Search Results (74)

Oil and gas pipelines are crucial component of the strategic infrastructure in China, but they are severely threatened by geological disasters in complex terrains. These disasters may cause pipeline rupture, leakage or explosion, resulting in significant economic losses, environmental pollution and casualties. Traditional manual disaster investigation is inefficient because the pipelines are widely distributed, access is limited and the terrain may be rugged. Therefore, efficient and accurate disaster identification and risk assessment have become a priority that the industry urgently needs to address. Taking the Jiangxi section of the West Line II Zhangshu–Xiangtan connection line as the research area, this study combines the SBAS-InSAR technology with spatial analysis based on GIS to support early disaster identification, surface deformation monitoring and vulnerability assessment. The analysis of 48 Sentinel-1A satellite images shows that the regional ground deformation range is −19.5 to 19.1 mm per year, and most areas show a slow deformation of within ±10 mm per year. The preliminary visual interpretation of the SBAS-InSAR ground deformation data yields 121 preliminary high-deformation disaster points. Combined with the 9 key assessment factors in the GIS platform and the entropy-weighted information model obtained from the geological disaster susceptibility evaluation map and using the optical remote sensing images, 21 human interference points are excluded, and finally 100 potential geological disaster hazard areas are retained. Field verification was conducted through ground reconnaissance surveys and confirmed that 78 of these areas have geological disaster hazards such as landslide, collapses, and slope water damage, providing solid technical support for geological disaster management, monitoring and early warning along the pipeline route. This study proposes a multi-source integrated framework combining SBAS-InSAR, GIS-based susceptibility assessment, and optical validation for improving the reliability of early geological hazard identification. Full article

The role of unmanned aerial vehicles (UAVs) in time-sensitive missions such as low-altitude reconnaissance and disaster rescue has gained increasing significance. To address the challenge of visual localisation for UAVs operating in complex terrains under Global Navigation Satellite System (GNSS)-denied environments, this paper proposes an improved adaptive gradient-damped Newton approach to mitigate the trade-off between terrain non-convexity and computational real-time performance. The proposed approach incorporates a terrain-gradient-based dynamic step-size adjustment mechanism that adaptively captures non-linear terrain characteristics in real time and effectively reduces the numerical oscillations typically observed in steep regions when using the standard Newton method. In addition, a tightly coupled vision–geometry framework was developed to constrain cumulative drift during long-range flight. Monte Carlo simulation results demonstrate that the proposed algorithm maintains submeter localisation accuracy while achieving approximately a three-fold improvement in computational efficiency compared with traditional grid-based methods, and a 27.4% increase in convergence speed relative to the standard Newton method. Experiments conducted under high-noise conditions and highly undulating terrains indicate that the approach exhibits strong convergence stability, offering a computationally efficient and robust solution for UAV navigation. Full article

Thermal infrared remote sensing offers a cost-effective means of regional geothermal reconnaissance, yet a fundamental challenge remains: isolating the weak geothermal surface signal (typically 1–3 °C) from dominant surface noise introduced by seasonal temperature cycles (annual amplitude > 20 °C), topographic variability, land cover heterogeneity, and irregular cloud-affected satellite sampling. Conventional single-scene or arithmetic-mean approaches are highly susceptible to these confounding factors and frequently produce pseudo-anomalies that obscure genuine geothermal targets. To overcome this limitation, we propose a physics-based time-series framework in which a nonlinear annual temperature variation model, T(t) = T0 + A·sin(2πt/τ + φ), is fitted to multi-temporal Landsat 8 thermal infrared data via the Levenberg–Marquardt algorithm. Applied to ~50 cloud-free scenes (2021–2022) processed on the Google Earth Engine over the Shanxi Graben System, northern China, the model simultaneously retrieves the background temperature parameter T0 and seasonal amplitude A—two physically interpretable quantities that encode distinct geothermal signatures more robustly than simple temporal statistics. Sub-regional corrections for the elevation (−4 °C/100 m above 800 m), aspect (R² > 0.95 in piecewise linear segments), and slope further suppress topographic pseudo-anomalies prior to anomaly extraction. Over known high-temperature geothermal fields (Tianzhen and Yanggao; >100 °C at 100 m depth), the method reveals clear T0 offsets of +1–2 °C (3–5% relative) and amplitude deficits of ~2 K (5–10% relative) relative to the background, with model-fitted T0 values averaging ~2 °C higher than arithmetic means due to the correction for seasonal sampling bias. Combined with 5 km fault-proximity buffers, extracted anomaly zones align well spatially with known geothermal sites and major structural corridors of the graben system. However, deeper low-temperature systems (45–50 °C at 300–500 m depth) produce ambiguous signals below the ~1.5 K detection threshold, indicating inherent limitations for deeply buried resources. The fully reproducible, training-data-free workflow is implementable via open satellite archives and cloud computing platforms, making it a transferable low-cost tool for structurally controlled geothermal reconnaissance across extensional basins worldwide. Full article

Recent advancements in satellite optical reconnaissance have elevated the sun angle to a critical factor in orbital pursuit-evasion games. The stringent imaging constraints imposed by sun angle and relative distance induce non-smoothness in the terminal region of such differential games, significantly complicating equilibrium-solution derivation. To address this challenge, we formulated a novel differential game model where the pursuer minimizes the time-to-entry into the evader’s effective imaging region. We first constructed a sequence of low-dimensional manifolds that collectively cover the terminal region, solving the differential game with this sequence to yield the Nash equilibrium. Subsequently, we approximated the terminal region using a smooth manifold of identical dimensions, enabling a computationally efficient approximate solution. Both methodologies demonstrate broad applicability to orbital differential games featuring non-smooth terminal regions. Simulation results confirm that the approximation error becomes pronounced only under extreme initial sun angles, though this error remains acceptable for practical space reconnaissance applications. Full article

Cross-view image geolocalization aims to achieve accurate localization of geo-tagged images without geo-tagging by matching ground-view images with satellite images. However, there are huge imaging differences between ground and satellite viewpoints, and existing methods usually rely on a large number of accurately labeled cross-view image pairs. Therefore, to address issues such as significant perspective differences, high annotation costs, and low utilization of unpaired data, this paper proposes a cross-view generation model that integrates multi-scale contrastive learning and dynamic optimization, designs a multi-scale contrast loss function to strengthen the semantic consistency between the generated images and the target domain, adaptively balances the quality and quantity of pseudo-labels according to a dynamic threshold screening mechanism, and introduces a hard-sample triplet loss to enhance the model discriminative ability. Ablation experiments on the CVUSA and CVACT datasets show that the BEV-CycleGAN+CL (Bird’s-Eye View Cycle-Consistent Generative Adversarial Network with Contrastive Learning) model proposed in this paper significantly outperforms the comparative models in PSNR, SSIM, and RMSE metrics. Specifically, on the CVACT dataset, compared with the BEV-CycleGAN, BEV, and CycleGAN baselines, PSNR increased by 2.83%, 16.02%, and 42.30%, SSIM increased by 6.12%, 8.00%, and 18.48%, and RMSE decreased by 9.28%, 15.51%, and 25.35%, respectively. Similar advantages are observed on the CVUSA dataset. Compared with current state-of-the-art models, the dynamic threshold pseudo-label localization method in this paper demonstrates overall superiority in recall metrics such as R@1, R@5, R@10, and R@1%, for example achieving an R@1 of 98.94% on CVUSA, outperforming the best comparative model, Sample4G^†, which reached 98.68%. This study provides innovative methodological support for disaster emergency response, high-precision map construction for autonomous driving, military reconnaissance, and other applications. Full article

Synthetic aperture radar (SAR), which appeared in the early 1990s, refers to a technology that creates a virtual large aperture by receiving/combining signals from various locations while moving with a fixed antenna. Using SAR-based image acquisition technology, a reconnaissance satellite can obtain high-quality images regardless of the weather and day/night conditions. In this study, the qualification tests of a space-grade 5m-class deployable reflector antenna for satellites, which is the primary payload of a SAR-based satellite, were conducted. In order to ensure the electrical performance of the reflector antenna, an alignment verification test was performed using a laser tracker system during the assembly and integration process. Generally, the satellite experiences a considerable amount of structural load under the launch condition and is exposed to extremely low- and high-temperature thermal environments under the orbital condition. For the space mission, environmental tests should be conducted to verify the structural/thermal stability for the launch and orbital conditions. A deployment repeatability test was conducted to ensure that the deployment mechanism operated properly before/after each test. The qualification process and philosophy proposed in this work could be applied to the development of the space-grade deployable reflector antenna. Full article

The accumulation of space debris in near-Earth orbit, particularly in Low Earth Orbit (LEO), poses an increasing threat to satellite operations, communication infrastructures, and long-term space sustainability. As modern constellations expand and incorporate advanced satellite technologies, including sensing and wireless communications, artificial intelligence-of-things (AIoT), enabled payloads, and edge computing for on-orbit data processing, the risk profile grows. This paper reviews the current debris environment and existing sensing and monitoring techniques, highlights major collision events and deliberate debris-generating activities, and analyzes the role of both governmental and commercial satellite constellations in exacerbating and mitigating the challenges. Emerging space surveillance and tracking (SST) techniques, leveraging radar, optical sensors, and interferometric SAR for enhanced intelligence, surveillance, and reconnaissance (ISR), are highlighted alongside software-defined networking (SDN) approaches and cloud communication technology that enable coordinated debris-avoidance maneuvers. Key international regulatory frameworks, tracking architectures, and mitigation measures, including alignment with ISO 24113 standards, advanced TT&C capabilities, and evolving active debris removal technologies, are examined. The study emphasizes the necessity of a global, interoperable ecosystem that integrates AI/ML (artificial intelligence and machine learning)-driven situational awareness, secure SATCOM links with AJ/LPI/LPD (anti-jamming/low probability of interception/low probability of detection) characteristics, and collaborative protocols among space agencies, commercial operators, and regulatory bodies to ensure the sustainable use of orbital space for future generations. Full article

Surge-like glacier instabilities in Central Asia remain underexplored, particularly in regions of mild instability or smaller glaciers. In 1980, two leading Soviet glaciologists proposed a classification method (GS1980) to calculate the spatial distribution of “pulsating” glaciers in the Hissar–Alay range, predicting a 20% prevalence of unstable flow and claiming highly accurate detection. These findings were unconfirmed in subsequent studies, which typically reported fewer than 10 surge-type glaciers in the region. Here, we address this discrepancy by reassessing the GS1980 predictions using a newly compiled multi-sensor satellite dataset covering nearly six decades. We systematically examine glacier dynamics in the region, assessing ice flow instabilities from changes in terminus position, ice thickness, and surface morphology. We identify 171 glaciers that exhibit pulsating behavior, corresponding to 25% of the sample—in broad agreement with GS1980. Flow instabilities tend to be modest in scale, with slow advances and long active phases (mean duration of 14 years). We find that the GS1980 model shows some ability to distinguish pulsating from stable-flowing glaciers; however, its predictive power is lower than claimed due to the simplifying assumptions of its morphology-based approach and the uncertainties in the input data. Our results indicate that pulsations in the region are more widespread than previously reported, but fall at the weaker end of the spectrum of glacier instability, which may not be well represented by a sharp binary classification (surge-type versus stable). As more detailed satellite records become available, we suggest that a more nuanced framework may be useful to recognize and interpret subtler instabilities of small glaciers. Full article

This study evaluates the capability of multi-temporal vegetation indices derived from Sentinel-2 imagery to indicate groundwater discharge in a forested mountainous sector of the Northern Apennines (Italy). The NDVI was computed from Level-2A surface reflectance data (10 m resolution) and analyzed over five growing seasons (2017–2021), encompassing recurrent summer droughts. Aridity conditions were quantified using the Standardized Precipitation–Evapotranspiration Index (SPEI) derived from long-term meteorological records. The methodological framework integrates cloud-masked satellite observations, drought characterization, and spatial statistical comparison between known spring discharge zones and randomly distributed forested control points. NDVI values extracted within 100 m radius buffers, centered on spring outlets, were systematically compared with those from control areas located outside the shallow-water-table influence zone. During periods of negative SPEI (moderate-to-severe drought), spring-centered buffers consistently exhibited higher NDVI values than control sites, with the NDVI contrast increasing under severe arid conditions. This pattern indicates enhanced vegetation resilience supported by shallow groundwater availability. The results demonstrate that vegetation health anomalies, when constrained by homogeneous land cover and a consistent hydrogeological setting, can serve as indicators of the groundwater discharge likelihood. The proposed workflow provides a reproducible and cost-effective tool to support hydrogeological reconnaissance and spring inventorying in rugged mountainous environments where field-based surveys are logistically demanding. Full article

Efficient target detection in hyperspectral images faces significant deployment challenges on resource-constrained edge platforms due to the large data volume and high computational complexity of detection algorithms. This paper proposes a CEM target detection method based on 1D-CNN feature dimensionality reduction. A lightweight 1D-CNN reduces spectral dimensions from L bands to 16 features, decreasing the core matrix inversion complexity from $O\left(L^3\right)$ to $O\left({16}^3\right)$. Unlike PCA-based dimensionality reduction requiring online eigenvalue decomposition, the proposed approach employs fixed pre-trained weights with simple convolution operations, enabling high parallelizability for FPGA implementation. A Zynq-based PS + PL collaborative acceleration scheme is designed, deploying CNN on the PL side through RTL implementation and CEM on the PS side using double-precision floating-point computation. Experimental validation on multiple hyperspectral datasets demonstrates that the proposed method achieves an AUC of 0.9953 with less than 1% difference compared to traditional CEM, processes 40,000 pixels in approximately 10.8 s, and consumes only 2.067 W, making it suitable for power-sensitive edge applications such as UAV reconnaissance and satellite on-board processing. The system achieves a processing rate of 3704 pixels/s. Full article

Under the intensifying water shortages in the vegetation season, early identification of Ips typographus L. damage is crucial for preventing wide outbreaks, which undermine the economic potential of commercial stands of Norway spruce (Picea abies Karst.) across Europe. For this purpose, remote sensing based on satellite images is considered one of the most efficient methods, particularly in homogenous and wide forested landscapes. However, under highly heterogeneous seminatural managed forest landscapes in lowland Central and Northern Europe, as illustrated by the eastern Baltic region and Latvia in particular, the efficiency of such an approach can lack the desired accuracy. Hence, the identification of smaller damage patches by I. typographus, which can act as a source of wider outbreaks, can be overlooked, and situational awareness can be further aggravated by infrastructure artefacts. In this study, the accuracy of satellite imaging for the identification of I. typographus damage was evaluated, focusing on the occurrence of false positives and particularly false negatives obtained from the comparison with UAV imaging. Across the studied landscapes, correct or partially correct identification of damage patches larger than 30 m² occurred in 73% of cases. Still, the satellite image analysis of the highly heterogeneous landscape resulted in quite a common occurrence of false negatives (up to one-third of cases), which were related to stand and patch properties. The high rate of false negatives, however, is crucial for the prevention of outbreaks, as the sources of outbreaks can be underestimated, burdening prompt and hence effective implication of countermeasures. Accordingly, elaborating an analysis of satellite images by incorporating stand inventory data could improve the efficiency of early detection systems, especially when coupled with UAV reconnaissance of heterogeneous landscapes, as in the eastern Baltic region. Full article

Very-Low Earth Orbit Synthetic Aperture Radar (VLEO SAR) satellites, defined as SAR satellites operating at orbital altitudes 350 km or below, offer distinct technical advantages compared to conventional SAR satellites. Equipped with a high-resolution SAR payload, the Haishao-1 (HS-1) satellite was successfully launched on 4 December 2024. According to publicly available information, the HS-1 satellite represents the world’s first VLEO SAR satellite and has successfully demonstrated 1-m resolution Stripmap mode imaging with continuous azimuth coverage. Through an analysis of the HS-1 satellite’s system parameters and imaging results, this paper comprehensively explores the advantages of VLEO SAR satellites over traditional orbit SAR satellites, particularly in terms of enhanced resolution, reduced payload costs, and improved constellation deployment capabilities. VLEO SAR satellites possess significant advantages, including the potential for higher-resolution imagery and lower-cost payload designs, positioning them for extensive application prospects in fields such as space-based military reconnaissance, natural resource surveying, and natural disaster monitoring. Full article

Accurate aircraft detection in remote sensing imagery is critical for aerospace surveillance, military reconnaissance, and aviation security but remains fundamentally challenged by extreme scale variations, arbitrary orientations, and dense spatial clustering in high-resolution scenes. This paper presents an adaptive attention-guided super-resolution network that integrates multi-scale feature learning with saliency-aware processing to address these challenges. Our architecture introduces three key innovations: (1) A hierarchical coarse-to-fine detection pipeline that first identifies potential regions in downsampled imagery before applying precision refinement, (2) A saliency-aware tile selection module employing learnable attention tokens to dynamically localize aircraft-dense regions without manual thresholds, and (3) A local tile refinement network combining transformer-based super-resolution for target regions with efficient upsampling for background areas. Extensive experiments on DIOR and FAIR1M benchmarks demonstrate state-of-the-art performance, achieving 93.1% AP₅₀ (DIOR) and 83.2% AP₅₀ (FAIR1M), significantly outperforming existing super-resolution-enhanced detectors. The proposed framework offers an adaptive sensing solution for satellite-based aircraft detection, effectively mitigating scale variations and background clutter in real-world operational environments. Full article

This paper represents the results of ten years of monitoring using satellite imagery and aerial reconnaissance, followed by in-depth analysis utilizing LiDAR data and geomagnetic prospection techniques of the first two Neolithic rondels detected in Croatia—Markušica Brošov salaš and Gorjani Topole. Through the exclusive use of satellite and aerial image analysis, we were able to accurately determine the general size, shape, and number of ditches present at the sites under investigation. The wealth of information obtained from these images was sufficient for us to confidently interpret these formations as Neolithic rondels—meeting all the criteria commonly used. The addition of LiDAR data and geomagnetic prospection further enhanced our understanding by revealing a range of additional features and peculiarities across both sites, including within all identified ditch systems. These advanced methods allowed us to uncover details that would otherwise remain invisible through surface observation alone. Our research demonstrates the remarkable power of publicly available satellite imagery as a primary tool for archeological site detection and preliminary interpretation. The results from Markušica and Gorjani emphasize the scientific necessity of combining complementary remote sensing and geophysical techniques to overcome individual methodological limitations, providing robust documentation and interpretation of prehistoric enclosures in highly transformed landscapes. This research contributes novel insights into Neolithic social landscapes, monumentality, and land use strategies in Croatia while offering a methodological model for archeological prospection applicable across Central and Southeastern Europe. Full article

This case report presents a multidisciplinary forensic investigation into a cold case involving a missing person in Italy, likely linked to a homicide that occurred in 2008. The investigation applied a standardized protocol integrating satellite imagery analysis, site reconnaissance, vegetation clearance, ground-penetrating radar (GPR), and cadaver dog (K9) deployment. A dedicated decision tree guided each phase, allowing for efficient allocation of resources and minimizing investigative delays. Although no human remains were recovered, the case demonstrates the practical utility and operational robustness of a structured, evidence-based model that supports decision-making even in the absence of positive findings. The approach highlights the relevance of “negative” results, which, when derived through scientifically validated procedures, offer substantial value by excluding burial scenarios with a high degree of reliability. This case is particularly significant in the Italian forensic context, where the adoption of standardized search protocols remains limited, especially in complex outdoor environments. The integration of geophysical, remote sensing, and canine methodologies—rooted in forensic geoarchaeology—provides a replicable framework that enhances both investigative effectiveness and the evidentiary admissibility of findings in court. The protocol illustrated in this study supports the consistent evaluation of large and morphologically complex areas, reduces the risk of interpretive error, and reinforces the transparency and scientific rigor expected in judicial settings. As such, it offers a model for improving forensic search strategies in both national and international contexts, particularly in long-standing or high-profile missing persons cases. Full article