Search Results (1,132)

Riming and aggregation are critical ice-phase microphysical processes in winter clouds, but their overlapping signatures and dynamic transitions pose challenges for conventional single-frequency radar detection. We introduce a novel gradient-based identification method using ground-based triple-frequency dual-polarization radar observations. By analyzing vertical gradients of triple-frequency radar variables, rather than their absolute values, we discern these microphysical processes through physically based thresholds that reflect particle growth regimes. This approach captures subtle spatiotemporal variations in riming and aggregation that conventional threshold methods would miss, particularly in resolving layered riming-aggregation transitions. The dynamic gradient-based method demonstrates enhanced physical consistency and adaptability near process boundaries, thereby improving the tracking of ice-particle evolution. These advances provide a pathway to refine microphysical parameterizations and enhance high-resolution snowfall forecasting. Full article

The time available for a counter-drone system to detect an unauthorized aircraft and determine an appropriate response can be limited. Although cloud processing remains useful for storage and offline analysis, communication delays may constrain its use in time-critical decision loops. This paper proposes EdgeTwin-DRL, an edge-assisted digital twin framework that integrates multimodal sensing and multi-agent deep reinforcement learning (DRL) for counter-drone detection and response optimization. The digital twin maintains a synchronized representation of the protected airspace using radar, electro-optical/infrared (EO/IR), radio-frequency (RF), and acoustic observations. This synchronized state is used by cooperative DRL actors to adjust computational-resource allocation, detection sensitivity, and candidate countermeasures, while a model-based forward-evaluation assesses proposed responses before they are passed to the simulated response pathway. The framework is evaluated in a simulation testbed in which the RF sensing models are calibrated and independently validated using publicly available datasets, while the remaining sensing components are parameterized using published experimental measurements. Within this calibrated simulation environment, EdgeTwin-DRL achieved a false-positive rate of 1.4% and reduced mean detection-to-response latency by up to 72% relative to the Cloud-DRL baseline and by 26% relative to the MAPPO baseline without calibrated, environment-dependent sensing under the communication and computational assumptions used in the simulator. The evaluation was conducted across modeled urban, suburban, and open-field conditions. These results demonstrate the comparative performance of the proposed architecture within the simulated environment and motivate further investigation of edge-assisted digital twins for counter-drone decision support. Hardware-in-the-loop and controlled field validation are required before operational deployment. Full article

Multi-modal synthetic aperture radar (SAR)–optical object detectors raise detection accuracy by fusing complementary physical responses, but require both modalities to be simultaneously available at inference. When the optical stream becomes unavailable—under heavy cloud cover, night-time conditions, or downlink disruption—the detector reverts to SAR-only operation and accuracy degrades sharply. A natural remedy is to distil a multi-modal teacher into a SAR-only student via privileged-information knowledge distillation. However, we observe that the leading channel-wise feature-level method (CWD) reduces the student’s accuracy below the non-distilled baseline, with its smallest-target AP collapsing to near zero, because SAR speckle and target high-frequency edges share the same band and the alignment loss is dominated by broadband speckle energy. We refer to this failure mode as the speckle-fitting trap, formalize it as a gradient-pollution effect, and validate it through spectral and feature-manifold diagnostics. To counter the trap, we propose M-FSAD-KD, a full-link distillation framework whose neck-stage Fourier-gated alignment transfers low-frequency structural content while preserving target-edge high-frequency content; a joint spatial–channel attention mask, a shallow backbone adapter, and a response-level knowledge distillation (KD) term complete the chain. With a MAIENet teacher on OGSOD-1.0, the advantage of M-FSAD-KD over the strongest response-level KD baseline scales with student capacity: it matches KD on a 2.39 M-parameter student (both ≈48% mean average precision at an intersection-over-union (IoU) threshold of 0.5 (${\mathrm{mAP}}_{50}$), averaged over multiple seeds) and exceeds it by 2.0 absolute points on a 19.98 M-parameter student ($+8$ over the non-distilled baseline), where it is the best of all distillation methods; at full convergence the 19.98 M-parameter student reaches 81.9% ${\mathrm{mAP}}_{50}$, within 8.9 absolute points of the multi-modal teacher. A frozen-feature transfer test to an out-of-domain SAR benchmark (SSDD ship detection) further shows that distilling from the multi-modal teacher yields substantially more transferable SAR features—about ten absolute points above the non-distilled backbone—with M-FSAD-KD transferring best. Cross-architecture validation with a dual-stream DEYOLO teacher yields 48.6% ${\mathrm{mAP}}_{50}$ at the student—1.1 absolute points below the MAIENet result—indicating that the framework transfers across the two representative teacher architectures tested (single-stream and dual-stream). Full article

Millimeter-wave (mmWave) radar-based gesture recognition has attracted increasing attention for real-time human–computer interaction owing to its robustness to illumination changes, privacy-preserving sensing capability, and suitability for embedded deployment. However, existing single-stream models often couple heterogeneous point-cloud and temporal statistical features, which may limit their ability to capture fine-grained motion patterns and key action frames. To address this problem, this paper proposes a dual-stream long short-term memory network (LSTM) and a bidirectional gated recurrent unit (BiGRU) with an attention mechanism (Attention-BiGRU) network for mmWave radar-based hand gesture recognition, termed as DSTG-Net. In the proposed DSTG-Net framework, an LSTM branch is used to process radar point-cloud sequences and extract fine-grained spatio-temporal features, while an Attention-BiGRU branch models global motion trends from statistical and temporal-difference features. The attention mechanism in the Attention-BiGRU branch is introduced to emphasize discriminative frames during gesture transitions, and the complementary features from the two branches are fused through feature concatenation for final classification. The proposed method is evaluated on a public mmWave radar gesture dataset to verify its recognition performance, and an additional self-built near-field dataset is used to test its effectiveness under a constrained acquisition scene. The proposed method achieves recognition accuracies of 97.40% and 98.75% on the two datasets, respectively, outperforming several baseline models. The Raspberry Pi-based implementation with a TI IWR1642 radar confirmed the functional feasibility of the proposed pipeline. Full article

This study presents a comprehensive statistical characterization of cloud vertical structure and phase occurrence over Limassol, Cyprus, using 18 months of continuous ground-based remote sensing observations from the Cyprus Cloud Aerosol and Radiation Experiment (CyCARE) campaign (October 2016–March 2018). The Eastern Mediterranean represents a climatologically complex, understudied subtropical region characterized by strong seasonal variability and frequent exposure to diverse aerosol mixtures, including mineral dust, biomass-burning smoke, marine particles, and anthropogenic pollution. Utilizing the standardized Cloudnet target classification framework, synergistically combining lidar and cloud radar observations, 1,393,659 vertical profiles are analysed, finding that 35% contained hydrometeors. Within these profiles, ice-phase targets occurred in 83.8% of cloud-containing profiles, followed by mixed-phase (33.5%) and liquid-phase (29.2%) targets, with precipitation detected in 32% of cases (these percentages are not mutually exclusive). Cloud occurrence exhibited pronounced seasonality: 57.7% in winter, 23.9% in spring, 17.8% in autumn, and virtually absent in summer (0.6%). Among five mutually exclusive cloud-layer classifications, pure ice clouds were the most frequent (38.4%), narrowly ahead of mixed-phase clouds (36.2%); combined, mixed-phase and mixed-phase precipitating layers together (41.0%) constituted the most frequent phase family overall. These results establish an 18-month observational baseline for evaluating climate model parameterizations, validating satellite-derived cloud products, and guiding future aerosol–cloud interaction studies in this climate-sensitive region. Full article

Thick-cloud contamination severely limits the usability of optical remote sensing imagery because cloud-covered regions may suffer from complete loss of surface information. Synthetic aperture radar (SAR) imagery provides complementary structural cues due to its cloud-penetrating capability, but the substantial cross-modal discrepancy between SAR and optical images makes high-fidelity SAR–optical fusion challenging. Existing methods usually either directly fuse heterogeneous SAR and optical features or use SAR-to-optical translation with insufficient spectral and structural constraints, which may lead to spectral distortion, structural artifacts, or degradation of cloud-free regions. To address these issues, we propose TFCRNet, a two-stage translation-and-fusion network for SAR–optical thick-cloud removal. In the translation stage, a Multi-Scale Feature Fusion Generator (MSFFG) transforms SAR imagery into optical-like images, while a Spectral Discriminator (SpeD) and a Structural Discriminator (StrD) separately constrain spectral fidelity and structural integrity. In the fusion stage, a Region-Gated Cross-Attention Fusion (RGCAF) module performs cloud-aware feature interaction between the translated optical image and the cloudy optical image. Using an externally supplied cloud mask, RGCAF emphasizes translated SAR-derived cues in cloud-covered regions while retaining reliable optical information in cloud-free regions. TFCRNet therefore requires a cloud mask during inference. Experiments on the SEN12MS-CR and SMILE-CR datasets show that TFCRNet achieves the best overall performance among the baseline methods reproduced under the unified experimental protocol adopted in this study. TFCRNet obtains 33.01/30.53 dB PSNR and 0.91/0.88 SSIM on SEN12MS-CR and SMILE-CR, respectively. These controlled results should be distinguished from literature-reported values obtained under different experimental settings, several of which are higher on selected metrics. Fine-grained ablations demonstrate that SpeD and StrD provide differentiated spectral and structural supervision, while RGCAF improves multimodal reconstruction through cloud-mask-guided regional information routing rather than spatially uniform feature fusion. Full article

Wetlands monitoring is essential for sustainable watershed biodiversity conservation, and climate resilience. This study performs a spatiotemporal wetland change analysis for the Sangamon River Watershed, Illinois between 2000 and 2025, using Landsat 5 Thematic Mapper (TM), Sentinel–2 Surface Reflectance (SR), Synthetic Aperture Radar (SAR), Gray–Level Co–occurrence Matrix (GLCM) and terrain data through cloud–based processing in Google Earth Engine (GEE), Google Colab and ArcGIS Pro 3.6.0. We conducted a comparative assessment of deep learning (Deep Neural Network [DNN], U-Net, Attention U-Net, and SegFormer), and machine learning models (Random Forest [RF], Gradient Tree Boosting [GTB], and Support Vector Machines [SVM]) through pixel–based and object–based methods. National Land Cover Database (NLCD) was used for training and validation using stratified random sampling for five categories namely wetlands, forest, agriculture/grassland/barren land, urban/developed and water. A proportion of 54.85% (860.68 km²) of wetlands extent was lost to other land uses, particularly agriculture, urban and forest, along with 46.51% (694.88 km²) forest and 36.80% (66.13 km²) water bodies loss. Agriculture/grassland/barren and urban/developed witnessed increases of 8.56% (820.62 km²) and 72.92% (799.52 km²), respectively. For Landsat–based and Sentinel–based classifications, SegFormer outperformed all ML and DL classifiers with (OA = 94%, Kappa = 0.89, mean F1 = 0.80, mean IoU = 0.70 and OA = 96%, Kappa = 0.92, mean F1 = 0.95, mean IoU = 0.73, respectively) with excellent wetland delineation (PA = 0.99, UA = 0.97, F1 = 0.98, IoU = 0.97 and PA = 0.99, UA = 0.99, F1 = 0.98, IoU = 0.99, respectively). Sentinel–based classifications had improved performance than Landsat, while object–based models consistently outperformed pixel–based methods. The Digital Elevation Model (DEM) and slope were the most influential predictors for RF models, while GLCM and SAR produced negligible influence. The integrated and comparative GeoAI framework provides a robust methodology for watershed–scale wetland monitoring and supports evidence–based conservation, restoration prioritization, climate resilience, and sustainable land–use planning, while offering strong potential for application in other agricultural watersheds following regional validation. Full article

This study utilizes the 89 GHz dual-polarization channel of the Ground-Based Multi-Frequency and Dual-Polarization Microwave Radiometer (GMD-MR) to overcome the challenges posed by the insensitivity of low-frequency microwave channels to cloud ice particles. By integrating data from Micro-Rain Radars (MRRs), we developed and implemented advanced convolutional and deep learning models. These models leverage brightness temperature, polarization differences, and constraints from cloud and precipitation data to quantitatively estimate cloud ice content, cloud water content profiles, rainwater content profiles, and precipitation rates, achieving correlation coefficients of 0.6, 0.7, 0.84, and 0.84, respectively. Our analysis of the spatiotemporal dynamics of ice water, cloud liquid water, and rain liquid water paths during precipitation events highlights their predictive value for precipitation occurrence. With a prediction accuracy of 97% and a temporal correlation coefficient of 0.9, our findings affirm the effectiveness of ground-based radiometers and micro-rain radars in precipitation detection. This study demonstrates the capability of multi-instrument joint retrieval for various meteorological parameters, highlighting the significant potential of multi-source microwave data fusion in quantitative precipitation estimation. It establishes and reinforces the foundation for future investigations into the physical processes of precipitation evolution. Full article

In recent years, the fusion of millimeter-wave radar and vision has emerged as a prominent research hotspot and a mainstream solution for autonomous driving perception. This integration spans multiple hierarchical levels, and the evolution of each level is not an isolated technological advancement, but rather a synergistic outcome driven by technological maturity, computational constraints, and mass-production requirements. Despite the inherent information loss associated with decision-level fusion, it remains the predominant engineering approach in the industry due to its superior functional safety and cost-effectiveness. Conversely, feature-level fusion has developed rapidly, propelled by a positive feedback loop of deep learning, bird’s-eye view (BEV) representations, and cross-modal attention mechanisms, moving beyond exclusive reliance on the Transformer architecture. Meanwhile, data-level fusion directly integrates raw radar point clouds and image pixels, a strategy that theoretically minimizes information loss. However, its large-scale deployment in practical engineering applications is hindered by critical bottlenecks, including poor interpretability, vulnerability to cross-sensor fault propagation, and severe challenges in safety isolation. From an engineering perspective, this paper systematically analyzes the evolutionary trajectory of millimeter-wave radar and vision fusion technologies, clarifying the parallel coexistence and adaptive deployment of these three fusion levels in practical autonomous driving scenarios. Full article

Avalanches pose serious threats to critical infrastructure in alpine-cold regions. Synthetic aperture radar (SAR) provides all-day, all-weather observations for avalanche mapping under persistent cloud cover. However, existing SAR change detection methods report accuracies from 0.49 to 0.87 and remain less effective for small avalanches. Their strong dependence on region-specific parameters also limits rapid and transferable avalanche mapping. To address these limitations, this study proposes a Siamese multiscale feature fusion network (SiamNet) for avalanche change detection from Sentinel-1A SAR images. SiamNet employs a shared-weight Siamese encoder to extract multiscale features from the pre- and post-event images and enhance avalanche-related feature responses. Experiments were conducted in the perennial snow-covered area of the typical alpine-cold area in the south-east Tibetan Plateau. A total of 33 avalanches were identified, with an overall accuracy of 98.76%. Field validation showed high agreement between the mapped areas and the actual avalanche tracks and deposits. Compared with representative change detection methods, including FC-Siam-Diff, SNUNet-CD, BIT, ChangeFormer, HANet, and EfficientCD, SiamNet effectively suppresses false detections while reducing missed detections and maintains superior performance across different scenarios. The results demonstrate that SiamNet can effectively map small avalanches with irregular boundaries and complex internal structures, providing technical support for rapid large-area avalanche surveys, spatial mapping, and risk assessment. Full article

Pasture quality monitoring is essential for optimizing grazing management and reducing the incidence of nutritional and metabolic disorders in ruminants, yet conventional field-based measurements remain labor-intensive and limited in spatial coverage. This review examines how remote sensing (RS) technologies can support pasture-based livestock systems by providing timely, scalable assessments of biomass, botanical composition, and nutritive attributes. Data from multispectral, hyperspectral, radio detection and ranging (RADAR), and light detection and ranging (LiDAR) sensors, acquired via satellite, unmanned aerial vehicle (UAV), and proximal platforms, are combined with machine learning (ML) methods and radiative transfer models to derive pasture biophysical and quality indicators. The reviewed evidence shows that RS reliably estimates pasture biomass and structural traits, while advances in spectral unmixing, data fusion, and artificial intelligence (AI) improve the characterization of heterogeneous swards and support emerging indicators related to forage quality. Integrating these remotely sensed metrics into grassland decision-support frameworks can enhance grazing allocation, inform fertilization and irrigation decisions, and help detect conditions associated with nutritional imbalances. Overall, the synthesis demonstrates that RS, particularly when combined with advanced modelling and cloud-based processing, offers a robust pathway for improving pasture monitoring and strengthening the nutritional management of ruminants, thereby supporting more sustainable and animal welfare-focused grazing systems. Full article

High-speed airborne platforms impose stringent aerodynamic constraints that restrict traditional planar antenna designs. Concurrently, multi-component echoes from extended targets and complex propagation environments induce strong signal coherence, severely degrading spatial angle estimation. To address these dual challenges, this paper proposes a straight-curved hybrid L-shaped asymmetric conformal array hardware topology tailored for cylindrical radomes. Building upon this, a millimeter-wave radar 3D point cloud imaging framework is developed for coherent targets. An orthogonal virtual manifold transformation is first devised to effectively compensate for the non-linear phase distortion induced by the conformal topology. Subsequently, a cascaded Forward-Backward Spatial Smoothing (FBSS) and Root-MUSIC framework is used for efficient signal decoherence. To resolve angle mismatches, a global cost function based on the cross-covariance Frobenius norm is formulated, which pairs independent angles and significantly suppresses spatial ghost targets. Systematic evaluations using 3D computer vision metrics demonstrate that the proposed method achieves accurate geometric restoration of aircraft targets with coherent signals. In the representative simulation, the method obtains a median point-wise localization error of 0.3986 m, a Chamfer Distance (CD) of 0.9695 m², an Earth Mover’s Distance (EMD) of 1.9504 m, and a spatial angular resolution of 1.0° under the stated test conditions. Under the stated simulation assumptions, boundary analyses indicate stable reconstruction around a post-pulse-compression SNR of −8.0 dB and a conformal curvature of 12.50 m⁻¹ ($r=0.08$ m), providing simulation-based design references for conformal radar 3D imaging. Full article

To address the issues of large temperature fluctuations, poor spatial perception, and low control robustness in traditional residential air conditioners, this paper proposes an adaptive temperature control algorithm based on millimeter-wave radar and a Light Gradient Boosting Machine (LGBM). Given that the bed is the primary obstacle and heat source in a bedroom, we develop a bed localization method using point cloud clustering. This method accurately identifies the bed position through time-window filtering, outlier removal, and density clustering. An LGBM weak teacher model, trained on massive cloud data, takes the bed position, indoor temperature, and compressor parameters as inputs to optimize air direction and fan speed, thereby effectively suppressing steady-state fluctuations in the return air temperature. Experiments on 719 real-world devices demonstrate that the bed positioning localization consistency rate reaches 83.6% under an error tolerance of 0.5 m, the average absolute temperature fluctuation is reduced to 0.21 °C, and the control accuracy of the air guide mechanism exceeds 0.98. The proposed method requires no hardware modification, offers strong generalizability and low deployment cost, significantly improves temperature stability and thermal comfort in bedroom environments, and provides a feasible technical solution for intelligent residential air conditioning control. Full article

In this study, a method capable of simultaneously extrapolating the position, shape, and intensity of weather radar echoes is proposed. As the method is an improved version of the previously proposed nonlinear grid transformation (NGT) method, it is referred to as the enhanced NGT (ENGT) method. By extending the nonlinear transformation matrix to include radar reflectivity as the third dimension in addition to the grid coordinates X and Y, a 3 × 9 transformation matrix is used to describe the continuous spatial variation in the radar reflectivity field. The transformation matrix is solved using historical near-term data, enabling the extrapolation of subsequent time steps. In a set of ideal extrapolation experiments combining translation, temporal increments, and path variations, the ENGT method demonstrated better qualitative and conceptual performance than the NGT and traditional optical flow (OF) methods. In a real squall line case, the ENGT method could predict the overall movement direction of the cloud system synthesized by moving and emerging cells. In a real enhanced convective cloud cluster case, the ENGT method achieved higher scores because it generated stronger reflectivity. Although there are still mathematically unsolved and statistically insignificant problems, the ENGT method shows potential in predicting strong reflectivity, and the computational efficiency for a single weather radar is considerable. Full article

Weather radar calibration is essential for ensuring data consistency and quantitative precipitation estimation in X-band radar networks. Existing absolute calibration methods (e.g., metal sphere, horn antenna) suffer from high cost, poor timeliness, and difficulty in automation due to meteorological conditions and airspace restrictions, while spatiotemporal matching methods based on volume scan data suffer from interpolation and matching inaccuracies. To address these issues, this study proposes a collaborative calibration method for X-band radar networks based on opposing Range–Height Indicator (RHI) scans. The method uses a rigorously calibrated reference radar as a benchmark and performs opposing RHI scans with the radar under calibration to obtain synchronized observations within the spatial overlap region. Precise spatial matching is achieved using the nearest-neighbor algorithm based on beam-broadening cross-coverage thresholds, and bias is extracted using both the midline 9-point averaging method (midline method) and spatially constrained regional Statistics method (regional method). Based on a total of 58 sets of opposing RHI scanning cases conducted under stratiform precipitation, scattered precipitation, and weak cloud conditions, the results show that under conditions where echo continuity is maintained near the midline of stratiform and scattered precipitation, both the midline method and the regional method can obtain stable matching data. The midline method achieves a median correlation coefficient (0.821–0.942) higher than that of the regional method (0.860–0.872), and its bias standard deviation remains relatively stable (midline method: 1.39–2.20 dB; regional method: 2.61–3.16 dB). Continuous RHI calibration tests confirm that within a 30-min window, the fluctuation of the data matching correlation coefficient is less than 0.05, and the fluctuation of the bias mean is controlled within ±0.3 dB. Under weak cloud conditions, although the midline method can still achieve a high correlation coefficient, the correctness of its results still requires auxiliary validation through other calibration means. This study provides a relatively efficient and effective technical approach for the automated collaborative calibration of dense X-band radar networks. Full article