Search Results (2,035)

A synthetic aperture radar (SAR) provides vital global observations of ocean waves. However, the quasi-linear inversion algorithm routinely used for Sentinel-1 Level-2 Ocean Swell Wave (OSW) products suffers from inherent nonlinear imaging limitations. These include severe distortions and the inability to resolve wind-sea components under a strong azimuth cut-off effect. To address these challenges, this paper proposes a novel prior-knowledge-integrated machine learning framework to reconstruct complete and accurate directional wave spectra from Sentinel-1A SAR wave mode data. First, an extreme gradient boosting model is trained to accurately estimate wind-sea heights, which are then used to construct a theoretical JONSWAP prior spectrum. Subsequently, a U-Net architecture seamlessly integrates this physical prior knowledge with the official OSW swell spectra baseline. Independent validation demonstrates that the proposed framework significantly increases the spectral similarity against ERA5 reanalysis compared to the standard OSW. Furthermore, the derived parameters of total significant wave height, mean wave period, and mean wave direction exhibit remarkable improvements, with root mean square errors of 0.4026 m, 0.4342 s and 20.42°, respectively. The enhancement of SAR inferred two-dimensional wave spectra is also examined and discussed by three typical case studies. It is indicated that integrating physical wave knowledge with machine learning robustly mitigates the non-linear limitations of SAR imaging, providing highly reliable directional wave spectra for global ocean monitoring and forecasting. Full article

Aiming at the problems of extremely scarce measured samples and significant domain shift between simulated and measured data in automatic target recognition (ATR) of air targets for spaceborne radar, this paper proposes an inverse synthetic aperture radar (ISAR) image recognition method for air targets combining physics-driven data augmentation guided by detection prior information with domain adversarial transfer learning. First, the mapping relationship between scattering point projection and ISAR images is established by using the target 3D point cloud and radar observation geometric priors, and a 2D sinc kernel function is introduced for energy distribution rendering. Then, under the unsupervised transfer learning paradigm, aiming at the distribution inconsistency between augmented data (source domain) and unlabeled simulated data (target domain), this paper designs a cross-domain recognition task experiment including six types of typical aircraft targets, and compares the cross-domain recognition performance of three transfer learning methods (model fine-tuning, deep domain confusion (DDC) and domain-adversarial neural networks (DANN)) on the target domain. Meanwhile, t-distributed stochastic neighbor embedding (t-SNE) visualization is used to analyze the feature distribution alignment ability of the models. Simulation experiments show that the DANN model with a dynamic inversion coefficient introduced in the gradient reversal layer (GRL) achieves a recognition accuracy of 99.5% on the unlabeled target domain, which is significantly superior to the model fine-tuning and DDC methods. Moreover, it makes the feature distributions of source and target domain samples highly overlapping, and maintains a strong inter-class discriminability while eliminating the domain shift. The proposed scheme provides a physically interpretable and robust technical path for few-shot radar target image recognition. Full article

With the advancement of remote sensing technology, image acquisition has become more convenient and the amount of information captured has significantly increased, playing a vital role in numerous fields. However, cloud cover often results in missing image data, severely affecting data usability. In recent years, although deep learning methods have made progress in cloud removal tasks, the complexity of modeling multispectral band relationships and the scarcity of paired data remain major challenges. To address this, this paper proposes a two-stage codebook fusion vector-quantized generative adversarial network (TCF-VQ GAN) and a training framework. The first stage employs synthetic aperture radar (SAR), MODIS, and cloud-free data for unsupervised training; the second stage performs fusion fine-tuning using SAR and MODIS on paired cloudy/cloud-free data. The model incorporates a space-channel jointed gated convolution (SCGC) module to model irregular cloud cover and combines channel attention for band selection, while a dynamically weighted wavelet alignment loss function (DW²A) is designed to enhance multiscale feature representation. Experiments on the SEN12MS-CR and SMILE-CR datasets demonstrate that the proposed method outperforms existing methods across all metrics: on SEN12MS-CR, PSNR is 31.0397 and SAM is 4.7243; they are 33.5191 and 2.1663, respectively, on SMILE-CR. Furthermore, under fixed paired data conditions, simply adding auxiliary and cloud-free data further improves performance, validating the method’s effectiveness in data-scarce scenarios. Full article

Lunar optical and synthetic aperture radar (SAR) imagery provide complementary information for characterizing the lunar surface. However, their joint use remains challenging because of substantial cross-modality differences and severe illumination constraints, particularly in polar regions. To address this challenge, we propose LS2ODiff (Lunar SAR-to-Optical Diffusion), a diffusion-based framework designed for SAR-to-optical image translation in lunar environments. LS2ODiff uses SAR observations as conditional guidance in the diffusion process and incorporates a partial-convolution strategy into the U-Net backbone to handle irregular invalid regions. In addition, self-attention modules are incorporated into the downsampling stages of the U-Net to model long-range spatial dependencies and enhance global structural consistency in complex lunar terrain. We further construct a dedicated paired dataset of the lunar south polar region by registering Chandrayaan-II DFSAR data with Lunar Reconnaissance Orbiter (LRO) Narrow-Angle Camera (NAC) imagery. Comparative experiments against Pix2Pix, CycleGAN, SynDiff, and ConDiff demonstrate that LS2ODiff achieves better visual fidelity and quantitative performance in terms of peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), Fréchet inception distance (FID), and learned perceptual image patch similarity (LPIPS). These results demonstrate the potential of diffusion models for high-fidelity lunar image translation, offering new opportunities for polar terrain interpretation and future exploration missions. Full article

The reinforced concrete structures of many bridges and buildings in Taiwan are over 30 years old. Seismic retrofitting of these structures requires an accurate assessment of reinforcement configuration and corrosion conditions to ensure structural safety and seismic performance. In this study, a 1 GHz ground-penetrating radar (GPR) antenna was used to scan reflected signals from single- and double-row reinforcing bars embedded in concrete. Based on established principles reported in previous studies, detailed analyses were conducted, including the use of the approximate circumference method to estimate reinforcing bar dimensions and the determination of spacing between double-row reinforcing bars (6–8 cm). The synthetic aperture focusing technique was first applied to process the original GPR data matrix. Subsequently, physical parameters related to interface diffraction, such as the perimeter S of the reinforcing bar, were extracted using the dielectric constant of the material interface, the calculated power reflection coefficient, and the First Fresnel Zone. These approaches enabled more accurate estimation of reinforcing bar dimensions (e.g., equivalent to #3 bar size) and improved resolution of spacing between double-row reinforcing bars to 3–6 cm. The results demonstrate that using the synthetic aperture focusing technique to process GPR data enhances the ability to determine reinforcing bar dimensions, interpret bar spacing, and improve imaging resolution, thereby providing a reliable reference for the safety assessment of reinforced concrete structures. Full article

Synthetic Aperture Radar (SAR) is one of mainstream remote sensing techniques, offering all-weather, day-and-night operational capabilities. However, throughout the processes of signal transmission, propagation, and reception, it is difficult to ensure that the amplitude and phase of the SAR signal strictly follow a linear frequency modulation (LFM) characteristic. The resulting signal distortion often leads to main lobe broadening and sidelobe elevation, degrading the focusing performance of SAR images. Traditionally, this issue has been addressed primarily through SAR system internal calibration and pre-distortion compensation, which makes it challenging to maintain the signal in an ideal state over the long term. At the same time, many simplified SAR systems also lack an internal calibration design, such as low-cost UAV-borne SAR payloads. In this paper, we propose a novel signal distortion compensation method based on SAR image data. Without relying on SAR system calibration signals, this method estimates and compensates for signal distortion directly using SAR image data, thereby improving SAR image focusing performance, achieving a resolution closer to the theoretical bandwidth and lower sidelobe. The processing and analysis of both manned and unmanned airborne SAR image data and calibration signals demonstrate that the proposed method effectively compensates for signal distortion phases, achieving performance comparable to that of real-time calibration-signal-based methods. Full article

Surface deformation is characterized by long-term accumulation and significant spatial differences, commonly inducing ground fissures and structural damage to roads and buildings, and in severe cases, causing collapses and other accidents that directly threaten human life. Reliable deformation monitoring and prediction are of great significance for early warning and infrastructure safety. Synthetic aperture radar interferometry (InSAR) technology can be used to acquire high-spatiotemporal-resolution temporal observations of surface deformation over a large area, but research on surface deformation prediction using InSAR temporal images is still relatively limited. Therefore, in this study, we used Sentinel-1 temporal imagery as the data foundation. Firstly, small baseline subset (SBAS)-InSAR inversion was used to obtain the line-of-sight-oriented cumulative deformation sequence and subsidence rate results. Based on this, the causal multi-trend fusion patch-to-point (CMTF-P2P) surface deformation prediction framework was developed. This framework effectively separates the long-term trends and short-term fluctuations in the deformation sequence through causal decomposition; introduces external drivers such as rainfall and event phase characteristics to enhance the temporal expression; and utilizes patch-to-point fusion of neighborhood spatial information to improve the spatial continuity and the ability to characterize local differences. Experimental results show that the method has an RMSE of 0.43 mm and an R² of 0.92, outperforming time-series deformation prediction models such as LSTM and iTransformer. Compared with traditional models that learn overall change patterns from historical time series, this paper alleviates the confusion between trends and volatility using causal STL decomposition, making the model’s expression of long-term trends and seasonal and short-term fluctuations in volatility clearer; event phase encoding enhances the model’s ability to characterize sudden disturbances and phased responses to events; the patch-to-point structure incorporates spatiotemporal information within the neighborhood, enhancing the model’s ability to apply spatiotemporal information; multi-branch collaborative modeling enhances the model’s ability to characterize multi-scale temporal features; and incremental cumulative consistency constraints enhance the physical consistency of the prediction results. Full article

Coal mines constitute a vital component of the national security system, where the extraction and utilisation of coal resources directly impact mine stability and engineering safety. Therefore, addressing the surface deformation issues caused by repeated mining activities across multiple coal seams at the Daliuta Mine, this study proposes a multi-source remote sensing monitoring technology system, which aims to improve the accuracy of surface deformation in the mining area. At the mining area scale, optimised Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technology utilised 168 Sentinel-1A image scenes to generate 789 interferometric image pairs. This extracted the long-term surface deformation field of the Daliuta mining area, revealing the spatiotemporal evolution patterns of surface subsidence under repeated mining activities. To further enhance monitoring accuracy and reliability, this study proposed a Satellite Aerial-Prior Weighting (SA-PW) method. This approach integrated satellite-based time-series InSAR, aerial Pixel Offset Tracking (POT), and unmanned aerial vehicle light detection and ranging (UAV LiDAR) data through a dynamic priority weighting model. This enabled the synergistic inversion of high-precision surface deformation parameters for repeatedly mined areas. Results demonstrated that compared to SBAS-InSAR alone, the SA-PW method achieved a 10% improvement in surface deformation parameter accuracy. By constructing a dynamic priority-weighted model, this approach systematically integrated multi-source data to achieve collaborative inversion of high-precision surface deformation parameters in repeatedly mined areas. Results demonstrated that compared to SBAS-InSAR and UAV LiDAR methods, SA-PW data fusion yielded higher accuracy, with MAE and RMSE values of 60 mm and 90 mm on the A line, and 57 mm and 83 mm on the H line, respectively. This method facilitates the establishment of integrated air–space–ground real-time monitoring networks for mining areas, enables subsidence hazard early warning and management, identifies key zones for ecological restoration, explores synergistic mechanisms between repeated mining and ecological rehabilitation, and promotes safe and green mining operations and development. Full article

Forest above-ground biomass (AGB) estimation is crucial for evaluating carbon dynamics. Although optical and synthetic aperture radar (SAR) data provide complementary spectral and structural information, limitations in existing fusion approaches restrict AGB estimation accuracy. This study proposes a multi-source data fusion framework comparing two image fusion strategies—the conventional Hue-Intensity-Saturation Wavelet (HIS-Wavelet) method and a deep learning-based HIS-Non-Subsampled Shearlet Transform combined with Pulse Coupled Neural Network (HIS-NSST + PCNN) approach—for forest AGB estimation using Gaofen-1 (GF-1), Gaofen-2 (GF-2), and Gaofen-3 (GF-3) satellite imagery in a subtropical forest area of Yunnan Province, China. Three regression models—Multiple Linear Stepwise Regression (MLSR), K-Nearest Neighbor (KNN), and KNN with Fast Iterative Feature Selection (KNN-FIFS)—were systematically compared to evaluate estimation performance and justify model selection. Results indicate that the HIS-NSST + PCNN method outperforms HIS-Wavelet in fusion quality metrics, with the GF-2 Red-Near-infrared-Blue (RNB) band and GF-3 combination using HH co-polarization achieving the highest image quality. The optimal AGB retrieval was achieved with the GF-1RNB and GF-3 combination under HIS-NSST + PCNN (coefficient of determination (R²) = 0.80, root mean square error (RMSE) = 14.79 t/ha), improving R² by 0.07 and RMSE by 2.35 t/ha over HIS-Wavelet. However, for GF-2 + GF-3, HIS-Wavelet achieved marginally better inversion accuracy (R² = 0.71) than HIS-NSST + PCNN (R² = 0.69), indicating that superior fusion quality does not directly translate to higher inversion accuracy. Bootstrap resampling analysis (1000 iterations) confirmed the statistical robustness, with the optimal KNN-FIFS yielding R² = 0.800 (95% confidence interval (CI): 0.678–0.924) and RMSE = 14.79 t/ha (95% CI: 12.51–17.22 t/ha), showing non-overlapping confidence intervals with both benchmark models. These findings demonstrate that spectral complementarity between optical and SAR data plays a more critical role than spatial resolution alone in fusion-based AGB estimation, and that adaptive feature selection is essential for maximizing inversion accuracy. 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

Video synthetic aperture radar (ViSAR) provides continuous multiframe images while maintaining high resolution and has become an important tool for complex scene surveillance and moving target tracking. ViSAR imaging is susceptible to interframe drift caused by motion errors, which severely degrades video stability. When registering long time series of real airborne video SAR images, conventional image registration based on Normalized Cross-Correlation (NCC) is affected by several factors, including platform residual motion errors, approximations in the imaging geometry, interpolation resampling, and SAR speckle noise. As a result, noticeable interframe jitter persists in the registered sequence, and the stabilization accuracy is insufficient to meet high-precision image stabilization requirements. To address these issues, this paper proposes an image stabilization method for airborne video SAR based on a joint Singer-random walk model. Firstly, with the first frame selected as the reference, subpixel drift measurements in the azimuth and range directions are extracted from continuous frames via NCC-based registration. Subsequently, the true drift is modeled as a two-dimensional Singer process and the systematic bias as a random walk process, yielding a joint state space model that comprises displacement, velocity, acceleration, and bias components. On this basis, a Kalman filter and a Rauch–Tung–Striebel (RTS) fixed-interval smoother are applied to perform temporal filtering and trajectory smoothing on the drift measurements, thereby producing smooth two-dimensional drift estimates that closely approximate the actual drift trajectory. Finally, the smoothed drift trajectory is used to perform frame-by-frame subpixel drift correction on the original image sequence, achieving high-precision interframe stabilization of the ViSAR imagery. The results of real data processing demonstrate that the proposed method can effectively improve the consistency and scene stability of ViSAR multi-frame imaging. Full article

Interferometric Synthetic Aperture Radar (InSAR) is crucial for monitoring ground displacement, particularly in Pakistan’s capital area, where urban expansion and active geotectonics converge. This study introduces the Consecutive Interferogram Stacking Approach (CISA), a processing framework optimized for near-real-time deformation monitoring using full-resolution Sentinel-1 data from adjacent acquisition pairs. Unlike conventional InSAR techniques that rely on spatial multilooking to suppress phase noise—which sacrifices spatial resolution for computational efficiency—CISA preserves native resolution through sequential interferogram stacking, accepting that short-interval interferograms retain geophysical phase instabilities (including fading signals) inherent to scatterer decorrelation. By minimizing temporal decorrelation through consecutive pairing, CISA enhances interferogram coherence (6–14% improvement) and reduces Root Mean Square Error (RMSE) by approximately 25% compared to conventional multilooked time series, while enabling the computational efficiency critical for operational applications. The framework’s incremental architecture allows velocity updates within hours of new image acquisition—requiring only single interferogram addition rather than complete network reprocessing—making it suitable for rapid-response hazard assessment where latency constraints outweigh the need for long-baseline phase filtering. CISA reveals spatiotemporal subsidence patterns potentially reflecting the influence of fault zone geometry, groundwater fluctuation, and urbanization, with full-resolution analysis delineating linear deformation patterns spatially consistent with blind fault traces through multi-directional displacement modeling. These findings demonstrate that operational monitoring of geohazards can be achieved through strategic trade-offs between processing latency and geophysical noise suppression, providing actionable intelligence for infrastructure risk management in tectonically active urban environments. Full article

The nonuniform raw data due to the varying pulse repetition interval (PRI) and the loss of echo pulses inevitably introduce azimuth grating lobes in the low-oversampled staggered synthetic aperture radar (LS-SAR) images, which result in ghost artifacts. In this paper, a deconvolution-based grating lobes reduction method for LS-SAR images is proposed to improve image quality. Firstly, the position-invariant property of azimuth grating lobes is theoretically analyzed and verified, and the LS-SAR image on the same range cell is mathematically modeled as the convolution between the scattering scene and the point spread function (PSF) of the LS-SAR imaging system, accompanied by the additive noise. Then, the PSF is numerically calculated according to the LS-SAR sampling strategy, the measured azimuthal antenna pattern, and the BP (Back Projection) imaging method. Finally, based on the Lucy–Richardson (LR) iterative deconvolution principle, the recovery of observed scenes and grating lobes reduction can be simultaneously achieved by deconvoluting the LS-SAR image with the acquired PSF. Both simulated experiments with point-array targets and real SAR images, as well as validation experiments with airborne measured LS-SAR data, demonstrated the effectiveness of the proposed method. Full article

Synthetic Aperture Radar (SAR) images are affected by speckle noise, which limits their application in fine object interpretation and quantitative analysis. Recent deep learning-based single-image SAR despeckling methods have made significant progress in spatial structure modeling but struggle to exploit temporal redundancy in multi-temporal data. Existing multi-temporal despeckling methods usually rely on complex spatiotemporal network structures, which are prone to overfitting or excessive smoothing of details when training samples are limited. To address these challenges, this paper proposes a latent-space-guided multi-temporal SAR despeckling method from the perspective of transfer learning and representation alignment, achieving effective knowledge transfer from single-image SAR despeckling to multi-temporal despeckling tasks. The method treats the single-image SAR despeckling task as a knowledge source domain, using stable latent space representations learned from the pre-trained single-image despeckling model as prior constraints. A latent space regularization mechanism is introduced during the training of the multi-temporal despeckling model, thereby establishing an explicit representation bridge between the 2D spatial model and the 3D spatiotemporal model. With this strategy, the multi-temporal model inherits the structural perception capability of the single-image model under limited training samples, improving speckle suppression while effectively maintaining image detail and structural consistency. Additionally, a pure convolutional network architecture is employed to support variable-length multi-temporal sequence input, enhancing the method’s adaptability under different temporal sampling conditions. Full article

Cross-domain object detection from optical to Synthetic Aperture Radar (SAR) imagery addresses the challenges of SAR data scarcity and high annotation costs, enabling crucial capabilities for persistent maritime surveillance and reconnaissance. However, the substantial modality gap resulting from distinct imaging mechanisms and severe coherent speckle noise significantly hampers knowledge transfer. Existing Unsupervised Domain Adaptation (UDA) methods, which primarily rely on adversarial feature alignment or static pseudo-labeling, struggle to replicate the physical backscattering properties of SAR data and often fall prey to confirmation bias due to intense background clutter. To overcome these limitations, this paper introduces the Diffusion-Enhanced Mutual Consistency (DEMC) framework. DEMC introduces a novel two-stage adaptation paradigm. The first stage, the Diffusion-Based Domain Alignment (DBDA) module, generates a physics-aware intermediate domain. By integrating step-efficient diffusion generation with physical refinement, this module effectively reduces the cross-modal visual discrepancy while preserving the semantic structure of the optical source. In the second stage, this paper tackles the pervasive issue of pseudo-label noise with the Dual-Student Mutual Verification (DSMV) mechanism. Guided by Cross-Agent Spatial Consensus (CASC) and Adaptive Thresholding (AIT), this mechanism dynamically refines pseudo-labels through geometric overlap validation, effectively recovering faint, low-contrast targets that would typically be discarded by standard thresholds. Extensive evaluations across four benchmark tasks (HRSC2016/ShipRSImageNet to SSDD/HRSID) demonstrate that DEMC establishes a new state-of-the-art. Notably, the framework significantly enhances detection recall and reduces omission errors in complex coastal environments, offering a robust solution for zero-tolerance, all-weather surveillance tasks. Full article