Search Results (95)

Conventional inverse synthetic aperture radar (ISAR) imaging techniques can produce high-resolution imagery from complete observation data. However, in practical scenarios, incomplete data caused by undersampling or missing data often leads to defocused results with traditional methods. While compressive sensing or low-rank reconstruction approaches have been proposed to address this challenge, existing techniques frequently fail to fully exploit both the sparsity and low-rank properties inherent in ISAR scenes. Moreover, they typically rely on convex approximations that introduce estimation bias, weaken sparsity promotion, and increase computational complexity, ultimately degrading imaging performance. To overcome these limitations, this work presents an enhanced sparse ISAR imaging method that jointly enforces non-convex sparsity and low-rank constraints for incomplete data recovery. The imaging model incorporates both inherent sparsity priors and a low-rank constraint. The resulting non-convex optimization problem is solved via an efficient iterative algorithm based on the alternating direction method of multipliers, where the sparse component is reconstructed using an iterative reweighted scheme with a regularizer and the low-rank component is recovered through truncated singular value decomposition. Experimental results on both simulated and measured data demonstrate the efficacy and superior performance of the proposed method. Full article

Synthetic aperture radar (SAR) provides all-weather imaging but faces challenges in visual interpretation due to low contrast and coherent speckle noise. To address contrast deficiency and edge blurring in SAR-to-optical translation, we propose a Dual-Scale Aligned Network (DSAN) built upon Pix2PixHD. First, an asymmetric dual-prior architecture is designed: the global generator ingests low-resolution SAR images enhanced by histogram equalization to capture macroscopic structures, while the local generator utilizes original high-resolution SAR images to preserve microscopic details, alleviating the trade-off between contrast and fine textures. Second, a Dual-Scale Fusion Module (DSFM) coupling Large Kernel Attention and Collaborative Attention breaks scale barriers, enabling bidirectional cross-scale alignment and deep fusion. Third, a continuous differentiable soft-edge loss is formulated using logarithmic dynamic range compression to prevent highlights from dominating gradients and enforce boundary consistency across urban areas, water bodies, and farmlands. Experiments on the Nanjing and public SEN1-2 datasets demonstrate that DSAN outperforms state-of-the-art models—including Pix2PixHD, CycleGAN, MSTMNet, and ICMA—in perceptual distribution realism (FID) with the sharpest geometric boundaries. Ablation studies confirm the effectiveness of the asymmetric dual-prior design, DSFM, and the refined edge loss. Full article

Conventional synthetic aperture radar (SAR) imaging algorithms establish the echo signal model based on the exact hyperbolic range equation and compensate for the range-azimuth coupling through complicated approximations to obtain high-resolution SAR images. Consequently, they incur high hardware requirements and implementation costs for real-time SAR imaging. This paper proposes an approximate SAR echo signal model based on two-dimensional block partitioning. Within each data block, the hyperbolic range equation is fitted with a straight line. Provided that a given accuracy requirement is met, the range-azimuth coupling inside the data block is neglected, thereby simplifying the range cell migration correction (RCMC). Based on this model, the two-dimensional matrix-form processing is decomposed into two one-dimensional vector-form processing steps. Through subband division, azimuth compression is decomposed into multiple short fast Fourier transform (FFT) operations at reduced sampling rates. The proposed algorithm reduces the computational load and storage requirements while ensuring imaging performance, thereby lowering the resource demands for real-time SAR imaging. After introducing the working principle and imaging model of SAR, this paper establishes an approximate SAR echo model based on two-dimensional block partitioning and analyzes its approximation errors; presents a subband imaging workflow; discusses the algorithm’s computational complexity, memory requirements, and valid parameter range; and verifies the algorithm’s effectiveness by processing both point target simulation data and real data. Full article

Tunnel lining defects pose significant threats to structural integrity and operational safety. Traditional image processing and machine learning methods often suffer from limited accuracy and poor generalization under complex backgrounds. To address these limitations, this study proposes a lightweight intelligent recognition method based on You Only Look Once version 11 nano (YOLOv11n) for Ground Penetrating Radar (GPR) images of tunnel linings. The backbone is replaced with Mobile Network Version 3 (MobileNetV3) to reduce parameters and Floating Point Operations (FLOPs), while depthwise separable convolution and a streamlined Compressed 2-Stage Fused-Lite (C2f-Lite) structure are integrated into the Neck to further decrease computational overhead. Channel mapping layers are employed to ensure smooth feature transfer, and selective use of Squeeze-and-Excitation (SE) attention and Hard-Swish (H-swish) activation balances detection accuracy with efficiency. Evaluated on a low-power mobile workstation acting as an edge-precursor proxy platform, experimental results demonstrate that the improved YOLOv11n_MobileNetV3 model achieves high accuracy with a mean Average Precision (mAP) at 0.5 of 94.4% and mAP@0.5:0.95 of 62.4%, low computational cost of 4.7 Giga Floating Point Operations (GFLOPs), and fast inference speed of 45 Frames Per Second (FPS). Comparative analysis further confirms its superior balance of detection performance and efficiency over YOLO version 5 (YOLOv5) and YOLO version 8 (YOLOv8) baselines. The proposed approach provides a highly optimized, edge-oriented engineering solution for real-time tunnel lining defect inspection, establishing strong structural and theoretical feasibility for future deployment in embedded systems. Full article

Near-field multistatic radar imaging requires evaluating a nonlinear matched-filter operator over a three-dimensional search region, imposing a prohibitive computational burden on systems utilizing sparse, large-aperture receiver layouts. In this paper, we study a static-target formulation with a known signal envelope and develop a receiver-domain-decomposition for computation burden mitigation. Starting from a maximum-likelihood model, we show that when the temporal waveform is known, the estimation problem reduces to a coherent spatial matched filter formed from time-compressed data. This representation enables a direct comparison between brute-force image formation and an approximation in which the receiver set is partitioned into subapertures, low-resolution subimages are computed on a coarse spatial grid, corrected by a reference phase, interpolated to the fine grid, and coherently aggregated. We derive the matched-filter formulation, provide interpolation-based error bounds under compensated-image smoothness assumptions, and analyze computational complexity. Numerical simulations demonstrate that phase correction substantially smooths low-resolution block images, thereby enabling interpolation. The results also clarify the conditions under which the proposed approximation is accurate and where it is expected to degrade, including insufficient phase compensation, overly aggressive coarse-grid factors, and extended-target interference. Full article

Geosynchronous spaceborne–airborne frequency-modulated continuous-wave bistatic synthetic aperture radar (GEO SA FMCW BiSAR) offers cost-effective and persistent target monitoring. However, both the maneuvers of the receiver during the signal propagation delay and the continuous movements of the radar platforms within the sweep complicate the received echo signal. These factors invalidate the “stop-and-go” assumption, which presumes constant-velocity motion. This paper proposes an echo model that simultaneously considers intra-pulse motion and accelerated motion of the high-maneuvering receiver. The introduction of receiver acceleration leads to nonlinear range terms in the bistatic range history, which will degrade the focusing performance if not properly compensated. Since the acceleration term is a small second-order quantity relative to the time delay, it is approximated by segmenting the aperture and applying the “stop-and-go” assumption within each sub-aperture. After dechirp, the two-dimensional (2-D) spectrum for imaging is derived by applying the principle of stationary phase and determining the azimuth stationary phase point via series reversion. Finally, imaging is achieved by azimuth compression, range cell migration correction, and secondary range compression. Simulation results demonstrate that the proposed algorithm achieves well-focused images while maintaining computational efficiency. Full article

Holographic synthetic aperture radar (HoloSAR) enables 360° three-dimensional reconstruction by incoherently stacking tomographic subaperture images. However, after conventional subaperture-wise TomoSAR reconstruction and non-coherent integration, the resulting 3D imagery suffers from severe dynamic range imbalance due to angle-dependent scattering responses: wide-angle strong scatterers are repeatedly amplified, whereas narrow-angle weak structures are buried below the noise floor. To address this post-processing challenge, we propose a joint statistical filtering framework operating on the reconstructed subaperture-domain 3D images that fuses the coefficient of variation, inter-subaperture correlation, and spectral entropy with adaptive discriminative-power weighting; target screening is then performed via a Gaussian mixture model-based Bayesian optimal threshold. For pixels classified as weak targets, a percentile-matching energy-balancing transformation is applied to adaptively rescale their energy to the main-target reference level while preserving relative amplitude relationships. Experiments on real-world Ku-band UAV circular SAR data demonstrate that the proposed method effectively compresses the dynamic range, suppresses background noise, and recovers weak narrow-angle structures that are lost in traditional non-coherent superposition, yielding more complete and interpretable HoloSAR 3D reconstructions. Quantitative evaluation on Ku-band UAV circular SAR data demonstrates that the proposed method improves the Target-to-Background Ratio by 0.7 dB (to 11.2 dB), achieves a Background Suppression Ratio of −5.2 dB, increases the Structural Completeness Index by 156% (to 1428.1), and compresses the original dynamic range imbalance, which exceeds 50 dB, while preserving scene physical realism (ENL ≈ 7.4 × 10⁻³). Full article

Compressed sensing (CS) and matrix completion algorithms (MCA) have each introduced sparse and low-rank priors into synthetic aperture radar (SAR) imaging. However, their combined use reveals a fundamental zero-sum trade-off: enhancing spatial continuity tends to obscure weak targets, while strengthening sparse recovery amplifies off-grid artifacts. This inherent conflict is further exacerbated by static regularization, which imposes a rigid global compromise and prevents genuine synergy between the two priors. To overcome this limitation, this paper proposes a Spatio-Temporal Adaptive Synergistic Constraint Imaging (STASCI) algorithm, which dynamically balances the two priors in a scene-aware manner. The core of STASCI is a unified regularization framework. The low-rank constraint models’ spatial continuity in the background to suppress off-grid artifacts. The sparse constraint, enhanced by a non-convex Geman-McClure function, is employed to detect weak targets and compensate for detail loss. A key innovation is a spatio-temporal dual-dimensional regularization mechanism that employs Sobel operators to probe local spatial gradients and dynamically adjusts the strength of each prior according to regional scene characteristics. This enables adaptive synergy rather than a fixed trade-off. The optimization is solved via the alternating direction method of multipliers (ADMM), with the low-rank subproblem accelerated by randomized singular value decomposition (RSVD). Final imaging is performed using the Range Migration Algorithm (RMA). Experiments on real measurements and public datasets demonstrate that STASCI breaks the conventional detail-background trade-off. It effectively suppresses off-grid artifacts while retaining weak targets, leading to significant improvements in imaging accuracy and robustness across complex scenarios. 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

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

Compared with conventional synthetic aperture radar (SAR) three-dimensional (3D) imaging systems, distributed unmanned aerial vehicle (UAV) SAR systems offer enhanced flexibility and single-pass capability, enabling rapid 3D imaging. Their performance, however, critically depends on the spatial arrangement of UAVs. Improper configurations result in grating lobes and increase the sidelobe level, thereby degrading elevation reconstruction. Additionally, the coordinated operation of distributed UAVs imposes spatial constraints such as safety separation. To address these challenges, this paper formulates the configuration design as a multi-constraint, multi-objective optimization problem that simultaneously considers both imaging performance and operational feasibility. Based on compressive sensing (CS) theory, the influence of configuration on sparse imaging is analyzed, and practical constraints are integrated, including 3D span limits, safety separation, and mainlobe avoidance. A joint optimization model is established to minimize the cumulative coherence of the sensing matrix while maximizing system spatial compactness. To efficiently solve this high-dimensional problem, a decoupled recursive strategy is proposed. In the first stage, a hybrid algorithm combining particle swarm optimization (PSO) and covariance matrix adaptation evolution strategy (CMA-ES) performs global optimization in the baseline domain. In the second stage, a compact configuration is constructed within the feasible region via analytical spatial recursion. Experimental results demonstrate that the proposed approach effectively reduces sensing matrix coherence and improves 3D reconstruction quality. Full article

Aimed at the elevation ambiguity problem in array synthetic aperture radar (SAR) three-dimensional imaging, this paper proposes a novel ambiguity-resolving method based on wavefront modulation. By introducing measured plasma lens modulation phases and constructing an array SAR signal echo model incorporating wavefront modulation, the method effectively overcomes the physical size limitations of traditional array antennas. Theoretical analysis demonstrates that wavefront modulation significantly reduces the grating lobe level of the array pattern, equivalently increasing the number of array channels and thereby shortening the shortest baseline length, which enhances the system’s maximum unambiguous height. At the signal processing level, an observation equation based on compressed sensing is established, and target reconstruction is achieved using the Orthogonal Matching Pursuit (OMP) algorithm. Monte Carlo simulation results indicate that under the same signal-to-noise ratio conditions, when the observation range is extended to twice the theoretical maximum unambiguous height, the proposed method maintains a reconstruction success rate of over 95%, whereas the traditional method’s reconstruction success rate drops rapidly below 40% once the maximum unambiguous range is exceeded. This study also investigates the 3D reconstruction of spatial point targets and a rectangular building, with the analysis of their theoretical ambiguous positions confirming the method’s effectiveness in suppressing ambiguous targets in the vicinity of spatial point targets as well as in front of and behind the structure. This study provides a new technical approach to overcoming antenna size constraints on airborne platforms, with significant application value in fields such as digital elevation model construction and urban 3D imaging. Full article

Spaceborne Synthetic Aperture Radar (SAR) interferometry provides long-term displacement measurements, but the quality of Persistent Scatterer (PS) time series depends critically on temporal coherence. Low-coherence points often exhibit auto-uncorrelated behaviours, which may be relevant to discriminate fast phenomena. This work introduces a coherence-based framework that identifies the coherence threshold beyond which PS displacement series retain sufficient reliability to support modelling. The threshold is estimated by analysing how data uncertainty, inferred through Sparse Bayesian Learning (SBL) techniques, varies with coherence and by detecting abrupt changes in this relationship. Once the optimal threshold is established, only the most reliable PS are used to train an SBL regression model linking satellite line-of-sight displacement to soil temperature and surface humidity measured by a low-cost ground sensor. PS-Interferometric SAR (PS-InSAR) time series are derived from COSMO-SkyMed raw images. The SBL model employs compressive-sensing principles and latent-parameter dictionaries of basis functions, whose latent parameters are calibrated through a constrained multi-start optimisation of a normalised residual-based objective function, regularised by a sub-validation dataset. In this work, it is shown that the trained model enables temporally denser reconstruction of displacement histories than the satellite revisit cycle allows and enables continuous soil monitoring by comparing model predictions with newly acquired PS-InSAR data. Full article

Earth Embedding (EMB) is a method that adapts embedding techniques from Large Language Models (LLMs) to compress the information contained in multiple remote sensing satellite images into feature vectors. This article introduces a new approach to measuring economic activity from EMBs. Using the Google Satellite Embedding Dataset (GSED), we extract a 64-dimensional representation of the Earth’s surface that integrates optical and radar imagery. A neural network maps these embeddings to nighttime light (NTL) intensity, yielding a 32-dimensional “income-aware” feature space aligned with economic variation. We then predict GDP levels and growth rates across countries and compare the results with those of traditional NTL-based models. The Earth-Embedding (EMB) based estimator achieves substantially lower mean squared error in estimating GDP levels. Combining the two sources yields the best overall accuracy. Further analysis shows that EMB performs particularly well in low-statistical-capacity and high-income economies. These results suggest that satellite embeddings can provide a scalable, globally consistent framework for monitoring economic development and validating official statistics. Full article

To enhance the comprehensive representation capability and fusion accuracy of remote sensing information, this paper proposes a multi-resolution hierarchical fusion network (MRLF) tailored to the heterogeneous characteristics of optical and synthetic aperture radar (SAR) images. By constructing a hierarchical feature decoupling mechanism, the method decomposes input images into low-resolution global structural features and high-resolution local detail features. A residual compression module is employed to preserve multi-scale information, laying a complementary feature foundation for subsequent fusion. To address cross-modal radiometric discrepancies, a pre-trained complementary feature extraction model (CFEM) is introduced. The brightness distribution differences between SAR and fusion results are quantified using the Gram matrix, and mean-variance alignment constraints are applied to eliminate radiometric discontinuities. In the feature fusion stage, a dual-attention collaborative mechanism is designed, integrating channel attention to dynamically adjust modal weights and spatial attention to focus on complementary regions. Additionally, a learnable radiometric enhancement factor is incorporated to enable efficient collaborative representation of SAR textures and optical semantics. To maintain spatial consistency, hierarchical deconvolution and skip connections are further used to reconstruct low-resolution features, gradually restoring them to the original resolution. Experimental results demonstrate that MRLF significantly outperforms mainstream methods such as DenseFuse and SwinFusion on the Dongying and Xi’an datasets. The fused images achieve an information entropy (EN) of 6.72 and a structural similarity of 1.25, while maintaining stable complementary feature retention under large-scale scenarios. By enhancing multi-scale complementary features and optimizing radiometric consistency, this method provides a highly robust multi-modal representation scheme for all-weather remote sensing monitoring and disaster emergency response. Full article