Search Results (1,188)

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

On 7 January 2025, an Mw 7.0 earthquake struck Dingri County, Shigatse, Tibet. This was the largest event in the region in recent years. Analysis of the Dingri earthquake is urgent for understanding the coseismic slip and early postseismic deformation characteristics. In this study, the coseismic characteristics were analyzed by using Lutan-1 and Sentinel-1 data with the Differential Interferometric Synthetic Aperture Radar method, and then the Okada elastic half-space dislocation model was used to invert the coseismic slip distribution of the seismogenic fault. The postseismic characteristics were analyzed by Sentinel-1 ascending and descending orbits, then time-series deformation results were obtained with the Small Baseline Subset InSAR method. The main results are as follows: (1) The maximum coseismic subsidence is −2.03 m and the maximum coseismic uplift is 0.68 m, the coseismic deformation is concentrated on the west side of the new rupture trace generated by the coseismic events; (2) the ruptured fault is dominated by normal faulting with a minor strike-slip component, and the slip is mainly distributed at depths of 0–15 km, with a maximum slip of about 3.97 m; (3) the deformation characteristics of the fault in the postseismic stage are basically consistent with those during the coseismic stage. The research results play an important role in understanding the earthquake fault tectonic activities. Full article

Surface deformation induced by the extraction of natural resources constitutes a non-stationary spatiotemporal process. Modeling surface deformation time series obtained through Interferometric Synthetic Aperture Radar (InSAR) technology using deep learning methods is crucial for disaster prevention and mitigation. However, the complexity of model hyperparameter configuration and the lack of interpretability in the resulting predictions constrain its engineering applications. To enhance the reliability of model outputs and their decision-making value for engineering applications, this study presents a workflow that combines a Tree-structured Parzen Estimator (TPE)-based Bayesian optimization approach with ensemble inference. Using the Rhineland coalfield in Germany as a case study, we systematically evaluated six deep learning architectures in conjunction with various spatiotemporal coding strategies. Pairwise comparisons were conducted using a Welch t-test to evaluate the performance differences across each architecture under two parameter-tuning approaches. The Benjamini–Hochberg method was applied to control the false discovery rate (FDR) at 0.05 for multiple comparisons. The results indicate that TPE-optimized models demonstrate significantly improved performance compared to their manually tuned counterparts, with the ResNet+Transformer architecture yielding the most favorable outcomes. A comprehensive analysis of the spatial residuals further revealed that TPE optimization not only enhances average accuracy, but also mitigates the model’s prediction bias in fault zones and mineralize areas by improving the spatial distribution structure of errors. Based on this optimal architecture, we combined the ten highest-performing models from the optimization stage to generate a quantile-based susceptibility map, using the ensemble median as the central predictor. Uncertainty was quantified from three complementary perspectives: ensemble spread, class ambiguity, and classification confidence. Our analysis revealed spatial collinearity between physical uncertainty and absolute residuals. This suggests that uncertainty is more closely related to the physical complexity of geological discontinuities and human-disturbed zones, rather than statistical noise. In the analysis of super-threshold probability, the threshold sensitivity exhibited by the mining area reflects the widespread yet moderate impact of mining activities. By contrast, the fault zone continues to exhibit distinct high-probability zones, even under extreme thresholds. It suggests that fault-controlled deformation is more physically intense and poses a greater risk of disaster than mining activities. Finally, we propose an engineering decision strategy that combines uncertainty and residual spatial patterns. This approach transforms statistical diagnostics into actionable, tiered control measures, thereby increasing the practical value of susceptibility mapping in the planning of natural resource extraction. Full article

Phase consistency is a critical prerequisite for achieving high-precision time-series InSAR deformation retrieval. However, conventional SBAS-InSAR methods provide only limited consideration of phase consistency during the inversion process. Within the SBAS-InSAR workflow, two principal categories of error sources are primarily responsible for phase inconsistency, manifested as non-zero closure phase (NCP): (1) fading biases introduced during multilooking and filtering prior to phase unwrapping; and (2) unwrapping errors caused by large deformation gradients, low coherence, or inappropriate selection of unwrapping algorithms. To address these issues, this study introduces an improved SBAS-InSAR processing workflow, termed NCP-SBAS, designed to improve the accuracy of deformation field estimation and thereby enhance its applicability to geological hazard monitoring. The key idea of the method is to enforce phase consistency as a constraint, jointly accounting for the spatiotemporal characteristics of fading biases and the valid deformation signals, thereby enabling effective correction of NCP. To evaluate the effectiveness of NCP-SBAS, this study conducted a detailed analysis of deformation differences in Hualong County, Qinghai Province, before and after NCP correction, highlighting the significant advantages of the proposed approach. The results indicate that the influence of fading biases on deformation estimates depends on both the magnitude and direction of deformation, while unwrapping errors primarily lead to an underestimation of deformation. In addition, the study provides an in-depth discussion of how fading biases and unwrapping errors affect landslide monitoring and identification. Full article

The Dead Sea (DS) region has experienced a sharp increase in sinkhole formation in recent years, posing environmental and infrastructure risks. The Geological Survey of Israel (GSI) employs Interferometric Synthetic Aperture Radar (InSAR) to monitor sinkhole activity and manually map land subsidence along the western shore of the DS. This process is both time-consuming and prone to human error. Automating detection with Deep Learning (DL) offers a transformative opportunity to enhance monitoring precision, scalability, and real-time decision-making. DL segmentation architectures such as UNet, Attention UNet, SAM, TransUNet, and SegFormer have shown effectiveness in learning geospatial deformation patterns in InSAR and related remote sensing data. This study provides a first comprehensive evaluation of a DL segmentation model applied to InSAR data for detecting land subsidence areas that occur as part of the sinkhole-formation process along the western shores of the DS. Unlike image-based tasks, our new model learns interferometric phase patterns that capture subtle ground deformations rather than direct visual features. As the ground truth in the supervised learning process, we use subsidence areas delineated on the phase maps by the GSI team over the years as part of the operational subsidence surveillance and monitoring activities. This unique data poses challenges for annotation, learning, and interpretability, making the dataset both non-trivial and valuable for advancing research in applied remote sensing and its application in the DS. We train the model across three partition schemes, each representing a different type and level of generalization, and introduce object-level metrics to assess its detection ability. Our results show that the model effectively identifies and generalizes subsidence areas in InSAR data across different setups and temporal conditions and shows promising potential for geographical generalization in previously unseen areas. Finally, large-scale subsidence trends are inferred by reconstructing smaller-scale patches and evaluated for different confidence thresholds. 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

Synthetic Aperture Radar (SAR) images of the sea surface reveal a variety of oceanic and atmospheric phenomena. Automatically detecting and identifying these phenomena is essential for understanding ocean dynamics and ocean–atmosphere interactions. This study selected 2383 Sentinel-1 Wave (WV) mode images and 2628 Interferometric Wide swath (IW) mode sub-images to construct a semantic segmentation dataset covering 12 typical oceanic and atmospheric phenomena, with a balanced distribution of approximately 400 sub-images per category, culminating in a comprehensive dataset of 5011 samples. The images in this dataset have a resolution of 100 m and dimensions of 256 × 256 pixels. We propose Segformer-OcnP model based on Segformer for the semantic segmentation of these multiple oceanic and atmospheric phenomena. Experimental results demonstrate that Segformer-OcnP outperforms classic CNN-based models (U-Net, DeepLabV3+) and mainstream Transformer-based models (SETR, the original Segformer), achieving 80.98% mDice, 70.32% mIoU, and 86.77% Overall Accuracy, verifying its superior segmentation performance. Full article

This study introduces an amplitude-based method that applies Spatially Variant Apodization (SVA) to reduce sidelobes in Synthetic Aperture Radar (SAR) data. Unlike conventional approaches, the filter is applied only to the amplitude while preserving the original interferometric phase, thereby enabling accurate Persistent Scatterer Interferometry (PSI) for dam deformation monitoring in Stanford Method for Persistent Scatterers (StaMPS) software. The SVA filter is integrated as an additional processing step within the Sentinel Application Platform (SNAP) for the SentiNel Application Platform to Stanford Method for Persistent Scatterers (SNAP2StaMPS) workflow. Filtering is performed in two dimensions (azimuth and range) separately on the In-phase (I) and Quadrature (Q) components of the coregistered data using a Python-based implementation via SNAP-Python (snappy). By recombining the SVA-filtered and original I and Q components, the method modifies only the amplitude while leaving the phase unchanged. The approach is evaluated in a proof-of-concept case study of the Sorpe Dam in Germany, where an Electronic Corner Reflector - C Band (ECR-C) produces sidelobe artifacts that degrade the Sentinel-1 (S-1) descending time series. The results demonstrated a successful integration of SVA filtering into the SNAP2StaMPS framework, achieving sidelobe reduction and improved Persistent Scatterer (PS) detection without compromising phase quality. The number of sidelobe-affected PS decreased by 39.26% after SVA filtering, while the amplitude-based approach preserved the original phase and deformation values, with a Root Mean Square Error (RMSE) of approximately 0.38 mm. Overall, this novel amplitude-based SVA approach extends the SNAP2StaMPS workflow by reducing strong sidelobes while preserving phase information for dam monitoring at the Sorpe dam site. Full article

Ship targets are usually high-value targets, and synthetic aperture radar (SAR) moving ship indication is of great importance in maritime traffic monitoring. However, due to the motion of the ocean, maritime ships may have rotational motion in addition to the conventional translational motion. The rotational motion, including the yaw, pitch, and roll, will cause the signal characteristics of the ship to become very complex, which increases the difficulty of designing moving target indication methods. This paper studies the effect of each rotation motion on the ship’s signal characteristics in image domain for spaceborne multichannel SAR. Firstly, the range equation of an arbitrary scatterer on the ship with both rotational and translational motions is developed. Then, the influences of each rotation motion on the coefficients of the range equation and the scatterer’s along-track interferometric (ATI) phase are revealed. Finally, numerical experiments are conducted to investigate the effect of each rotation motion on the scatterer’s azimuth position shift, azimuth defocusing, azimuth sidelobe symmetry, and ATI phase, which are important parameters for moving target indication. Full article

Landslide risk assessment along high-altitude transportation corridors is critical for infrastructure resilience. This study presents an integrated framework combining Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) deformation data and machine learning (ML) to systematically identify and assess landslide susceptibility along the entire 245.5 km West Kunlun Highway. We first compiled a landslide inventory through visual interpretation and SBAS-InSAR analysis. Subsequently, fourteen causative factors were selected to construct and compare six ML models: random forest (RF), K-nearest neighbours (KNN), artificial neural network (ANN), gradient boosting decision trees (GBDT), support vector machine (SVM), and logistical regression (LR). Research findings indicate that along the Hotan–Kangziva Highway in the Western Kunlun Mountains, there exist 21 potential risk points for small-scale landslides, 12 for medium-scale landslides, and 5 for large-scale landslides, with hazard identification accuracy reaching 80%. The random forest model demonstrated outstanding performance, classifying areas with 5.10%, 4.55% and 4.96% probability as extremely high, high and medium susceptibility, respectively. This work provides a robust methodology and a high-accuracy assessment tool for landslide risk management in the data-scarce Western Kunlun Mountains. Full article

Surface subsidence, a major geological hazard induced by mining activities, severely compromises the sustainable economic development of mining areas and the safety and stability of residents’ livelihoods. Consequently, long-term and effective monitoring and prediction of mining areas are essential. Aiming to identify the key characteristic of narrow and elongated mining areas—where the strike length is significantly greater than the dip length—this study proposes a surface subsidence monitoring method integrating Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) and the Improved Probability Integral Method (IPIM). Specifically, this method utilizes SBAS-InSAR technology to acquire cumulative subsidence results of low-gradient deformation zones in mining areas. To address the issue of excessively fast edge convergence in traditional Probability Integral Method (PIM) applications for narrow and elongated mining areas, the traditional PIM is adjusted by modifying the dip-direction influence radius parameter; this adjustment alters the shape of the dip-direction subsidence curve at the edge of the subsidence basin, thereby resolving the convergence problem. Meanwhile, based on the InSAR deformation gradient theory, the subsidence edge and subsidence center are identified, and the corresponding threshold is determined. The results of SBAS-InSAR and IPIM are then fused via the inverse distance squared weighting (IDSW) method to eliminate discontinuous boundaries in fused results and obtain complete surface subsidence data of the mining area. Taking the 31109-1 working face of the Lijiahao Coal Mine as the study area, 14 scenes of Sentinel-1A imagery and field leveling data of the working face were used to validate the feasibility and accuracy of the proposed method. The results indicate that a distinct rectangular subsidence basin was formed in the working face during the monitoring period. The maximum subsidence measured by the integrated method is 3453 mm, and the location, subsidence curve, and variation trend of the monitored subsidence basin are basically consistent with actual mining conditions. The maximum relative errors of subsidence in the strike and dip directions are 5.2% and 4.1%, respectively. This method can effectively compensate for the limitations of SBAS-InSAR and PIM when applied individually to surface subsidence monitoring in narrow and elongated mining areas, enabling the acquisition of refined subsidence information for the entire mining basin. The research results provide a scientific basis for subsidence monitoring and early warning, disaster prevention and mitigation, and the rational development and utilization of resources in mining areas. Full article

Urban land subsidence poses a significant threat in rapidly urbanizing regions, threatening infrastructure integrity and sustainable development. This study focuses on Mandalay, Myanmar, and presents a novel clustering framework—Dynamic Time Warping and Trend-based Longest Common Subsequence with Agglomerative Hierarchical Clustering (DTLCS-AHC)—to classify spatiotemporal deformation patterns from Small Baseline Subset (SBAS) Interferometric Synthetic Aperture Radar (InSAR) time series derived from Sentinel-1A imagery covering January 2022 to March 2025. The method identifies four characteristic deformation regimes: stable uplift, stable subsidence, primary subsidence, and secondary subsidence. Time–frequency analysis employing Empirical Mode Decomposition (EMD) and Discrete Fourier Transform (DFT) reveals seasonal oscillations in stable areas. Notably, a transition from subsidence to uplift was detected in specific areas approximately seven months prior to the Mw 7.7 earthquake, but causal relationships require further validation. This study further establishes correlations between subsidence and both urban expansion and rainfall patterns. A physically informed conceptual model is developed through multi-source data integration, and cross-city validation in Yangon confirms the robustness and generalizability of the approach. This research provides a scalable technical framework for deformation monitoring and risk assessment in tropical, data-scarce urban environments. Full article

Underground gas storage (UGS) facilities may cause ground displacements as a result of the cavern convergence or regular gas injection (alternate ground uplift and subsidence). The occurrence and scale of displacements are strongly dependent on the storage time and cavern capacity. At an early stage of facility operation, displacements can be difficult to detect in the presence of wetlands. The main objective of this study was to describe the global and local relationships between vertical ground displacements observed over a small and relatively new Kosakowo UGS facility (Poland) from 2014 to 2024 (dependent variable) and selected topographic, hydrological, and mining factors (independent variables). The dependent variable was determined through SBAS-InSAR analysis of Sentinel-1 SAR data, while the independent variables were developed using passive Sentinel-2 imagery and open geospatial data. The global relationships between variables were described using Ordinary Least Squares (OLS) and Generalized Linear Regression (GLR) models, while the Geographically Weighted Regression (GWR) model was utilized to analyze local relations. The results obtained indicate that ground displacements were characterized by seasonal fluctuations between 4 mm and 10 mm. The factors that had, both globally and locally, the strongest influence were soil moisture, vegetation water content, and the flora condition, indicating that the environmental hydrogeology had the greatest impact on the phenomenon under study. None of the considered models identified underground gas storage as a significant contributing factor to the observed ground displacements. The results confirm that the presence of wetlands can be a significant obstacle to an accurate description of the impact of gas storage on the ground movements, especially in UGS areas at an early stage of operation. Full article