Search Results (695)

Synthetic Aperture Radar (SAR) and Interferometric SAR (InSAR) techniques have received rapid advance in recent years, and the Multi-temporal InSAR (MT-InSAR) has been widely applied in various earth observations. Distributed scatterer (DS) InSAR is one of the most advanced MT-InSAR methods, and has overcome the limitation of the lack of enough measurement points in the low coherent regions for traditional methods. While the Joint-Scatterer InSAR (JS-InSAR) is the extension of DS InSAR method, which exploited the overall information of Joint Scatterers to carry out DS identification and phase optimization. And it can avoid the inaccuracy caused by the offset errors between scatterers in complex terrain areas. However, the intensive computation and low efficiency have severely restricted the application of JS-InSAR, especially when dealing with massive and long historical SAR images. As the sequential estimator has proven to successfully improve the efficiency of MT-InAR and obtain near-time deformation time series, in this work, we proposed the sequential-based JS-InSAR (S-JSInSAR) method with flexible batches. This method has adaptively divided large single look complex (SLC) stack into different batches with flexible number and certain overlaps. Then, the JS-InSAR processing is performed on each batch, respectively, and these estimated results are integrated into the final deformation time series based on the connection mode. Thus, S-JSInSAR can efficiently process large InSAR dataset, and mitigate the decorrelation effect caused by long temporal baselines. To demonstrate the effectiveness of the S-JSInSAR, a multi-year of 145 Sentinel-1 ascending SAR images in Tangshan, China, were collected to estimate the long deformation time series. And the results compared with other methods have shown the processing time has substantially decreased without the loss of deformation accuracy, and obtain deformation spatial distribution with more details in local regions, which have well validated the efficiency and reliability of the proposed method. Full article

Currently, the manual delineation of landslide anomalies from Interferometric Synthetic Aperture Radar(InSAR )deformation data is labor-intensive and time-consuming, creating a major bottleneck for operational large-scale landslide mapping. This study proposes an automated approach for large-scale landslide identification by integrating InSAR technology with an improved Faster Regional Convolutional Neural Network (Faster R-CNN). First, surface deformation over the study area was obtained using the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique. An enhanced CRF-Faster R-CNN model was then developed by incorporating a Residual Network with 50 layers (ResNet-50)-based backbone, strengthened with a Convolutional Block Attention Module (CBAM), within a Feature Pyramid Network (FPN) framework. This model was applied to deformation velocity maps for the automated detection of landslide-prone areas. Preliminary results were subsequently validated and refined using optical images to produce a final landslide inventory. The proposed method was evaluated in the Ahai Reservoir area of the Jinsha River Basin using 248 ascending and descending Sentinel-1A images acquired between January 2019 and December 2021. Its performance was compared with that of the standard Faster R-CNN model. The results indicate that the CRF-Faster R-CNN model outperforms the conventional approach in terms of landslide anomaly detection, convergence speed, and overall accuracy. A total of 38 potential landslide hazards were identified in the Ahai Reservoir area, with an 84% validation accuracy confirmed through field investigations. This study provides crucial technical support for the rapid identification and operational application of large-scale potential landslide hazards. Full article

Within the field of the structural monitoring of bridges, numerous technologies and methodologies have been developed. Among these, methods based on synthetic aperture radar (SAR) which utilise satellite data from missions such as Sentinel-1 (European Space Agency-ESA) and COSMO-SkyMed (Agenzia Spaziale Italiana—ASI) to capture displacements, temperature-related changes, and other geophysical measurements have gained increasing attention. However, SAR has yet to establish its value and potential fully; its broader adoption hinges on consistently demonstrating its robustness through recurrent applications, well-defined use cases, and effective strategies to address its inherent limitations. This study presents a systematic literature review (SLR) conducted in accordance with key stages of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 framework. An initial corpus of 1218 peer-reviewed articles was screened, and a final set of 25 studies was selected for in-depth analysis based on citation impact, keyword recurrence, and thematic relevance from the last five years. The review critically examines SAR-based techniques—including Differential Interferometric SAR (DInSAR), multi-temporal InSAR (MT-InSAR), and Persistent Scatterer Interferometry (PSI), as well as approaches to integrating SAR data with ground-based measurements and complementary digital models. Emphasis is placed on real-world case studies and persistent technical challenges, such as atmospheric artefacts, Line-of-Sight (LOS) geometry constraints, phase noise, ambiguities in displacement interpretation, and the translation of radar-derived deformations into actionable structural insights. The findings underscore SAR’s significant contribution to the structural health monitoring (SHM) of bridges, consistently delivering millimetre-level displacement accuracy and enabling engineering-relevant interpretations. While standalone SAR-based techniques offer wide-area monitoring capabilities, their full potential is realised only when integrated with complementary procedures such as thermal modelling, multi-sensor validation, and structural knowledge. Finally, this document highlights the persistent technical constraints of InSAR in bridge monitoring—including measurement ambiguities, SAR image acquisition limitations, and a lack of standardised, automated workflows—that continue to impede operational adoption but also point toward opportunities for methodological improvement. Full article

This paper presents ACTD-Net (Attention-Convolutional Transformer Denoising Network), a novel hybrid deep learning approach for speckle noise reduction from differential synthetic aperture radar (SAR) interferometric phase maps. Differential interferometric SAR (DInSAR) is a powerful technique for detecting and quantifying surface deformations, but the obtained phase maps are corrupted by speckle noise, topographic contributions, and atmospheric artifacts. Effective speckle denoising is crucial for accurate extraction of the desired deformation information. ACTD-Net combines the strengths of convolutional neural networks (CNNs) and vision transformers (ViTs) in a two-stage architecture. First, a modified U-Net model with residual connections performs initial despeckling of the input DInSAR phase map. Then, the denoised phase map is fed into a Swin Transformer adapted with a masked self-attention mechanism, which further refines the denoising while preserving fine details and discontinuities related to surface deformations. Experimental results on simulated and real DInSAR data, including from the September 2023 Morocco earthquake region, demonstrate the effectiveness of ACTD-Net, outperforming traditional techniques and current deep learning methods in terms of quantitative metrics such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and edge preservation index (EPI). The comprehensive evaluation shows that ACTD-Net achieves up to 33.55 dB PSNR, 0.96 SSIM, and 0.94 EPI on simulated data, and 33.62 ± 2.75 dB PSNR on 388 real Morocco earthquake patches, with significant improvements in preserving phase discontinuities and reducing unwrapping errors by approximately 62% on real earthquake data. Full article

The Synthetic Aperture Radar Interferometry (InSAR) technique enables researchers to generate Digital Elevation Models (DEMs) from SAR data, which researchers widely apply in multi-temporal analyses, including ground deformation monitoring, susceptibility mapping, and analysis of spatial changes in erosion basins. In this study, we generated two interferometric DEMs from Sentinel-1 (S1, VV polarization) and TerraSAR-X (TSX, HH polarization, ascending orbit) data, processed in SNAP, over a mountainous sector of the central Andes in Chile. We assessed the accuracy of the DEMs against two reference datasets: the SRTM DEM and a high-resolution LiDAR-derived DEM. We selected 150 randomly distributed points across different slope classes to compute statistical metrics, including RMSE and MedAE. Relative to the LiDAR DEM, both sensors yielded rMSE values of approximately 20 m, increasing to 23–24 m when compared with the SRTM DEM. The MedAE, a metric less sensitive to outliers, was 3.97 m for S1 and 3.26 m for TSX with respect to LiDAR, and 7.07 m for S1 and 7.49 m for TSX relative to SRTM. We observed a clear positive correlation between elevation error and terrain slope. In areas with slopes greater than 45°, the MedAE exceeded 14 m relative to the LiDAR DEM and reached ~15 m relative to the SRTM for both S1 and TSX. Full article

Fiber-optic gyroscopes (FOGs) have become one of the most important elements of modern inertial navigation systems due to their high accuracy, reliability, and independence from external signals such as satellite navigation. This review analyzes and discusses the key FOG architectures: interferometric (IFOG), resonant (RFOG), digital (DFOG), and hybrid (HFOG). The concepts of their functioning, structural features, and the main advantages and limitations of each architecture are examined. Particular focus is placed on advanced signal-processing and error-compensation algorithms, including filtering techniques, noise suppression, mitigation of thermal and mechanical drifts, and emerging machine learning (ML) based approaches. The analysis of these architectures is carried out in terms of major parameters that determine accuracy, robustness, and miniaturization potential. Various applications of FOGs in space systems, ground platforms, marine and underwater navigation, aviation, and scientific research are also being considered. Finally, the latest development trends are summarized, with a particular focus on miniaturization, integration with additional sensors, and the introduction of digital and AI-driven solutions, aimed at achieving higher accuracy, long-term stability, and resilience to real-world disturbances. Full article

The development of minimally invasive diagnostic devices in the biomedical field has grown significantly, especially those that take advantage of photoacoustic phenomena. Photoacoustic imaging is an imaging technique that exploits the photoacoustic effect, relying on the conversion of absorbed light into ultrasound waves. Thanks to lab-on-fiber technology, optical fiber can be functionalized to generate and receive a photoacoustic signal. Weak acoustic signals often limit this process, as conversion efficiency can be influenced by factors such as tissue heterogeneity, light scattering, and the attenuation of the acoustic waves within tissues. Consequently, there is significant interest in the development of highly sensitive systems with broad bandwidths. While the literature has largely focused on standard devices utilizing the interferometric effect in homogeneous slabs, this study explores the potential of multilayer structures that leverage Bragg reflection to be realized on the fiber tip. We numerically investigated both periodic and aperiodic designs of polymeric multilayer structures to further enhance the optical performance of opto-acoustic sensors. We demonstrate an enhancement in sensitivity of up to about three orders of magnitude without compromising bandwidth. This work highlights the advantages of multilayer sensor designs in improving sensitivity and performance for high-frequency opto-acoustic sensing. Full article

With advances in scientific foundations and engineering practice, segmented mirrors—a key architecture for realizing extremely large apertures and high-resolution imaging—have become foundational across space astronomy, ground-based telescopes, and advanced manufacturing. In recent years, interferometry, which leverages optical coherence and phase sensitivity, has become a powerful tool for inter-segment co-phasing. Its capabilities have advanced markedly owing to developments in multi-wavelength techniques, high-speed high-dynamic-range detectors, and instantaneous phase-shifting methods. Relative to non-interferometric sensing, interferometry directly encodes and unwraps phase. This enables a unified framework that combines millimeter-scale dynamic range with nanometer-level resolution throughout coarse acquisition, fine phasing, and in situ maintenance. This paper first outlines the degrees of freedom and error sources in segmented mirrors. It then reviews the configurations and acquisition strategies of shearing, Mach–Zehnder, Michelson, Fizeau, and PISTIL interferometers, and systematizes interferogram processing methods—such as phase-shifting, synthetic-wavelength techniques, and digital holography—for retrieving piston and tip/tilt. Accuracy of piston is λ/50–λ/100, and tip/tilt accuracy can reach the arcsecond level, with resolution at the nanometer scale. Finally, we discuss pathways to extend interferometric metrology from segmented mirrors to other discontinuous surfaces (e.g., segmented detectors, segmented gratings, microlens arrays) and outlines future research directions. Full article

Phase optimization, aimed to enhance phase signal-to-noise ratio, is a critical component of the distributed scatterer interferometric synthetic aperture radar technique and directly determines the fineness and reliability of deformation monitoring. As a state-of-the-art method that balances computational efficiency and optimization performance in high-dimensional data environments, sequential phase optimization has been widely studied. However, the improper matrix partitioning and discontinuous sequence compensation in current sequential methods severely restrict their optimization performance. To address these limitations, an adaptive sequential phase optimization method (AdSeq) based on coherence stability detection and adjustment correction is proposed. A submatrix dimension adaptive estimation model driven by coherence stability detection is first established based on persistent exceedance detection analysis. Then, a covariance matrix adaptive sequential partitioning strategy is developed by introducing the submatrix overlap criterion. Finally, a phase reference correction model based on weighted least squares adjustment is proposed to improve phase continuity and overall optimization performance. Experiments with simulated and real datasets are performed to comprehensively evaluate the optimization performance. Experimental results demonstrate that, compared with traditional phase optimization methods, the monitoring point density obtained by AdSeq increased by over 21.07%, and the deformation monitoring accuracy reached 16.49 mm, representing an improvement exceeding 10.09%. These results confirm that the proposed AdSeq method achieves superior noise robustness and phase optimization performance, and provides a higher deformation monitoring accuracy. Full article

In recent years, differential synthetic aperture radar interferometry (DInSAR) has been widely used to monitor ground deformation induced by mineral resource exploitation. Compared with conventional DInSAR, InSAR time series (TS-InSAR) techniques offer significantly improved monitoring accuracy. However, their results still remain strongly influenced by atmospheric delays. To address this and discuss the applicability of tropospheric delay correction methods over mining areas, this study applied multiple correction strategies to distributed scatterer InSAR (DS-InSAR), including the Linear, ERA5, GACOS, spatio-temporal filtering method, and their adaptive weighted fusion approach. Meanwhile, an improved Common Scene Stacking (CSS) InSAR tropospheric delay correction method has been proposed. These methods’ performance have been evaluated by the quantitative comparisons of the corrected interferometric phases and by in situ measurements. The results indicated that the adaptive fusion method outperformed any individual model included, where spatio-temporal filtering should be applied with caution, as it may undermine part of the deformation signal. The effectiveness of ERA5 and GACOS is limited due to their resolution mismatch with that of the SAR images. On the other hand, the improved CSS method achieved the best results over the study area, with an average reduction of 32.22% in the RMSE of the interferometric phase, resulting in an RMSE below 8 mm on average and as low as 5 mm over certain areas. Thus, over local mining areas with large-magnitude and ground deformation, the improved CSS outperforms all the other compared methods, where it can effectively mitigate atmospheric delays while preserving the deformation signals. Full article

The Irbene single-baseline radio interferometer (ISBI), operated by the Ventspils International Radio Astronomy Centre (VIRAC), offers a rare and versatile configuration in modern radio astronomy. Combining the 32-m and 16-m fully steerable parabolic radio telescopes separated by an 800-m baseline, this system possesses a unique capability for high-sensitivity, time-domain interferometric observations. Unlike large interferometric arrays optimized for sub-arcsecond resolution imaging, the Irbene system is tailored for studies that require high temporal resolution and a strong signal-to-noise ratio. This paper reviews key scientific applications of the Irbene interferometer, including simultaneous methanol maser and radio continuum variability studies, high-cadence monitoring of quasi-periodic pulsations (QPPs) in stellar flares, ionospheric diagnostics using GNSS signals, orbit determination of navigation satellites and forward scatter radar techniques for space object detection. These diverse applications demonstrate the scientific potential of compact interferometric systems in an era dominated by large-scale observatories. Full article

Interferometric Synthetic Aperture Radar (InSAR) technology is crucial for large-scale land subsidence analysis in cultivated areas within hilly and mountainous regions. Accurate prediction of this subsidence is of significant importance for agricultural resource management and planning. Addressing the limitations of existing subsidence prediction methods in terms of accuracy and model selection, this paper proposes a deep neural network prediction model based on Variational Mode Decomposition (VMD) and the Snake Optimizer (SO), termed VMD-SO-CNN-LSTM-MATT. VMD decomposes complex subsidence signals into stable intrinsic components, improving input data quality. The SO algorithm is introduced to globally optimize model parameters, preventing local optima and enhancing prediction accuracy. This model utilizes time–series subsidence data extracted via the SBAS-InSAR technique as input. Initially, the original sequence is decomposed into multiple intrinsic mode functions (IMFs) using VMD. Subsequently, a CNN-LSTM network incorporating a Multi-Head Attention mechanism (MATT) is employed to model and predict each component. Concurrently, the SO algorithm performs global optimization of the model hyperparameters. Experimental results demonstrate that the proposed model significantly outperforms comparative models (traditional Long Short-Term Memory (LSTM) neural network, VMD-CNN-LSTM-MATT, and Sparrow Search Algorithm (SSA)-optimized CNN-LSTM) across key metrics: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). Specifically, the reductions achieved are minimum improvements of 29.85% for MAE, 8.42% for RMSE, and 33.69% for MAPE. This model effectively enhances the prediction accuracy of land subsidence in cultivated hilly and mountainous areas, validating its high reliability and practicality for subsidence monitoring and prediction tasks. Full article

Urban land subsidence, a globally prevalent environmental problem and geohazard triggered by rapid urbanization, threatens ecological security and socioeconomic stability. Chongqing City in southwestern China, recognized as the world’s largest mountainous city, has encountered land subsidence challenges exacerbated by accelerated urban construction. This study proposes an effective method for extracting urbanization intensity by integrating Sentinel-1, Sentinel-2, and its derived synthetic aperture radar and spectral indices features, combined with texture features. The small baseline subset interferometric synthetic aperture radar technique was employed to monitor land subsidence in Chongqing between 2018 and 2024. Furthermore, the relationships among urbanization intensity, metro construction, groundwater dynamics, and land subsidence were systematically analyzed. Finally, geographical detector and multiscale geographically weighted regression models were employed to explore the interactive effects of anthropogenic, topographic, geological-tectonic, climatic, and land surface characteristic factors contributing to land subsidence. The findings reveal that (1) the method proposed in this paper can effectively extract urbanization intensity and provide an important approach to analyze the influence of urbanization on land subsidence. (2) Land subsidence along newly opened metro lines was more pronounced than along existing lines. The shorter the interval between metro construction completion and the start of operation, the greater the subsidence observed within the first 3 months of operation, which indicates that this interval influences land subsidence. (3) Overall, groundwater dynamics and land subsidence showed a clear correlation from June 2022 to June 2023, a phenomenon largely caused by the extreme summer high temperatures of 2022, triggering reduced precipitation and a notable groundwater decline. Beyond this period, however, only a weak correlation was observed between groundwater fluctuations and land subsidence trends, indicating that other factors likely dominated subsidence dynamics. (4) The anthropogenic factors have a higher relative influence on land subsidence than other drivers. In terms of q-value, the top six factors are road network density > precipitation > elevation > enhanced normalized difference impervious surface index > population density > nighttime light, while distance to fault exhibits the least explanatory power. Given Chongqing’s exemplary status as a mountainous city, this study offers a foundational reference for subsequent quantitative analyses of land subsidence and its drivers in other mountainous cities worldwide. Full article

The comprehensive, accurate, and rapid acquisition of large-scale surface deformation using Interferometric Synthetic Aperture Radar (InSAR) technology provides crucial information support for regional eco-geological safety assessments and the rational development and utilization of groundwater resources. The Linfen-Yuncheng Basin in Shanxi Province is one of China’s historically most frequented regions for geological hazards in plain areas, such as land subsidence and ground fissures. This study employed the coherent point targets based Small Baseline Subset (SBAS) time-series InSAR technique to interpret a dataset of 224 scenes of 5 m resolution RADARSAT-2 satellite SAR images acquired from January 2017 to May 2024. This enabled the acquisition of high-resolution spatiotemporal characteristics of surface deformation in the Linfen-Yuncheng Basin during the monitoring period. The results show that the area with a deformation rate exceeding 5 mm/a in the study area accounts for 12.3% of the total area, among which the subsidence area accounts for 11.1% and the uplift area accounts for 1.2%, indicating that the overall surface is relatively stable. There are four relatively significant local subsidence areas in the study area. The total area with a rate exceeding 30 mm/a is 41.12 km², and the maximum cumulative subsidence is close to 810 mm. By combining high-resolution satellite images and field survey data, it is found that the causes of the four subsidence areas are all the extraction of groundwater for production, living, and agricultural irrigation. This conclusion is further confirmed by comparing the InSAR monitoring results with the groundwater level data of monitoring wells. In addition, on-site investigations reveal that there is a mutually promoting and spatially symbiotic relationship between land subsidence and ground fissures in the study area. The non-uniform subsidence areas monitored by InSAR show significant ground fissure activity characteristics. The InSAR monitoring results can be used to guide the identification and analysis of ground fissure disasters. This study also finds that due to the implementation of surface water supply projects, the demand for groundwater in the study area has been continuously decreasing. The problem of ground water over-extraction has been gradually alleviated, which in turn promotes the continuous recovery of the groundwater level and reduces the development intensity of land subsidence and ground fissures. Full article

Monitoring large-gradient surface displacement caused by underground mining remains a significant challenge for conventional Synthetic Aperture Radar (SAR)-based techniques. This study introduces optical flow methods to monitor large-gradient displacement in mining areas and conducts a comprehensive comparison with Small Baseline Subset Interferometric SAR (SBAS-InSAR) and Pixel Offset Tracking (POT) methods. Using 12 high-resolution TerraSAR-X (TSX) SAR images over the Daliuta mining area in Yulin, China, we evaluate the performance of each method in terms of sensitivity to displacement gradients, computational efficiency, and monitoring accuracy. Results indicate that SBAS-InSAR is only capable of detecting displacement at the decimeter level in the Dalinta mining area and is unable to monitor rapid, large-gradient displacement exceeding the meter scale. While POT can detect meter-scale displacements, it suffers from low efficiency and low precision. In contrast, the proposed optical flow method (OFM) achieves sub-pixel accuracy with root mean square errors of 0.17 m (compared to 0.26 m for POT) when validated against Global Navigation Satellite System (GNSS) data while improving computational efficiency by nearly 30 times compared to POT. Furthermore, based on the optical flow results, mining parameters and three-dimensional (3D) displacement fields were successfully inverted, revealing maximum vertical subsidence exceeding 4.4 m and horizontal displacement over 1.5 m. These findings demonstrate that the OFM is a reliable and efficient tool for large-gradient displacement monitoring in mining areas, offering valuable support for hazard assessment and mining management. Full article