multiple subblocks in the range direction, and FFBP processing is applied separately to each full-aperture subblock, further enhancing computational efficiency. The paper analyzes the algorithm's principles, underscores the necessity of integral aperture determination and full-aperture data block processing, provides specific implementation steps, and applies the algorithm to point target simulation and experimental data from a vehicle-mounted ice radar. The experiments validate the algorithm's efficiency in stripmap SAR imaging. [ABSTRACT FROM AUTHOR]

Dedicated to synchronously acquiring large-area, high-precision, and multi-scale ocean wind and wave information, a novel concept of a spaceborne ocean microwave dual-function integrated sensor is proposed in this paper. It integrates the functions of a scatterometer and SAR by sharing a single phased-array antenna. An overview of the scientific requirements and motivations for the sensor are outlined firstly. In order to fulfill the observation requirements of both the functions, the constraints on the system parameters such as frequency, antenna size, and incidence angle are analyzed. Then, the selection principles of these parameters are discussed within the limitations of antenna area, bandwidth, available time, and cost. Additionally, the constraints on the time sequence of transmitting and receiving pulses are derived to ensure that there is no conflict when the two functions operate simultaneously. Subsequently, a method for jointly designing the pulse repetition frequency (PRF) of both the functions is introduced, along with zebra maps to verify its effectiveness. At the end of the paper, the system and performance parameters of the sensor are given for further insight into it. [ABSTRACT FROM AUTHOR]

Many fields of human activity benefit from the ability to create images of obscured objects placed behind walls and to map their displacement in a noninvasive way. Usually, imaging devices like Synthetic Aperture Radars (SARs) and Ground-Penetrating Radars (GPRs) use expensive dedicated electronics which results in prohibitive prices. This paper presents the experimental implementation and the results obtained from an imaging system capable of performing SAR imaging and interferometric displacement mapping of targets located behind walls, as well as 3D GPR imaging using a low-cost general-purpose radar sensor. The proposed solution uses for the RF section of the system a K-band microwave radar sensor module implementing Frequency-Modulated Continuous Wave (FMCW) operation. The low-cost sensor was originally intended for simple presence detection and ranging for domestic applications. The proposed system was tested in several scenarios and proved to operate as intended for a fraction of the cost of a commercial imaging device. In one scenario, it was able to detect and locate a 15 cm-diameter fire-extinguisher located at a distance of 3.5 m from the scanning system and 1.6 m behind a 3 cm-thick MDF (medium-density fiberboard) wall with cm-level accuracy. In a second test, the proposed system was used to perform interferometric displacement measurements, and it was capable of determining the displacement of a metal case with sub-millimeter accuracy. In a third experiment, the system was used to construct a 3D image of the inside of a wood table with cm-level resolution. [ABSTRACT FROM AUTHOR]

Nearshore ship detection has significant applications in both the military and civilian domains. Compared to synthetic aperture radar (SAR), polarimetric synthetic aperture radar (PolSAR) provides richer information for analyzing the scattering mechanisms of ships and enables better detection of ship targets. However, ships in nearshore areas tend to be highly concentrated, and ship detection is often affected by adjacent strong scattering, resulting in false alarms or missed detections. While the GP-PNF detector performs well in PolSAR ship detection, it cannot obtain satisfactory results in these scenarios, and it also struggles in the presence of azimuthal ambiguity or strong clutter interference. To address these challenges, we propose a nearshore ship detection method named ECD-PNF by integrating superpixel-level GP-PNF and refined polarimetric decomposition. Firstly, polarimetric superpixel segmentation and sea–land segmentation are performed to reduce the influence of land on ship detection. To estimate the sea clutter more accurately, an automatic censoring (AC) mechanism combined with superpixels is used to select the sea clutter superpixels. By utilizing refined eight-component polarimetric decomposition to improve the scattering vector, the physical interpretability of the detector is enhanced. Additionally, the expression of polarimetric coherence is improved to enhance the target clutter ratio (TCR). Finally, this paper combines the third eigenvalue of eigenvalue–eigenvector decomposition to reduce the impact of azimuthal ambiguity. Three spaceborne PolSAR datasets from Radarsat-2 and GF-3 are adopted in the experiments for comparison. The proposed ECD-PNF method achieves the highest figure of merit (FoM) value of 0.980, 1.000, and 1.000 for three datasets, validating the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper presents a method for extracting the digital elevation model (DEM) of forested areas from polarimetric interferometric synthetic aperture radar (PolInSAR) data. The method models the ground phase as a Von Mises distribution, with a mean of the topographic phase computed from an external DEM. By combining the prior distribution of the ground phase with the complex Wishart distribution of the observation covariance matrix, we derive the maximum a posterior (MAP) inversion method based on the RVoG model and analyze its Cramer–Rao Lower Bound (CRLB). Furthermore, considering the characteristics of the objective function, this paper introduces a Four-Step Optimization (FSO) method based on gradient optimization, which solves the inefficiency problem caused by exhaustive search in solving ground phase using the MAP method. The method is validated using spaceborne L-band repeat-pass SAOCOM data from a test forest area. The test results for FSO indicate that it is approximately 5.6 times faster than traditional methods without compromising accuracy. Simultaneously, the experimental results demonstrate that the method effectively solves the problem of elevation jumps in DEM inversion when modeling the ground phase with the Gaussian distribution. ICESAT-2 data are used to evaluate the accuracy of the inverted DEM, revealing that our method improves the root mean square error (RMSE) by about 23.6% compared to the traditional methods. [ABSTRACT FROM AUTHOR]

The airborne and satellite-based synthetic aperture radar enables the acquisition of high-resolution SAR oceanographic images in which even the outlines of ships can be identified. The detection of ship targets from SAR images has a wide range of applications. Due to the density of ships in SAR images, the extreme imbalance between foreground and background clutter, and the diversity of target sizes, achieving lightweight and highly accurate multi-scale ship target detection remains a great challenge. To this end, this paper proposed an attention mechanism for multi-scale receptive fields convolution block (AMMRF). AMMRF not only makes full use of the location information of the feature map to accurately capture the regions in the feature map that are useful for detection results, but also effectively captures the relationship between the feature map channels, so as to better learn the relationship between the ship and the background. Based on this, a new YOLOv7-based ship target detection method, You Only Look Once SAR Ship Identification (YOLO-SARSI), was proposed, which acquires the abstract semantic information extracted from the high-level convolution while retaining the detailed semantic information extracted from the low-level convolution. Compared to the deep learning detection methods proposed by previous authors, our method is more lightweight, only 18.43 M. We examined the effectiveness of our method on two SAR image public datasets: the High-Resolution SAR Images Dataset (HRSID) and the Large-Scale SAR Ship Detection Dataset-v1.0 (LS-SSDD-V1.0). The results show that the average accuracy ( A P 50 ) of the detection method YOLO-SARSI proposed in this paper on the HRSID and LS-SSDD-V1.0 datasets is 2.6% and 3.9% higher than that of YOLOv7, respectively. [ABSTRACT FROM AUTHOR]

When using ground-based synthetic aperture radar (GB-SAR) for monitoring open-pit mines, dynamic atmospheric conditions can interfere with the propagation speed of electromagnetic waves, resulting in atmospheric phase errors. These errors are particularly complex in rapidly changing weather conditions or steep terrain, significantly impacting monitoring accuracy. In such scenarios, traditional regression model-based atmospheric phase correction (APC) methods often become unsuitable. To address this issue, this paper proposes a clustering method based on the spatial autocorrelation function. First, the interferogram is uniformly divided into multiple blocks, and the phase consistency of each block is evaluated using the spatial autocorrelation function. Then, a region growing algorithm is employed to classify each block according to its phase pattern, followed by merging adjacent blocks based on statistical data. To verify the feasibility of the proposed method, both the traditional regression model-based method and the proposed method were applied to deformation monitoring of an open-pit mine in Northwest China. The experimental results show that for complex atmospheric phase scenarios, the proposed method significantly outperformed traditional methods, demonstrating its superiority. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) can detect moving targets on the ground/sea, and high-resolution imaging on the ground/sea has critical applications in both military and civilian fields. This paper attempts to use a spaceborne SAR system to detect and image moving targets in the air for the first time. Due to the high velocity of aerial targets, they usually appear as two-dimensional range and azimuth direction defocus in SAR images, and clutter will also have a profound impact on target detection. To solve the above problems, a method of detecting and focusing on a spaceborne SAR target based on a two-dimensional velocity search is proposed by combining the BP algorithm. According to the current environment of the aerial target and the number of system channels, the clutter suppression methods are set and combined with two-dimensional velocity search with different precision, the Shannon entropy under different search velocity groups is used to obtain the search velocity group closest to the actual velocity and realize the integrated processing of moving target detection–focused imaging parameter estimation. Combined with simulation data, the effectiveness of the proposed method is verified. [ABSTRACT FROM AUTHOR]

The spaceborne high-resolution and wide-swath synthetic aperture radar (HRWS-SAR) system combined with the ground moving target indication (GMTI) mode provides a promising prospect in the realization of wide-area target surveying and high-resolution target imaging. In this paper, a system design method is proposed for an HRWS-SAR-GMTI system with ideal reconstruction configuration. In the proposed method, the whole azimuth receiving channels are uniformly divided into multiple groups, where HRWS-SAR imaging is implemented in each sub-group and then GMTI processing is performed based on the reconstructed SAR images. Then, an optimal candidate PRF is properly selected with respect to the optimal reconstruction configuration. After that, the digital beam forming scanning on receive (DBF-SCORE) technique is applied to further enlarge the range swath and improve the noise equivalent scattering coefficient (NESZ). Based on the predesigned system, HRWS-SAR image-based GMTI processing can finally be accomplished. The effectiveness of the proposed method is validated by simulated experiments. [ABSTRACT FROM AUTHOR]

This paper investigates the airborne synthetic aperture radar via outfield experiments based on carrier-free ultra-wideband signals with fifth-order Gaussian pulses with imaging using the backward projection algorithm. Typical blanket jamming is selected in the study, and NAM, NFM, and SFM are used as jamming signals to investigate the backward projection BP imaging effect of airborne synthetic aperture radar when subjected to blanket jamming in a real-world environment. Subsequently, a variational modal decomposition algorithm is proposed, aiming at the anti-jamming processing of airborne synthetic aperture radar. Further, the variational modal decomposition algorithms are compared with the empirical modal decomposition for the anti-jamming effect, which verified the effectiveness and high efficiency of the method. This study provides a new solution for the imaging problem of airborne synthetic aperture radar when subjected to blanket jamming, and a helpful reference is provided for the selection of anti-jamming processing methods. [ABSTRACT FROM AUTHOR]

SAR image target recognition relies heavily on a large number of annotated samples, making it difficult to classify the unseen class targets. Due to the lack of effective category auxiliary information, the current zero-shot target recognition methods for SAR images are limited to inferring only one unseen class rather than classifying multiple unseen classes. To address this issue, a conditional generative network with the category features from the simulated images for zero-shot SAR target recognition is proposed in this paper. Firstly, the deep features are extracted from the simulated images and fused into the category features that characterize the entire class. Then, a conditional VAE-GAN network is constructed to generate the feature instances of the unseen classes. The high-level semantic information shared in the category features aids in generalizing the mapping learned from the seen classes to the unseen classes. Finally, the generated features of the unseen classes are used to train a classifier that can classify the real unseen images. The classification accuracies for the targets of the three unseen classes based on the proposed method can reach 99.80 ± 1.22% and 71.57 ± 2.28% with the SAMPLE dataset and the MSTAR dataset, respectively. The advantage and validity of the proposed architecture are indicated with a small number of the seen classes and a small amount of the training data. Furthermore, the proposed method can be extended to generalized zero-shot recognition. [ABSTRACT FROM AUTHOR]

High-resolution wide-swath (HRWS) imaging is the research focus of the modern spaceborne synthetic-aperture radar (SAR) imaging field, with significant relevance and vast application potential. Staggered SAR, as an innovative imaging system, mitigates blind areas across the entire swath by periodically altering the radar pulse repetition interval (PRI), thereby extending the swath width to multiples of that achievable by conventional systems. However, the staggered mode introduces inherent challenges, such as nonuniform azimuth sampling and echo data loss, leading to azimuth ambiguities and substantially impacting image quality. This paper proposes a sparse SAR imaging method for the low-oversampled staggered mode via compound regularization. The proposed method not only effectively suppresses azimuth ambiguities arising from nonuniform sampling without necessitating the restoration of missing echo data, but also incorporates total variation (TV) regularization into the sparse reconstruction model. This enhances the accurate reconstruction of distributed targets within the scene. The efficacy of the proposed method is substantiated through simulations and real data experiments from spaceborne missions. [ABSTRACT FROM AUTHOR]

Operating mode identification is an important prerequisite for precise deceptive jamming technology against synthetic aperture radar (SAR). In order to solve the problems of traditional spaceborne SAR operating mode identification, such as low identification accuracy, poor timeliness, and limitation to main lobe reconnaissance, an efficient identification method based on sidelobe reconnaissance and machine learning is proposed in this paper. It can identify four classical SAR operating modes, including stripmap, scan, spotlight and ground moving target indication (GMTI). Firstly, the signal models of different operating modes are presented from the perspective of sidelobe reconnaissance. By setting the parameters differently, such as the SAR trajectory height, antenna length, transmit/receive gain and loss, signal–noise ratio, and so on, the feature samples based on multiple parameters can be obtained, respectively. Then, based on the generated database of feature samples, the initialized neural network can be pre-trained. As a result, in practice, with the intercepted sidelobe signal and the pre-trained network, we can precisely infer the SAR operating mode before the arrival of the main lobe beam footprint. Finally, the effect of SNR and the jammer's position on the identification accuracy is experimentally detailed in the simulation. The simulation results show that the identification accuracy can reach above 91%. [ABSTRACT FROM AUTHOR]

Geosynchronous spaceborne–airborne (GEO-SA) ultra-high-frequency ultra-wideband bistatic synthetic aperture radar (UHF UWB BiSAR) provides high-precision images for marine and polar environments, which are pivotal in glacier monitoring and sea ice thickness measurement for polar ocean mapping and navigation. Contrasting with traditional high-frequency BiSAR, it faces unique challenges, such as the considerable spatial variability, significant range–azimuth coupling, and vast volumes of echo data, which impede high-resolution image reconstruction. This paper presents an improved bistatic nonlinear chirp scaling (NLCS) algorithm for imaging oceanic scenes with GEO-SA UHF UWB BiSAR. This methodology extends the two-dimensional (2-D) spectrum up to the sixth order via the method of series reversion (MSR) to meet accuracy demands and then employs an elliptical model to elucidate the alterations in the azimuth frequency modulation (FM) rate mismatch. Initially, the imaging geometry and signal model are introduced, and then a separation of bistatic slant ranges based on the configuration is proposed. In addition, during range processing, after eliminating linear range cell migration (RCM), the derivation process for the sixth-order 2-D spectrum is detailed and an improved filter is applied to correct the high-order RCM. Finally, during azimuth processing, the causes of the FM rate mismatch are analyzed, a cubic perturbation function derived from the elliptical model is used for FM rate equalization, and a unified sixth-order filter is applied to complete the azimuth compression. Experimental results with point targets and natural oceanic scenes validate the outstanding efficacy of the proposed NLCS algorithm, particularly in imaging quality enhancements for GEO-SA UHF UWB BiSAR. [ABSTRACT FROM AUTHOR]

The deployable reflector antenna based on the synthetic aperture radar is a satellite component that consists of a unit structure in the form of a folded reflector. During the launch process, this satellite antenna is in the stowed condition to improve storage efficiency. It is then deployed to perform the space mission in the on-orbit condition. Due to these structural characteristics of the deployable reflector antenna, the reflector is possible to be loaded in the limited volume of the launch vehicle with the reduced size. Additionally, because the deployable reflector antenna is made by the lightweight material of the carbon fiber reinforced polymer and honeycomb core, it can reduce the launching cost and improve the revisit interval. In this paper, the conceptual design of the main reflector of the deployable reflector antenna was conducted. The main reflector was designed as the honeycomb sandwich composite structure. To design the main reflector, the stacking sequence of the composite material and honeycomb core was investigated to maximize the structural stiffness and minimize the antenna's mass. Subsequently, finite element analyses including modal, quasi-static, structural–thermal coupling, and transient response were performed to numerically evaluate the structural performance of the lightweight composite reflector antenna. [ABSTRACT FROM AUTHOR]

A trend in the development of spaceborne Synthetic Aperture Radar (SAR) technology is the shift from a single-satellite repeated observation mode to a multi-satellite collaborative observation mode. However, current multi-satellite collaborative geolocation algorithms face challenges, such as geometric model mismatch and poor baseline estimation accuracy, arising from highly dynamic changes among multi-satellites. This paper introduces a high-precision and efficient geolocation algorithm for a spaceborne bistatic interferometric SAR (BiInSAR) system based on an improved range–Doppler (IRD) model. The proposed algorithm encompasses three key contributions. Firstly, a comprehensive description of the spatial baseline geometric model unique to the bistatic configuration is provided, with a specific focus on deriving the perpendicular baseline expression. Secondly, IRD geolocation functions are established to meet the specific requirements of the bistatic configuration. Then, a novel BiInSAR geolocation algorithm based on the IRD's functions is proposed, which can significantly improve the target geolocation accuracy by modifying the range–Doppler equation to suit the bistatic configuration. Meanwhile, a low-coupling parallel calculation method is proposed, which can improve the calculation speed by two to three times. Finally, the accuracy and efficiency of the algorithm are demonstrated using experimental data acquired by the TH-2 satellite, which is China's first spaceborne BiInSAR system. The experimental results prove that the IRD algorithm exhibits geolocation accuracy with an average error of less than 1 m and a standard deviation of less than 2.5 m while maintaining computational efficiency at a calculation speed of 1,429,678 pixels per second. [ABSTRACT FROM AUTHOR]

Quasi-continuous wave radar is an attempt to give consideration to the performance of pulse and continuous wave radar signals. However, it also has the shortcomings of both. This paper aims to add a new quasi-continuous-wave coding method to the spaceborne synthetic aperture radar (SAR) system. The technology of improving spaceborne SAR imaging performance by coding quasi-continuous-wave pulses is studied, and some shortcomings of this algorithm are improved. Firstly, the application of quasi-continuous-wave radar in the SAR system is studied, and the coding and reconstruction scheme is provided so that this technology can be successfully applied in spaceborne SAR. Secondly, the effects of different quasi-continuous-wave coding methods on SAR imaging performance are evaluated, including signal-to-noise ratio, resolution, and integration time. Then, several coding schemes are given, and the characteristic changes of the signal after quasi-continuous-wave coding are analyzed. The transmit–receive conversion loss function and azimuth Doppler ambiguity function of the design scheme are analyzed, which proves the advantages of the scheme. Finally, we design the hardware implementation scheme and carry out the practical test. [ABSTRACT FROM AUTHOR]

The integration of deep neural networks into sparse synthetic aperture radar (SAR) imaging is explored to enhance SAR imaging performance and reduce the system's sampling rate. However, the scarcity of training samples and mismatches between the training data and the SAR system pose significant challenges to the method's further development. In this paper, we propose a novel SAR imaging approach based on deep image prior powered by RED (DeepRED), enabling unsupervised SAR imaging without the need for additional training data. Initially, DeepRED is introduced as the regularization technique within the sparse SAR imaging model. Subsequently, variable splitting and the alternating direction method of multipliers (ADMM) are employed to solve the imaging model, alternately updating the magnitude and phase of the SAR image. Additionally, the SAR echo simulation operator is utilized as an observation model to enhance computational efficiency. Through simulations and real data experiments, we demonstrate that our method maintains imaging quality and system downsampling rate on par with deep-neural-network-based sparse SAR imaging but without the requirement for training data. [ABSTRACT FROM AUTHOR]

The multiple-input multiple-output synthetic aperture radar (MIMO SAR) system has developed rapidly since its discovery. At the same time, the low-disturbance and high-gain requirements of the MIMO system are continuing to increase. Through the application of digital beamforming (DBF) techniques, the multidimensional waveform encoding (MWE) technique can play a key role in MIMO systems, which can greatly improve the system's performance, especially the multi-mission capability of radar. Intrapulse beamsteering in elevation is a typical form of multi-dimensional waveform encoding which can greatly improve the transmitting efficiency and multi-mission performance of radar. However, because of the high sensitivity of the DBF technique to height, there is significant deterioration in performance in the presence of terrain undulations. The OFDM (Orthogonal Frequency Division Multiplexing) technique is widely used in communication. Due to the similarity of radar and communication systems and the great waveform diversity of OFDM signals, the OFDM radar has recently begun to emerge as a new radar system, simultaneously, the orthogonality of OFDM signals is in the time and frequency domains, and is not affected by terrain undulation. So, this paper proposes a novel radar mode combining intrapulse beamsteering in elevation and OFDM-Chirp signals, that is, the combination of "beam orthogonality" and "waveform orthogonality", which can greatly improve the performance and fault tolerance to interference signals. In this manuscript, the system working mode and signal processing flow are introduced in detail, and simulations for both point targets and distributed targets are carried out to verify the feasibility of the proposed mode. Simultaneously, a comparison experiment is carried out, which shows the high level of fault tolerance to terrain undulation and the high potential of the proposed radar mode in Earth observation. [ABSTRACT FROM AUTHOR]

Multistatic synthetic aperture radar (SAR) is a special mode of SAR system. The radar transmitter and receiver are located on different satellites, which brings many advantages, such as flexible baseline configuration, diverse receiving modes, and more detailed ground object classification information. The multistatic SAR has been widely used in interferometry, moving target detection, three-dimensional imaging, and other fields. The frequency offset between different oscillators will cause a modulation phase error in the signal. Therefore, phase synchronization is one of the most critical problems to be addressed in distributed SAR systems. This article reviews phase synchronization techniques, which are mainly divided into two methods: synchronization by direct microwave link and synchronization by a data-based estimation algorithm. Furthermore, the future development of synchronization technology is anticipated. [ABSTRACT FROM AUTHOR]

Spaceborne synthetic aperture radar (SAR) is an advanced microwave imaging technology that provides all-weather and all-day target information. However, as spaceborne SAR resolution improves, traditional echo signal models based on airborne SAR design become inadequate due to the curved orbit, Earth rotation, and increased propagation distance. In this study, we propose an accurate range model for high-resolution spaceborne SAR by analyzing motion trajectory and Doppler parameters from the perspective of the space geometry of spaceborne SAR. We evaluate the accuracy of existing range models and propose an advanced equivalent squint range model (AESRM) that accurately fits the actual range history and compensates for high-order term errors by introducing third-order and fourth-order error terms while maintaining the simplicity of the traditional model. The proposed AESRM's concise two-dimensional frequency spectrum form facilitates the design of imaging algorithms. Point target simulations confirm the effectiveness of the proposed AESRM, demonstrating significant improvements in fitting accuracy for range histories characterized by nonlinear trajectories. The developed AESRM provides a robust foundation for designing imaging algorithms and enables higher resolution and more accurate radar imaging. [ABSTRACT FROM AUTHOR]

Due to a unique imaging mechanism, Synthetic Aperture Radar (SAR) images typically exhibit degradation phenomena. To enhance image quality and support real-time on-board processing capabilities, we propose a lightweight deep generative network framework, namely, the Lightweight Super-Resolution Generative Adversarial Network (LSRGAN). This method introduces Depthwise Separable Convolution (DSConv) in residual blocks to compress the original Generative Adversarial Network (GAN) and uses the SeLU activation function to construct a lightweight residual module (LRM) suitable for SAR image characteristics. Furthermore, we combine the LRM with an optimized Coordinated Attention (CA) module, enhancing the lightweight network's capability to learn feature representations. Experimental results on spaceborne SAR images demonstrate that compared to other deep generative networks focused on SAR image super-resolution reconstruction, LSRGAN achieves compression ratios of 74.68 % in model storage requirements and 55.93 % in computational resource demands. In this work, we significantly reduce the model complexity, improve the quality of spaceborne SAR images, and validate the effectiveness of the SAR image super-resolution algorithm as well as the feasibility of real-time on-board processing technology. [ABSTRACT FROM AUTHOR]

Lithium mining has become a controversial issue in the transition to green technologies due to the intervention in natural basins that impact the native flora and fauna in these environments. Large resources of this element are concentrated in Andean salt flats in South America, where extraction is much easier than in other geological configurations. The Pozuelos highland salt flat, located in northern Argentina (Salta's Province), was chosen for this study due to the presence of different evaporitic crusts and its proven economic potential in lithium-rich brines. A comprehensive analysis of a 5.5-year-long time series of its microwave backscatter with Synthetic Aperture Radar (SAR) images yielded significant insights into the dynamics of their crusts. During a field campaign conducted near the acquisition of three SAR images (Sentinel-1, ALOS-2/PALSAR-2, and SAOCOM-1), field measurements were collected for computational modeling of the SAR response. The temporal backscattering coefficients for the crusts in the salt flat are directly linked to rainfall events, where changes in surface roughness, soil moisture, and water table depth represent the most critical variables. Field parameters were employed to model the backscattering response of the salt flat using the Small Slope Approximation (SSA) model. Salt concentration of the subsurface brine and the water table depth over the slightly to moderately roughed crusts were quantitatively derived from Bayesian inference of the ALOS-2/PALSAR-2 and SAOCOM-1 SAR backscattering coefficient data. The results demonstrated the potential for subsurface estimation with L-band dual-polarization images, constrained to crusts compatible with the feasibility range of the layered model. [ABSTRACT FROM AUTHOR]

Exploring the deformation mechanism of the 2021 Mw 7.4 Maduo Earthquake is crucial for better understanding the seismic hazard of the faults with low strain rates inside the Bayan Har block. This study leverages deformation information derived from Sentient-1 A/B images and GPS data to investigate in detail the co- and postseismic deformation mechanisms using multiple methods. The main results are as follows. First, the postseismic InSAR time series robustly identified the reactivation of the Changmahe fault, indicating the impact of the Maduo event on surrounding active faults. Second, the joint inversion of Interferometric Synthetic Aperture Radar and GPS revealed that (1) there was a complementary and partially overlapping relationship between the coseismic slip and postseismic afterslip of the main rupture; and (2) the Changmahe fault exhibited thrust compression dislocation in the early stage and experienced a sustained compressive effect from afterslip in the one year after the mainshock. Third, modeling the processes of viscoelastic relaxation and poroelastic rebound revealed that the postseismic deformation was probably caused by a combination of afterslip (near-field) and viscoelastic relaxation (near and far field). Fourth, the stress changes driven by the Maduo event revealed that the seismic gaps inside the Maqin-Maqu segment and the Kunlun Pass-Jiangcuo fault will be potential seismic risks in the future. [ABSTRACT FROM AUTHOR]

The unique imaging modality of synthetic aperture radar (SAR) has posed significant challenges for object detection, making it more complex to acquire and interpret than optical images. Recently, numerous studies have proposed cross-domain adaptive methods based on convolutional neural networks (CNNs) to promote SAR object detection using optical data. However, existing cross-domain methods focus on image features, lack improvement on input data, and ignore the valuable supervision provided by few labeled SAR images. Therefore, we propose a semi-supervised cross-domain object detection framework that uses optical data and few SAR data to achieve knowledge transfer for SAR object detection. Our method focuses on the data processing aspects to gradually reduce the domain shift at the image, instance, and feature levels. First, we propose a data augmentation method of image mixing and instance swapping to generate a mixed domain that is more similar to the SAR domain. This method fully utilizes few SAR annotation information to reduce domain shift at image and instance levels. Second, at the feature level, we propose an adaptive optimization strategy to filter out mixed domain samples that significantly deviate from the SAR feature distribution to train feature extractor. In addition, we employ Vision Transformer (ViT) as feature extractor to handle the global feature extraction of mixed images. We propose a detection head based on normalized Wasserstein distance (NWD) to enhance objects with smaller effective regions in SAR images. The effectiveness of our proposed method is evaluated on public SAR ship and oil tank datasets. [ABSTRACT FROM AUTHOR]

Spaceborne synthetic aperture radar (SAR) has been proven to be a useful technique for observing the sea surface wind and current over the open ocean given its all-weather data-gathering capability and high spatial resolution. In addition to the commonly used radar return magnitude quantified by normalized radar cross section (NRCS), the Doppler centroid anomaly (DCA) has added another dimension of information. In this study, we combine the NRCS and DCA for a joint inversion of wind and surface current information using a Bayesian method. SAR-estimated Doppler is corrected by a series of steps, including the removal of scalloping effect and land correction. The cost function of this inversion scheme is constructed based on NRCS, DCA, and a background model wind. The retrieved wind results show the quality of performance through comparison with the in situ buoy measurements, showing a mean bias and a root-mean-square error (RMSE) of 0.33 m/s and 1.45 m/s for wind speed and 6.94° and 35.74° for wind direction, respectively. The correlation coefficients for wind speed and direction reach 0.931 and 0.661, respectively. Based on the obtained wind field, the line-of-sight velocity of the sea surface current is then derived by removing the wind contribution using the empirical model. The results show a consistent spatial pattern relative to the high-frequency radars, with the comparison relative to the drifter-measured current velocity exhibiting a mean bias of 0.02 m/s and RMSE of 0.32 m/s, demonstrating the reliability of the proposed inversion scheme. Such results will serve as a prototype for future spaceborne sensors to combine the radar return and Doppler information for the joint retrieval of wind vector and surface current velocity. This technique could be readily extended to the radar configuration of rotating beams for monitoring winds and current vectors. [ABSTRACT FROM AUTHOR]

Multi-moving-target imaging in a synthetic aperture radar (SAR) system poses a significant challenge owing to target defocusing and being contaminated by strong background clutter. Aiming at this problem, a new deep-convolutional-neural-network (CNN)-assisted method is proposed for multi-moving-target imaging in a SAR-GMTI system. The multi-moving-target signal can be modeled by a multicomponent LFM signal with additive perturbation. A fully convolutional network named MLFMSS-Net was designed based on an encoder–decoder architecture to extract the most-energetic LFM signal component from the multicomponent LFM signal in the time domain. Without prior knowledge of the target number, an iterative signal-separation framework based on the well-trained MLFMSS-Net is proposed to separate the multi-moving-target signal into multiple LFM signal components while eliminating the residual clutter. It works well, exhibiting high imaging robustness and low dependence on the system parameters, making it a suitable solution for practical imaging applications. Consequently, a well-focused multi-moving-target image can be obtained by parameter estimation and secondary azimuth compression for each separated LFM signal component. The simulations and experiments on both airborne and spaceborne SAR data showed that the proposed method is superior to traditional imaging methods in both imaging quality and efficiency. [ABSTRACT FROM AUTHOR]