Search Results (551)

Spaceborne Synthetic Aperture Radar (SAR) provides all-weather, day and night and wide-area imaging capability, and plays a critical role in maritime surveillance. While substantial progress has been achieved in SAR ship detection, SAR ship classification remains relatively underexplored, mainly due to the scarcity of reliable category labels. Automatic Identification System (AIS) provides vessel identity, type, and dynamic trajectory information, and thus offers vessel type information that is difficult to obtain directly from SAR imagery. This paper proposes a fine-grained nine-category SAR ship classification method based on AIS association, which reorganizes the original AIS vessel types into nine fine-grained categories of SAR ship, transfers AIS vessel type information to SAR detection through a global optimal matching strategy, and supports SAR-only vessel category recognition. By retaining only high-confidence SAR and AIS matched pairs and cropping the corresponding SAR ship chips, an SAR ship classification dataset containing 4472 ship chips across the nine categories is constructed. In Monte Carlo experiments based on real AIS records, the proposed association strategy achieves more reliable high-confidence label generation than the compared association methods under close ship ambiguity, spatial perturbation, distractor AIS candidates, and AIS static size errors. In the benchmark experiment on the constructed classification dataset, ConvNeXt-Tiny achieves the best performance among the compared mainstream classifiers. These results demonstrate that AIS association can provide reliable category supervision for SAR ship classification, and the trained classifier can perform ship classification using SAR imagery alone. Full article

Passive reception of spaceborne synthetic aperture radar (SAR) signals is of great significance for acquiring target characteristics and identifying SAR operating states. With the rapidly growing demand for high-quality SAR signal reception, signal-receiving arrays are prone to beam performance deterioration and difficulty in beamforming under wide-angle scanning conditions. Traditional uniform arrays fail to meet practical engineering requirements and cannot balance multiple conflicting performance indicators. To address the above technical bottlenecks, this paper proposes a design method of a non-uniform planar receiving array based on the MOEA/D-HPSO algorithm. Taking maximum sidelobe level (MSL), array gain (G), and beamwidth (BW) as core performance indicators, a multi-objective optimization model of SAR signal-receiving array for wide-angle scanning is established. This method integrates the multi-objective decomposition strategy and hybrid genetic particle swarm optimization mechanism, decomposes complex multi-objective problems into several scalar subproblems, obtains uniformly distributed Pareto fronts, and effectively improves the diversity of solution sets. Simulation experimental results show that the proposed algorithm is superior to traditional mainstream algorithms such as NSGA-II and MOEA/D-DE in terms of convergence accuracy, solution set distribution, and various performance indicators. Typical array design examples verify that the proposed method can adapt to various engineering application scenarios and provide technical support for spaceborne SAR signal reception and spectrum management. Full article

Accurate characterization of mining-induced surface subsidence is essential for safety assessment in mining areas; however, single monitoring techniques have inherent limitations. Spaceborne interferometric synthetic aperture radar (InSAR) provides large-area coverage but suffers from low signal-to-noise ratio in the subsidence center, whereas terrestrial laser scanning offers high accuracy but limited spatial coverage. To achieve physically consistent quantitative fusion, a multi-source subsidence fusion framework based on variational data assimilation is proposed. By constructing an objective function that incorporates a background prior, D-InSAR-derived boundary constraints, TLS observations, spatial smoothness constraints, and gradient penalty terms, multi-source data are integrated into a unified optimization framework. The results show that, compared with RTK observations, the fused subsidence field achieves an RMSE of 0.12 m and an RRMSE of 2.4% approximately. Parameter sensitivity analysis indicates that the smoothing strength has the greatest influence on fusion accuracy, whereas the observation weight and gradient penalty coefficient exhibit relatively wide stable intervals, and the background constraint has a minor effect on the results. Parameter interaction analysis further demonstrates that the coupling between smoothing strength and observation weight is the most significant. The proposed method provides a physically consistent and parameter-controllable framework for multi-source deformation data fusion in mining subsidence monitoring. Full article

The essential role of electricity supply for public and private services highlights the need to monitor the stability of power transmission networks during, or immediately after, hazardous events. In the aftermath of calamities, traditional field inspections may be impractical or unsafe, leaving operators without timely information on the condition of critical assets. In this paper, we present and discuss the performance of two automatic Artificial Intelligence (AI)-based models (Multi-Layer Perceptron (MLP) neural network architectures and Support Vector Machine (SVM) model) designed to automatically assess the status of high-voltage transmission towers and power lines through multi-temporal spaceborne Synthetic Aperture Radar (SAR) image analysis. Model development and testing rely on real COSMO-SkyMed Stripmap observations of damaged towers and power lines affected by documented hazardous events across Italy, complemented by simulated tower data generated with a physics-guided, signature-based SAR simulator designed to preserve the observed target-to-background contrast and spatial footprint patterns of real SAR tower signatures. Results indicate that the MLP, trained on either real or simulated data, achieved 100% Overall Accuracy (OA) with no observed false positives or false negatives within the considered visibility-screened real test set, while providing inference times on the order of tenths of milliseconds per target… Computational performance characteristics, operational advantages, and the potential pathway toward satellite on-board porting are discussed to enhance situational awareness and support the prioritisation of interventions during critical events. Full article

Spaceborne interrupted frequency-modulated continuous-wave synthetic aperture radar (iFMCW SAR) employs a single antenna on a single spacecraft operating in a time-division transmit/receive mode, effectively avoiding mutual interference between transmitted and received signals and thereby overturning the design paradigm of spaceborne FMCW SAR systems. However, the periodic switching of the antenna between transmit and receive states results in periodic data gaps along the azimuth direction in the echo signal, leading to spurious artifacts in the reconstructed images and severely degrading image quality. Sparse signal recovery techniques based on compressive sensing models have been shown to effectively suppress such spurious targets. Nevertheless, the generalized orthogonal matching pursuit (GOMP) algorithm requires prior knowledge of the signal sparsity, a condition that is often impractical in real-world scenarios. To address this limitation, this paper investigates the variation pattern of the residual norm with respect to sparsity in the GOMP algorithm and proposes a modified GOMP algorithm based on binary search. This approach enables rapid and accurate determination of the true sparsity level without prior knowledge, thereby achieving sparsity-adaptive reconstruction with GOMP and significantly enhancing the imaging quality of iFMCW SAR. Simulation experiments involving both point and scene targets are provided to demonstrate the effectiveness and potential of the proposed algorithms for practical applications. Full article

Small unmanned aerial vehicle (UAV)-borne distributed tomographic synthetic aperture radar (TomoSAR) systems offer flexible baseline configurations and low deployment cost, making them attractive for rapid and high-resolution three-dimensional (3D) reconstruction. However, the distance between adjacent channels placed on different UAVs is relatively large due to the flight safety spacing considerations. This leads to high sidelobes in the elevation point spread function (PSF) within the reconstruction range. Meanwhile, atmospheric turbulence may cause UAVs to deviate from their predefined trajectories, making it difficult to suppress sidelobes through baseline optimization. Large baselines may also introduce spatial decorrelation between channels, which gives rise to random phase noise in the interferometric phase and further aggravates elevation ambiguity by increasing the sidelobe level of the PSF. To address this problem, this paper proposes an elevation ambiguity resolution method based on neighborhood-adaptive elevation priors. In the proposed method, a window function is constructed from reconstruction results of neighboring pixels and incorporated into the reconstruction process to suppress the interference caused by high sidelobes. In this way, the probability of correct target reconstruction is improved. The effectiveness and robustness of the proposed method are validated using both simulations and real measured data. Experimental results obtained with a C-band small UAV-borne distributed TomoSAR system show that the proposed method effectively suppresses ambiguity and enables ambiguity-free reconstruction of target buildings. Statistical analysis further demonstrates that the number of ambiguous points produced by the proposed algorithm is only one-fifth of that produced by the conventional OMP method. Full article

The acquisition of spaceborne tomographic data usually requires a longer period of time due to the satellite’s long revisit period. To address this issue, it is possible to leverage the similarity of neighboring pixels in order to perform tomographic reconstruction of building targets in urban areas. As a result, the insufficient number of samples can be approximately substituted by pixels with similar scattering characteristics. However, the current utilization of building structures is often limited to horizontal characteristics such as contour lines (CL); in addition, extraction methods either rely on external data as prior information or are constrained by the need to fit operations, which limits the shape of the contour lines. This paper proposes a spaceborne tomographic reconstruction method based on building characteristics, starting from data and fully utilizing the horizontal and vertical characteristics of buildings for reconstruction. First, interferometric information is used to assist in tomographic processing and a strategy combining multi-point growth with multi-level fusion is employed to extract contour lines. Additionally, the vertical characteristics of buildings are established to provide constraints on the solution space for tomographic processing. The three-dimensional reconstruction of isolated and vertical buildings is then achieved by combining signal elimination techniques. By more fully exploiting the structural characteristics of buildings, the proposed method is capable of recovering building structures even with a limited number of samples. The effectiveness of the proposed method is validated through simulated data and TerraSAR-X data. Full article

Geological hazards induced by large-scale and high-intensity mining activities worldwide are primary drivers of regional ecological degradation and pose significant threats to human safety and property. To construct efficient monitoring systems and enhance early warning capabilities, it is essential to clarify the formation mechanisms of various hazards and the suitability of corresponding technologies. Focusing on five typical geological hazards prevalent in mining areas (surface subsidence, ground fissures, landslides, collapses, and sinkholes), this paper characterizes their specific features and monitoring requirements. It systematically analyzes the physical principles, accuracy levels, and technical advantages and limitations of ground-based, aerial, and spaceborne monitoring, as well as multi-source remote sensing data fusion and emerging technologies (e.g., distributed optical fiber, light detection and range, microseismical monitoring, and deep learning). Utilizing case studies from an open-pit coal mine in Turkey and a loess gully mining area in China, the paper evaluates the effectiveness of methods like multi-temporal InSAR and UAV photogrammetry in identifying the evolution of these hazards. The findings indicate that the technological framework for mining area monitoring is transitioning from single-method approaches to integrated systems. However, given the complex mining environment, several bottleneck challenges remain, including single data dimensions, the limited environmental adaptability of aerospace remote sensing, insufficient stability of deep monitoring equipment, and weak anti-interference capabilities under extreme operating conditions. Consequently, this paper proposes that future innovations in geological hazard monitoring in mining areas will focus on multi-platform hierarchical collaboration, the development of multi-parameter fusion early warning criteria, and the construction of digital and visual platforms. Constructing a comprehensive monitoring system characterized by multi-scale collaboration and dynamic prediction capabilities is vital for improving safety standards in mining areas and achieving coordinated development between resource exploitation and environmental protection. The findings provide a theoretical foundation for the precise prevention and control of mining hazards, as well as for land ecological restoration. Full article

Accurate absolute radiometric calibration is critical for ensuring the data quality of spaceborne Synthetic Aperture Radar (SAR) systems and supporting quantitative remote sensing applications. Absolute radiometric calibration generally relies on ground reference targets with known radar cross-section (RCS) deployed at dedicated calibration sites. Such ground-based calibration methods are costly and time-consuming, and calibration frequency is constrained by the distribution of calibration sites and the satellite revisit cycles. Additionally, for specialized SAR missions, such as deep space exploration, deploying calibration equipment on the observed extraterrestrial surface is infeasible. This study proposes a space-based absolute calibration concept using a small calibration satellite carrying a well-characterized reference (e.g., a passive reflector or an active transponder) and flying in formation with the SAR satellite. The relative motion ensures a side-looking acquisition geometry, enabling the SAR to image the accompanying target and derive calibration factors. The overall calibration process is divided into two stages: determination of an in-orbit calibration factor using the calibration satellite, followed by its transformation to accommodate ground imaging conditions. This method effectively isolates the radar system gain to characterize the intrinsic hardware response. Furthermore, by operating entirely in space, it avoids atmospheric and ground-clutter distortions, ensuring a fully space-based, end-to-end calibration process dominated primarily by sensor systematic errors. Moreover, it allows for more frequent and flexible calibration, eliminating reliance on ground calibration sites and infrastructure. The feasibility and advantages of the proposed concept are demonstrated through comprehensive simulations, covering orbit analysis, echo simulation, and image processing. Full article

Synthetic Aperture Radar (SAR) ship target detection is crucial for marine monitoring, offering vital support for maritime security, navigation safety, and environmental surveillance. However, deploying advanced deep learning models on resource-constrained edge devices like UAVs and spaceborne platforms is challenging due to the high computational complexity and large parameter counts, hindering real-time performance. To address this, we propose the HGXES model, a lightweight SAR ship detection network. This model integrates efficient structural design, feature enhancement mechanisms, and an attention mechanism to reduce computational costs while preserving feature extraction capabilities. It employs factorized convolutions, a cross-level feature reuse module, and an attention mechanism to dynamically adjust feature weights, enhancing sensitivity to ship targets. A lightweight detection head ensures rapid and accurate target classification and localization. Experiments on benchmark SAR datasets show that based on the lightweight HGNetV2 backbone, our incremental designs (Xfeat, ELA, LWDetect) further compress the model and achieve a 70% reduction in parameters compared with traditional models, with a model size of just 1.9 MB, 2.3 M parameters, and 3.9 G FLOPs, achieving 49.7 fps detection speed. Comparative analyses reveal the superiority of the ELA attention mechanism and ShapeIoU loss function in enhancing performance. Thus, the HGXES model successfully achieves lightweight SAR ship detection, supporting real-time marine monitoring on resource-limited platforms with high accuracy and reduced computational costs. Full article

Ocean surface currents (OSCs) are central to upper ocean dynamics and air–sea exchange, yet their retrieval from spaceborne synthetic aperture radar (SAR) is limited by wave-induced bias (WB). WB arises from the inherent motion of the scattering facets and from long-wave hydrodynamic and tilt modulations, and is therefore jointly controlled by sea state and radar viewing geometry. This study develops an observation geometry optimization framework. Five years of hourly ERA5 wind and wave reanalysis data over the Kuroshio are used as a representative ensemble of sea states to drive the KaDOP model, and an exhaustive grid search over line-of-sight (LOS) azimuth (0–360°) and incidence angle (20–60°) is performed to identify, for each location and season, the viewing geometry that minimizes the time-mean WB. These local optima are then summarized as mission-level metrics, including the minimum achievable WB, the coverage meeting prescribed WB thresholds, and the spatial coherence of the preferred LOS azimuth and incidence angle. Finally, the theoretical minima are compared with the fixed left-looking geometry of the Luojia-2 (LJ-2) satellite along a 213 km × 6 km observation corridor and with Gaofen-3 (GF-3) viewing geometries at four representative locations in the Kuroshio. Across these validation cases, the optimized geometry reduces mean absolute WB by about 20–60% for LJ-2 and 20–80% for GF-3, providing quantitative constraints for future SAR mission design targeting OSCs. Full article

Earthquake-induced landslides in active orogens such as the Nepal Himalaya pose severe threats to lives, infrastructure, and post-disaster recovery. While machine learning (ML) and deep learning (DL) approaches to coseismic landslide susceptibility mapping have advanced considerably, spaceborne interferometric synthetic aperture radar (InSAR) products, particularly line-of-sight (LOS) displacement and coherence-based damage proxy maps (DPMs), remain underutilized in event-based frameworks. This study develops and evaluates a multi-factor coseismic landslide probability model that integrates InSAR-derived deformation metrics with geomorphic and hydrologic predictors to support rapid post-earthquake hazard assessment. Using the 25 April 2015 Mw 7.8 Gorkha earthquake as a case study, LOS displacement was derived from ALOS-2 PALSAR-2 ScanSAR interferometry, and the normalized channel steepness index (K_sn) was computed from a digital elevation model. Fourteen conditioning factors were used to train five architectures: Random Forest (RF), XGBoost, CNN, U-Net, and DeepLabV3. Spatial autocorrelation was mitigated using a leave-one-basin-out three-fold spatial cross-validation strategy, with models evaluated on a patch-based domain comprising 655,360 pixels at a positive-class prevalence of 6.35%, establishing a no-skill AUC-PR baseline of 0.0635. InSAR integration consistently improved model performance under high class imbalance, increasing AUC-PR across all models by 7.8% to 17.3%. Random Forest achieved the highest AUC-PR (0.7940, nearly 12.5 times the baseline) and CSI (0.3027), providing the best balance between landslide recall (88.09%) and non-landslide specificity (88.68%) with the lowest false alarm rate (11.32%). XGBoost attained the highest AUC-ROC (0.9501) but exhibited lower recall (83.73%) and poorer calibration (Brier = 0.1397). Among DL models, DeepLabV3 produced the best-calibrated probabilities (Brier = 0.0693) and the highest CSI (0.2307), while U-Net offered the most balanced DL performance and CNN achieved the highest recall (92.40%) at the expense of elevated false alarms. Permutation feature importance identified K_sn as the dominant predictor, highlighting the strong tectono-geomorphic control on coseismic landslide occurrence. These results demonstrate that integrating InSAR-derived products substantially enhances landslide hazard assessment and supports more reliable rapid response in the Nepal Himalaya. Full article

The resolution and accuracy of airborne/spaceborne SAR are continuously improving, making it an effective means for observing ocean dynamic processes and detecting marine targets. In contrast, utilizing its unique orbital angular momentum (OAM) mode, vortex radar does not require temporal accumulation to achieve azimuthal resolution, making it particularly suitable for observing moving sea surfaces. This capability enables stable and continuous monitoring of dynamic ocean scenes. This paper proposes a vortex radar imaging method based on three-dimensional sea surface scattering characteristics: first, a three-dimensional wind-driven sea surface geometric model is established based on the Elfouhaily sea spectrum, and its scattering characteristics under different incident angles, wind speeds, and wind directions are analyzed using the semi-deterministic facet-based two-scale method; then, two-dimensional range-azimuth imaging is achieved through coordinate transformation, echo modeling, pulse compression, and fast Fourier transform (FFT) in OAM mode domain, with the correctness of the imaging algorithm verified through multiple point target imaging results. Finally, simulation results of two-dimensional sea surface vortex imaging under different incident angles are presented, and the influence of wind speed and direction on sea surface vortex imaging is analyzed. The study shows that the vortex imaging system can effectively reflect wave fluctuations and wind direction characteristics, demonstrating the feasibility and potential of vortex radar imaging in oceanographic applications. Full article

The accuracy of Digital Elevation Models (DEMs) plays a crucial role in determining their reliability for geoscientific and engineering applications. Next-generation distributed interferometric synthetic aperture radar (SAR) constellations, such as the PIESAT-1 wheel constellation with its “one primary, three secondary” setup, provide a novel method for efficiently acquiring high-precision DEMs. However, a comprehensive and systematic performance evaluation of DEMs derived from such an innovative constellation is lacking, particularly in the context of comparative studies under complex terrain conditions. This study uses PIESAT-1 SAR imagery to generate a 10 m resolution DEM through multi-baseline interferometric processing. The ICESat-2 ATL08 dataset serves as the reference baseline, and mainstream products, including ZY-3, GLO-30, TanDEM-X DEM, and AW3D30, are incorporated for a multidimensional vertical accuracy evaluation, considering land cover, slope, aspect, and topographic profiles. The results indicate that, in three representative mountainous regions, the PIESAT-1 DEM achieves optimal overall accuracy (RMSE = 3.25 m). Furthermore, in regions with significant radar geometric distortions, such as south-facing slopes, vegetation-covered areas, and regions with noticeable anthropogenic topographic changes, the PIESAT-1 DEM demonstrates superior stability and information capture capabilities relative to conventional single- or dual-baseline SAR systems. This study validates the technological potential of the PIESAT-1 wheel constellation in enhancing DEM accuracy and terrain adaptability, and provides insights for the scientific selection of high-resolution topographic data and the design of future spaceborne interferometric missions. Full article

Tropospheric delays can be the leading source of error in spaceborne interferometric synthetic aperture radar (InSAR) measurements. Here, we find that the non-uniform troposphere delay features are dependent on the squint angles used for repeat-pass InSAR data acquisitions. Large squint angles cause large along-track shifts in these non-uniform troposphere delay features. By processing the airborne L-band uninhabited aerial vehicle SAR (UAVSAR) data with three different squint angles, we were able to see various non-uniform delay structures of different sizes with varying delays of up to a few centimeters across the observed interferograms. We were also able to estimate the altitude of the effective troposphere delay layers. The understanding of the squint-dependent troposphere delay can help us separate the surface deformation component from the atmosphere delay component in the InSAR phase measurements. A number of methods are proposed for this separation. We used the UAVSAR data and simulated surface deformations to verify these methods. This technique can also be used for spaceborne cases. Full article