Search Results (5,174)

In high-resolution wide-swath (HRWS) spaceborne synthetic aperture radar (SAR) systems, clustered phased array (CPA) architectures alleviate hardware costs but face critical subarray synthesis challenges: manually prescribed pattern masks, redundant equality matching, and unrealizable crossover topologies. This paper proposes a mask- and contiguity-constrained power pattern matching method (MC-PMM) to address these limitations. First, a semidefinite programming (SDP) procedure constructs an SAR-metric-aware SDP-relaxed reference mask for the noise equivalent sigma zero (NESZ) and range ambiguity-to-signal ratio (RASR) requirements within the relaxed covariance space. Second, a dynamic programming (DP) strategy is integrated with a novel mask-constrained iterative projection method (Mask-IPM) to optimize physically contiguous partitions and weights. By transforming equality waveform fitting into mask-violation-based inequality matching, MC-PMM focuses restricted spatial degrees of freedom onto critical performance boundaries. Simulations on a 64-element array demonstrate that, at a subarray ratio of $3/4$, MC-PMM achieves a 100% crossover-free topology. Furthermore, relative to the conventional equality matching framework, it lowers the mean RASR by $24.50$ dB, with a negligible worst-case NESZ degradation of under $0.4$ dB. Full article

Rock glaciers are critical indicators of periglacial environments and the spatial distribution of mountain permafrost. Given their complex deformation patterns and temporal variability, which may indicate progressive destabilization, a quantitative evaluation of their kinematic activity is critical from both climatological and geohazard perspectives. This study applies Persistent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR) to Sentinel-1 radar imagery on both ascending and descending orbits, in order to detect and map moving areas (MA) within the Pirin Mountains (Bulgaria). The primary objective of this study is to update the existing rock glacier inventory (RoGI) by integrating high-resolution Line-of-Sight (LOS) velocity data in accordance with the latest international standards established by the Rock Glacier Inventories and Kinematics (RGIK) standing committee. A secondary objective is to investigate the spatial relationships between the identified moving areas (MAs) and other surrounding geomorphological features (e.g., talus slopes), hence providing a wider context for slope dynamics and landform evolution. The results identified MAs with PSInSAR-derived Line-of-Sight (LOS) velocities reaching up to 10 cm yr⁻¹, which were subsequently classified according to RGIK kinematic categories. A substantial proportion of the detected moving areas occur outside mapped rock glacier boundaries and may reflect a range of geomorphological processes, including permafrost-related creep, talus creep, or other forms of slope deformation. The LOS velocity data were used to assess the activity status of 74 rock glacier units within the regional inventory, classifying 8 as transitional (velocity exceeding 1 cm yr⁻¹) and 66 as relict. Furthermore, we analyse the spatial distribution of these moving areas in relation to primary topographic variables, such as elevation, aspect, and slope. The results highlight the influence of topographic control factors and rock glacier dynamics and provide new insights into the distribution of active periglacial landforms and terrain potentially affected by permafrost in the Balkan Peninsula under changing climatic conditions. Full article

Deep-learning-based few-shot synthetic aperture radar (SAR) ship detection has demonstrated considerable potential in scenarios with limited annotated samples and dynamically emerging ship categories, closely matching the practical requirements of maritime surveillance and intelligent SAR image interpretation. However, existing methods often overlook challenging negative proposals and the incomplete annotation problem inherent in few-shot learning. In complex offshore and inshore scenes, negative proposals contain either difficult background regions caused by sea clutter, port facilities, and strong scattering interference, or unlabeled ship targets resulting from missing annotations. Existing methods struggle to distinguish between these two types of proposals. Ignoring challenging negatives prevents the detector from learning precise decision boundaries between ships and complex backgrounds, whereas treating unlabeled ships as background introduces erroneous gradients during backpropagation and degrades detection performance. To address these issues, we introduce uncertainty as a measure of proposal reliability and propose two complementary components: uncertainty-guided proposal separation (UGPS) and uncertainty-aware discriminative gradient refocusing (UADGR). UGPS jointly exploits proposal uncertainty and intersection-over-union (IoU) to separate challenging negatives and confusing negatives from the negative proposal set, thereby preserving informative hard backgrounds while identifying potential unlabeled ships. Subsequently, UADGR combines proposal uncertainty with feature dissimilarity to a background prototype to adaptively regulate their training gradients. Specifically, higher weights are assigned to challenging negatives to improve discrimination between ships and complex background interference, whereas lower weights are assigned to confusing negatives to suppress erroneous supervision introduced by missing ship annotations. Extensive experiments on SRSDD-v1.0 demonstrate consistent improvements over existing few-shot detection approaches across different data splits and shot settings, while additional results on SAR-AIRcraft-1.0 further confirm the generalization of the proposed method. Full article

Fast synthetic aperture radar (SAR) imaging simulation is required by many computer vision applications. Although the Shooting and Bouncing Ray (SBR) method has significantly accelerated electric field calculation, the number of ray tubes is still the bottleneck for SAR image simulation speed. This paper proposes an innovative adaptive SBR method driven by Q-learning for accelerated SAR imaging simulation. The core strategy is to convert the ray tube allocation into a reinforcement learning problem. The ray-shooting plane is dynamically partitioned into localized patches, where a Q-learning agent intelligently scales the ray density in real time. By observing the geometric features of the target surface, the agent learns to employ coarser ray tubes in flat regions to eliminate redundant computation, while deploying denser ray tubes in complex areas. A multi-objective reward function is designed to balance accuracy against computational resource consumption. Numerical experiments demonstrate that the proposed Q-learning-based SBR method drastically reduces computational cost while preserving imaging similarity. Full article

Henan Province, a major coal producing region in China, faces severe surface subsidence induced by extensive underground mining, which compromises regional ecological security and infrastructure stability. In this study, small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) was applied to Sentinel-1A imagery acquired from March 2017 to February 2025 to characterize surface deformation in concentrated coal mining areas. A local validation was conducted within a representative mining area in Study Area 3 using measurements from 14 leveling benchmarks acquired between 5 May and 20 July 2023. The comparison yielded an $R^2$ of 0.816 and an RMSE of 9.22 mm, indicating good agreement between the SBAS-InSAR and leveling measurements during the validation interval. The subsidence in the study area exhibits significant spatial heterogeneity and continuous accumulation characteristics. The most negative approximate vertically projected deformation rate reached −371 mm/yr, and the maximum cumulative displacement reached −2101 mm. Scenario-based sensitivity analysis indicated potential projection errors of 6.76–8.34% for a horizontal-to-vertical displacement ratio of 0.10 and 20.28–25.01% for a ratio of 0.30, with larger uncertainty expected near subsidence trough margins. Given the difficulty of quantifying large-scale underground mining parameters, this study employs multisource environmental and topographic variables as auxiliary indicators and develops an XGBoost-SHAP model to evaluate their relative explanatory contributions to the spatial heterogeneity of mining-induced subsidence. Among the selected measurable environmental and topographic variables, groundwater table depth represents the most important measurable explanatory factor for the spatial heterogeneity of subsidence, with distinct response patterns between plain areas with thick unconsolidated layers and piedmont bedrock regions. Furthermore, wavelet coherence analysis identifies scale-dependent spatial associations between topography and subsidence. At the regional scale, elevation exhibits spatial correspondence with the geomorphological framework of contiguous subsidence basins. At the local scale, slope and aspect show localized associations with differential deformation gradients near the margins of subsidence troughs. Full article

Against the backdrop of global climate change, wildfires have emerged as key disturbance factors accelerating permafrost degradation. However, how wildfires affect ground-surface deformation, including spatial patterns and potential driving mechanisms, remains unclear. Therefore, in this study, the permafrost region in the northern Da Xing’an Mountains affected by the catastrophic Great Black Dragon Fire (1987) is taken as a case study. On the basis of Sentinel-1 SAR imagery acquired from 2016 to 2021, the small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technique was employed to derive surface deformation rates. These rates were combined with historical fire severity (dNBR) and topographic factors. Random forest and spatial autocorrelation analyses were used to evaluate the long-term association between wildfire disturbance and surface deformation and its potential controls. The results indicate that (1) thirty-five years after the wildfire, vegetation in the permafrost region had not fully recovered to prefire levels; (2) surface deformation from 2016 to 2021 was dominated by subsidence overall. When unburned patches within the same region were used as controls for climate-driven background subsidence, the proportion of areas experiencing severe subsidence (annual rate ≤ −50 mm yr⁻¹) reached 12.86% in high-severity fire zones, compared with 10.21% in unburned areas, suggesting that high-severity fires may amplify regional background subsidence; and (3) the random forest model had low explanatory power (R² = 0.03) and was therefore used for exploratory comparison of the selected predictors rather than for accurate prediction of surface deformation. Among the selected variables, dNBR had the greatest relative importance, followed by terrain ruggedness and slope, whereas the remaining spatial variability may reflect unmeasured hydrological and subsurface controls. This study provides a quantitative basis for understanding the wildfire-induced “abrupt degradation” of permafrost, defined here as disturbance-driven acceleration of thaw and subsidence beyond gradual climate-driven degradation, and contributes to understanding carbon–climate feedback mechanisms in permafrost regions. Full article

High-risk landslide extraction from remote sensing imagery is a fundamental task for geological disaster prevention, emergency response, and land-use planning in mountainous regions. Although deep-learning semantic segmentation has substantially advanced landslide detection from optical imagery, existing methods still suffer from three critical limitations: (i) Interferometric Synthetic Aperture Radar (InSAR) deformation data are treated as auxiliary channels and dominated by optical features during fusion; (ii) predicted masks exhibit fragmented boundaries and incomplete delineation due to the absence of deformation continuity constraints reflecting the physical coherence of slope movements; and (iii) heavy Transformer backbones hinder practical deployment over large areas. To address these issues, we propose a Deformation-Integrity-Guided Symmetric Fusion Network (DIGSFNet) for high-risk landslide extraction from InSAR and optical imagery. The framework consists of three components: a Symmetric Deformation-Aware Encoder (SDAE) that treats InSAR and optical modalities as equal information sources through modality-aware adapters and dynamic sparse cross-modal fusion; a Deformation Integrity Prior Decoder (DIPD) that imposes deformation continuity and boundary-gradient consistency as physical priors to enforce mask completeness and boundary accuracy; and a Lightweight Deployable Student Network (LDSN) obtained via cross-modal knowledge distillation and INT8 quantization for efficient inference. Experiments on the Nanning High-Hazard Landslide Segmentation (Nanning-HHLS) dataset and the public HAEFNet benchmark covering the Qinghai–Tibet–Sichuan landslide-prone regions show that the full DIGSFNet achieves state-of-the-art extraction accuracy, reaching 83.57% and 78.92% mIoU on the two datasets and surpassing the strongest competing method by 2.63 and 3.68 percentage points with a Recall of 91.48% on Nanning-HHLS, while its distilled lightweight student retains 79.24% mIoU at 218 frames per second after INT8 quantization, enabling efficient large-area operational deployment. Full article

Synthetic-aperture radar (SAR) ship detection is a fundamental task in maritime remote sensing, supporting wide-area surveillance, traffic monitoring, and emergency response under all-weather imaging conditions. Existing deep detectors mainly rely on spatial cues such as intensity, shape and context, but structured sea clutter and near-shore interference can still produce ship-like responses, while fine scattering details are weakened by deep downsampling. We address two practical representation limitations: incomplete preservation of shallow high-resolution details, and limited explicit modeling of local directional variation. To this end, we propose HMF-RTMDet, a shallow-neck spatial–frequency fusion detector. A P2 high-resolution path combines $C_2$ features with upsampled $P_3$ semantics. HybridMFBlock then processes the fused feature through a morphology branch and a trainable depthwise branch initialized by fractional Gabor templates, followed by channel-wise fusion. In the reported main HRSID run, HMF-RTMDet improves RTMDet-s from 67.9% to 72.6% in AP_50:95, from 90.2% to 94.2% in AP₅₀, and from 68.2% to 73.4% in AP_s. Across three runs, however, its AP_50:95 is 72.17 ± 0.38%, comparable to the SFS-Conv and MCU-only controls. The evidence therefore identifies the P2 path as the main gain source but does not establish a stable advantage for HybridMFBlock over these controls. On SSDD, overall AP_50:95 remains nearly unchanged and large-target performance decreases, defining an important boundary of the current design. Full article

Continuous Global Navigation Satellite System (GNSS) monitoring is essential for characterizing surface subsidence in underground coal-mining areas. However, monitoring-station site selection remains strongly dependent on empirical judgment. Furthermore, steep deformation gradients can cause interferometric synthetic aperture radar (InSAR) decorrelation, phase-unwrapping failure, and data gaps in areas where ground-based monitoring is most needed. This study develops a quantitative, multi-source remote-sensing-assisted framework for GNSS monitoring-station site selection under a short-baseline real-time kinematic (RTK) configuration. Sentinel-1A, Sentinel-2C, unmanned aerial vehicle (UAV) photogrammetric products, and road-network data were integrated to construct eight evaluation factors: normalized difference vegetation index (NDVI), slope, terrain ruggedness index, deformation intensity, cumulative subsidence, InSAR coverage, distance to the subsidence edge, and distance to roads. An analytic hierarchy process (AHP) was used as the primary suitability assessment framework, while a random forest (RF) model was introduced with a limited weight to provide auxiliary information only in InSAR data-sparse areas. Grid-level aggregation, three-component Gaussian mixture model (GMM) screening, minimum-distance thinning, field reconnaissance, and monitoring-network review were subsequently combined to convert the continuous suitability surface into deployable station candidates. The resulting high-suitability areas were concentrated mainly along subsidence margins and near InSAR coverage gaps, reflecting the combined effects of deformation representativeness, supplementary monitoring demand, observation conditions, and engineering accessibility. Sixteen candidate stations were identified, of which S12, S3, and S10 were finally recommended to improve the northern, southwestern, and southeastern coverage of the existing monitoring network, respectively. Sensitivity analyses confirmed that the overall suitability pattern and recommended-station rankings remained stable under moderate parameter perturbations. Comparison with two existing GNSS stations further showed that the station with the higher suitability score exhibited a higher fitted subsidence rate (0.438 versus 0.320 mm day⁻¹). Given the two-station and two-month validation dataset, this agreement represents preliminary consistency evidence rather than statistical proof of general effectiveness. The proposed framework links regional remote-sensing assessment with site-scale engineering review and monitoring-network optimization, providing practical decision support for GNSS deployment in underground mining-subsidence areas. Full article

Zero-shot SAR ship recognition aims to identify unseen ship categories without annotated SAR samples, offering a promising solution for open-category maritime remote sensing. Although synthetic aperture radar (SAR) provides all-weather and day-and-night observation capability, practical maritime surveillance often encounters newly emerging or rarely observed ship types with limited labeled data. Existing zero-shot recognition methods mainly rely on direct visual–semantic mapping, while overlooking the scattering-driven structural characteristics of SAR imagery and the semantic relationships among ship categories. This leads to two key challenges: weak alignment between SAR visual features and textual attributes, and semantic isolation of unseen categories in the embedding space. To address these issues, we propose a prototype-driven semantic tree framework with a bimodal embedding space for zero-shot SAR ship recognition. First, a Bimodal Embedding Space Construction (BESC) module is designed to learn structure-aware visual embeddings from SAR images and dependency-aware semantic embeddings from textual attributes, and align them within a unified embedding space. Second, a Prototype-Driven Semantic Tree (PDST) module organizes class prototypes into a hierarchical structure and refines them through tree-guided propagation, enabling structured knowledge transfer among related ship types. During inference, an unseen SAR image is classified by matching its visual embedding with the refined semantic prototypes. Experiments on the FUSAR and SRSDD datasets show that BESC-PDST improves the harmonic mean from 0.424 to 0.448 and from 0.509 to 0.541, respectively, outperforming representative zero-shot recognition methods. These results demonstrate the effectiveness of the proposed framework for knowledge-driven open-category SAR ship understanding. Full article

Urban wetlands and agricultural lands are among the most important socio-ecological systems in urban areas. They support food production, biodiversity conservation, water regulation, climate resilience, and human well-being. However, rapid urbanization negatively affects wetland–agricultural coexistence (WAC) by destroying habitats, fragmenting landscapes, and intensifying land-use conflicts. Our study proposed a Wetland–Agriculture Coexistence Index (WACI) to assess the spatial pattern of WAC in Colombo, Sri Lanka. We considered four components for the WACI framework: wetland condition (WC), agricultural condition (AC), urban pressure (UP), and hydrological connectivity (HC). The WACI was developed using Landsat 8/9, Sentinel-1 Synthetic Aperture Radar (SAR), and Advanced Land Observing Satellite (ALOS) data, along with road network, population, and hydrological network data. Variables used in this study include land surface temperature (LST), Enhanced Vegetation Index (EVI), Modified Soil-Adjusted Vegetation Index (MSAVI), Bare Soil Index (BSI), soil moisture, elevation, slope, population density, distance to roads, hydrological connectivity, and built-up %, which were normalized and integrated into four dimensions using an equal-weighted approach to develop the WACI. The results showed substantial spatial heterogeneity in WAC across Colombo. The average WACI was 0.51, indicating a moderate WAC. This study further identified that favorable environmental conditions, rich hydrological connectivity, and low urban pressure increased coexistence potentials. The spatial pattern of WACI identified priority areas for conservation, restoration, and sustainable urban planning implications in Colombo. Our WACI framework provides a practical and transferable method for assessing WAC in urban areas. The findings of this study support balanced urban wetland–agricultural management, conservation, food security, and long-term sustainability of rapidly urbanizing cities. Full article

Geotechnical hazards such as landslides, subsidence, slope instability, and infrastructure deformation threaten rapidly urbanising and environmentally stressed regions worldwide, intensifying the need for scalable and intelligent monitoring systems capable of continuously observing complex Earth surface dynamics. Although multisensor remote sensing fusion has substantially expanded the observational capabilities of modern geotechnical monitoring through the integration of Synthetic Aperture Radar (SAR), optical imagery, Light Detection and Ranging (LiDAR), and environmental data, existing fusion pipelines remain subject to several well-documented constraints, including weak semantic alignment, limited temporal reasoning, and poor transferability across heterogeneous environmental conditions. This review synthesises the emerging transition from conventional sensor-centric fusion toward intelligent geospatial monitoring architectures centred on deep multimodal representation learning, transformer-based temporal reasoning, self-supervised learning, and geospatial foundation models. Particular emphasis is placed on how recent architectures are designed to better preserve coherent spatial, temporal, and contextual environmental relationships within unified latent representation spaces rather than through downstream handcrafted integration. The review further examines the growing role of multimodal transformers, masked autoencoders, contrastive learning, and large-scale geospatial foundation models in enabling scalable environmental reasoning, adaptive multimodal learning, and transferable geospatial intelligence across sensing modalities and geographic domains. Finally, remaining challenges involving uncertainty, explainability, computational scalability, and environmental generalisation are discussed alongside future research directions involving continual learning, physics-aware artificial intelligence, and autonomous geotechnical monitoring systems. Together, the reviewed literature suggests that multimodal Earth observation is evolving from passive environmental sensing toward adaptive geospatial intelligence systems capable of scalable hazard reasoning and autonomous environmental understanding. Full article

Bistatic Synthetic Aperture Radar (BiSAR) is an advanced radar imaging system in which the transmitter and receiver platforms are positioned at distinct spatial locations. This separated transmit–receive architecture enables coordinated observation of the target scene. In particular, the highly squinted spaceborne bistatic configuration offers advantages in multi-angle observation, overcoming the insensitivity of conventional spaceborne interferometric SAR (InSAR) to north–south surface deformations, thereby enabling efficient and high-precision measurement of global three-dimensional (3D) surface deformations, which holds significant engineering application value. Focusing on the highly squinted spaceborne BiSAR imaging geometric model, this paper proposes a novel highly squinted imaging method based on a high-order model. Traditional imaging algorithms are founded on straight-line models and employ the method of series reversion (MSR) to achieve imaging. In contrast, the proposed method is specifically tailored to the highly squinted bistatic observation geometry, fully accommodating orbital curvature effects while simultaneously resolving the imaging challenges posed by two-dimensional (2D) spatial variations of imaging parameters. In this method, control points are judiciously distributed within the observation scene, and the imaging parameters are solved via high-order polynomial fitting. Based on this foundation, the 2D spectrum expression for the highly squinted bistatic configuration is rigorously derived, together with the frequency-domain resampling mapping relation that compensates for the 2D spatial variation of imaging parameters, thereby achieving full-scene high-accuracy focused imaging. The proposed approach broadens the applicability of conventional straight-line-model-based algorithms and is well suited for highly squinted bistatic SAR imaging. The validity of the method is ultimately demonstrated via extensive simulation experiments and thorough performance evaluations. Full article

Reference labels for crop-type mapping are frequently coarse, and administrative land-use registries such as Japan’s eMAF (electronic Map of Agriculture and Forestry) database routinely group agronomically distinct crops under broad, ambiguous categories. This study addresses that problem for the Nakasatsunai region of Hokkaido, Japan, by combining Sentinel-1 synthetic aperture radar (SAR) time series with field-level optical vegetation-index analysis in a modular processing pipeline. The principal novelty of the work is not the pipeline architecture alone but a three-step, Normalized Difference Vegetation Index (NDVI)-driven label-refinement procedure—automatic removal of non-growing or low-amplitude field samples, Euclidean k-means subclass discovery within each coarse label, and trajectory-based label correction—that converts noisy nine-class eMAF labels into a more reliable training set prior to classifier training. The feature set combines the Radar Vegetation Index (RVI), VV and VH backscatter, the γVH/γVV polarization ratio, and NDVI, together with temporal-shape descriptors (phenological timing, peak magnitude, amplitude, maximum slope, and area under the curve) derived from monthly growth trajectories over the 2018 growing season. A Random Forest classifier, together with a gradient-boosting comparator, is evaluated before and after preprocessing under stratified k-fold cross-validation. Across n = 1208 field samples spanning the nine eMAF classes, classification accuracy improved from an overall accuracy of 71.8% on the raw labels to 82.6% after the three-step refinement; Cohen’s kappa increased from 0.63 to 0.77. Correlation analysis indicates that γVH/γVV tracks field-level NDVI more consistently (mean Pearson r = 0.68) than RVI does (mean Pearson r = 0.43) across the eight classes with sufficient samples, motivating its use as a SAR-only phenological proxy; this comparison is extended to the polarimetric PRVI, DPSVI, and DpRVI indices in the discussion. The underlying 80–90% label-accuracy estimate is derived from NDVI trajectory inspection rather than independent, field-surveyed ground truth, and a factorial ablation is used to characterize, to the extent the cross-validated evidence allows, how much of the reported accuracy gain is attributable to label-error correction as opposed to NDVI–SAR feature fusion; both this attribution and the label-accuracy estimate itself are identified as priorities for field validation in future work. The proposed framework is intended to convert coarse, noisy crop labels into a structured and reliable dataset while producing interpretable, field-level phenological insight for agricultural monitoring. Full article

Failures at large-scale open-pit mines and tailing dams pose critical risks to human life, environmental systems, and economic sustainability. Although Interferometric Synthetic Aperture Radar (InSAR) has proven effective in detecting long-term surface deformation, a scientifically robust early-warning framework has not yet been established because standardized quantitative thresholds that are capable of distinguishing benign consolidation settlement from instability-driven deformation remain unavailable.InSAR has proven effective for detecting long-term surface deformation. However, a scientific early-warning framework has not yet been proposed or developed due to the absence of standardized quantitative thresholds that distinguish benign consolidation settlement from instability-driven deformation. This research proposes a novel InSAR-based Failure-Risk Index (FRI) that integrates displacement, velocity, and, most importantly, deformation acceleration into a single, normalized metric as an early warning system for mining infrastructure instability. The framework that we propose (i) emphasizes acceleration as a leading indicator of change in mechanical regime, (ii) incorporates a statistically guided separation of long-term consolidation settlement from anomalous deformation based on baseline variability, (iii) applies a statistical standardization and change-point detection system. The methodology is validated through a retrospective analysis of the heap leach failure—that occurred in Çöpler Gold Mine in Erzincan, Türkiye, on 13 February 2024—by using a set of Sentinel-1 time-series images collected between 2014 and 2024. The results prove that while displacement and velocity remained within ranges typically interpreted as stable, deformation acceleration exhibited a statistically significant increase that began around 2020, exceeded baseline variability by approximately two orders of magnitude, which is approximately four years before the collapse, and marked the onset of tertiary creep and progressive instability. The proposed FRI framework successfully captures this transition and provides a transferable, meaningful early-warning framework to support proactive risk management and improve the safety of mining infrastructure. Full article