Search Results (544)

Cloud detection is a fundamental task in atmospheric science and satellite remote sensing. While numerous algorithms utilizing multiple visible and infrared channels have been developed, the absence of visible light at night forces most current methods to rely on multi-channel thermal infrared (TIR) observations. Consequently, detection accuracy is significantly reduced due to the minimal thermal contrast between low clouds and the ground. Furthermore, distinguishing clouds under strictly moonless conditions remains a critical challenge. Leveraging the low-light observation capability of the Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS/DNB), this study proposes a single-channel cloud detection algorithm. Based on the physical scattering of ground-based artificial lights by clouds, the algorithm integrates a feature-engineering layer with a Random Forest machine learning model. This moonlight-independent approach can rapidly determine cloudy conditions, offering a novel method for high-precision nighttime cloud detection. Validation experiments using a single fixed radar site in Longmen, China, with 97 rigorously synchronized satellite-radar sample pairs, demonstrate that the proposed algorithm achieves an overall accuracy of 86.6% (95% CI: 78.4–92.0%) against millimeter-wave cloud radar observations. While strictly reliant on stable artificial ground lights—making it primarily applicable to urban and artificially lit regions—this method provides a valuable supplementary tool for nighttime monitoring. Full article

The solar corona has an extremely low density, and its brightness is only about one millionth of that of the photosphere. High-dynamic-range imaging of its faint structure is therefore essential for studying coronal heating, coronal mass ejections, and space weather. Quantitative coronagraph imaging requires flat-field measurement and calibration, which underpin intensity calibration, small-scale feature detection, and long-term cyclic analysis. This paper analyzes the coronagraph imaging chain (baffle–optical system–detector) and the origins of flat-field errors, including optical aberrations, stray light, and pixel-response non-uniformity, and summarizes the resulting calibration requirements of next-generation coronagraphs. On this basis, ground-based and space-based flat-fielding methods are systematically reviewed: the ground-based methods include integrating-sphere uniform light sources, opal glass/diffuser plates, clear-sky and thin-cloud backgrounds, and solar disk scanning, while the space-based methods include internal light sources and diffuser plates, attitude-roll and off-corona offset observations, and multi-phase statistical self-consistent flat-fielding. Their accuracy, resource cost, and applicability are compared. The review shows that no single method is simultaneously high-precision, easy to update, and engineer-friendly; a hierarchical, multi-method calibration framework is therefore recommended. Finally, a new method is proposed in which lithographically generated structured light fields, combined with Fourier optics and machine learning inversion, are used to estimate the pixel-response function. Preliminary experiments show that this method achieves a lower residual error than the integrating-sphere and opal glass methods, providing a high-precision reference for future wide-band, high-resolution coronagraph calibration. Full article

Reliable pavement condition assessment using UAV-derived orthophotos remains challenging under manual flight conditions, where acquisition parameters are not predefined and photogrammetric quality is highly operator-dependent. This study evaluates how UAV flight configuration influences orthophoto quality and operational usability for road infrastructure assessment in real-world manual survey scenarios. Eight flight treatments combining altitude (30–40 m AGL), flight speed (low/normal), and image capture interval (2–3 s) were tested over an urban–peri-urban road segment in Misantla, Veracruz, Mexico, using a DJI Air 3S platform. Orthomosaic quality was assessed through ground sampling distance (GSD), tie-point density, multiplicity, RMS reprojection error, dense cloud size, orthomosaic continuity, and a criteria-based interpretability index supported by field observations. Results show that while altitude controls spatial resolution, resolution alone is insufficient for reliable pavement assessment. Configurations with higher image overlap and photogrammetric redundancy (notably Treatment 1 (T1) and Treatment 3 (T3)) achieved superior geometric consistency, reduced seam artifacts, and improved detection of subtle surface irregularities. In contrast, reduced-overlap configurations produced complete but less interpretable orthomosaics. The study provides experimentally validated operational guidelines for optimizing UAV flight parameters under manual conditions, bridging the gap between controlled photogrammetric theory and practical infrastructure monitoring. Full article

Free-space optical (FSO) downlinks from satellites enable high data rates but are highly sensitive to cloud-induced attenuation and blockage. We present an integrated all-sky imaging framework for optical ground stations that converts station-local sky observations into direction- and lead-time-dependent cloud-free line-of-sight (CFLOS) decision support along predicted satellite links. The framework combines geometric calibration of hemispheric imagery, two-line element (TLE)-based orbit propagation, stereographic remapping into a common processing domain, short-horizon autoregressive sky-frame prediction using a diffusion-based sequence model, cloud/no-cloud segmentation, and a corridor-based CFLOS decision rule along the projected satellite path. The contribution lies in the operational integration of these components into a unified CFLOS-oriented sensing, prediction, and evaluation chain for optical downlink support. The framework is demonstrated at the German Aerospace Center (DLR) optical ground-station site in Trauen and evaluated using a geometry-controlled reference-track protocol across image sequences acquired at 15 s, 30 s, and 45 s cadence. Under this protocol, nowcasting-based CFLOS decisions outperformed a constant-persistence baseline across all evaluated lead times. At a 90 s lead time, the method achieved an F1-score of 0.857 and a balanced accuracy of 0.865, corresponding to gains of +0.083 and +0.089 over persistence, respectively. Positive performance margins are maintained across the full evaluated range up to a 450 s lead time. These results show that all-sky image sequences can be translated into physically interpretable CFLOS decision support and provide a basis for future network-level site-selection and handover strategies. Full article

In this paper, we estimate and refine 3D human pose using the 3D point cloud or mesh model reconstructed from RGB-D cameras or volumetric capture systems. We first reconstruct the 3D model using the multi-view cameras to estimate a highly accurate skeleton. To obtain a 2D skeleton with low error, the reconstructed 3D model is projected to four virtual planes after decidi ng the direction of the 3D model. Four 2D skeletons are estimated from four images projected in the virtual plane. Afterward, the refinement process selects candidate joints based on the distribution of local vertices and the DBSCAN algorithm. It applies a sphere fitting to ensure that the final joints are located within the body volume. The joints are combined at the intersection through the back-projection of the joints, including those in the 2D skeleton on the virtual plane. The joints in the intersection are refined using the spatial distribution of the 3D information. Through the proposed method, we estimated a stable and geometrically consistent 3D human pose from reconstructed volumetric data. Using models with ground truth, we calculated the MPJPE between the skeletons of the proposed and the ground truth. The 3D pose estimation was evaluated through a visual assessment of the captured image, and the results were quantitatively compared with the 3D joint positions acquired by the motion capture device. Full article

Stereo radargrammetry using Synthetic Aperture Radar (SAR) images is a powerful technique for all-weather 3D topographic measurements. However, conventional methods based on local template matching often struggle to establish accurate correspondences in mountainous or vegetated areas due to severe SAR-specific geometric modulations. In this paper, we propose a novel high-accuracy stereo radargrammetry framework by introducing RoMa, a robust Transformer-based deep learning model, for dense SAR image matching. Optical pre-trained deep learning models often suffer from a domain gap. To overcome this limitation, we develop an automated pipeline to construct a patch-based SAR image dataset using a reference Digital Surface Model (DSM) and an SAR projection model. By fine-tuning RoMa on this dataset, the model effectively adapts to the complex non-linear deformations of SAR images. Furthermore, unlike conventional methods, our approach establishes correspondences directly on the original slant-range images without requiring ground-range projection, thereby avoiding image quality degradation caused by pixel interpolation. Experimental results using airborne Pi-SAR2 images demonstrate that the fine-tuned RoMa significantly outperforms conventional methods, achieving an 82.86% matching accuracy at a 10-pixel threshold. In the 3D measurement evaluation, the proposed method achieves the lowest elevation mean error ($-1.24$ m) and the highest inlier ratio (74.1%), proving its effectiveness in generating accurate, dense, and wide-area 3D point clouds even in challenging terrains. Full article

Barely Visible Impact Damage (BVID) in heterogeneous polymer-matrix composites remains difficult to detect because subtle damage signatures are often masked by complex architectures, hybrid textures, and overlapping failure morphologies. This study therefore presents an experimentally grounded, physics-aware, and statistically validated vision-based inspection framework rather than a purely detector-centered benchmarking exercise. Real post-impact images were obtained from controlled low-velocity impact experiments on 20 composite architectures and 60 physical specimens, yielding approximately 2000 images across laminated, hybrid, textile-reinforced, and sandwich structures. The dataset was organized using a specimen-disjoint splitting protocol to prevent leakage across training, validation, and test subsets. To improve robustness while preserving physical realism, a physically grounded Albumentations strategy was developed using only physically admissible transformations and explicit exclusion of non-physical operations that could distort damage morphology or surface continuity. Model development was further complemented by a hybrid hardware workflow in which cloud-based GPU training was combined with deployment-oriented inference profiling on resource-constrained edge-like hardware, thereby linking detection accuracy to practical industrial feasibility. In addition, model performance was evaluated under a standardized training budget and validated through repeated runs, Friedman significance testing, and Holm-corrected Wilcoxon signed-rank pairwise comparisons to ensure error-controlled interpretation of inter-model differences. Across the evaluated compact YOLO families, YOLO26s delivered the strongest overall performance, reaching 0.841 mAP@0.5, 0.586 ± 0.004 mAP@0.5:0.95, and an F1-score of 0.809, while YOLO11s achieved the highest precision and YOLO26n remained competitive in recall with nano-level compactness. Overall, the results show that experimentally generated heterogeneous composite data, morphology-preserving augmentation strategy development, leakage-aware dataset design, deployment-oriented computational profiling, and statistically grounded validation together provide a more robust and application-relevant basis for automated BVID detection in polymer-matrix composite structures. Full article

High-precision plant phenotyping requires efficient 3D reconstruction methods with high geometric quality. 3D Gaussian Splatting (3DGS) has recently emerged as a promising approach for real-time 3D reconstruction, achieving impressive visual quality. However, in crop environments dominated by monochromatic and low-texture regions, existing 3DGS methods often produce ambiguous geometries and fail to recover geometry-consistent 3D surfaces. To address these limitations, we propose LV-3DGS (Leafy Vegetables-3DGS), an optimized 3DGS-based framework tailored for the reconstruction of leafy vegetable scenes. First, a blurred reconstruction module is introduced to mitigate reconstruction artifacts caused by camera motion blur during multi-view image acquisition. Second, we propose a planar optimization strategy and design both local and global geometric consistency regularizations to optimize the model, thereby improving the surface reconstruction quality and geometric accuracy. Third, based on an analysis of individual Gaussian contributions, a contribution-based pruning strategy is developed to selectively remove inaccurate geometric components, achieving accurate scene geometry while reducing memory consumption and improving rendering efficiency. In addition, a quantitative geometric evaluation method is proposed for assessing reconstruction quality. Experimental results demonstrate that the proposed method achieves the highest accuracy among the tested baselines, with SSIM, PSNR, and LPIPS reaching 0.94, 34.53 dB, and 0.11, respectively. Moreover, the geometric consistency (GC) metric attains 0.317 cm. Finally, phenotypic parameters are measured from the reconstructed leafy vegetable point clouds. Compared with ground truth measurements, the proposed approach yields coefficients of determination ($R^2$) of 0.9959, 0.9651, and 0.9895 for plant height, leaf number, and leaf area, respectively. These results are significantly outperform to some existing phenotyping methods, providing a new methodology and technical solution for high-precision, low-cost, and high-throughput crop phenotyping. Full article

This paper proposes an intelligent stress detection method based on convolutional neural networks and the DeepFace framework, addressing the challenges of increasingly prominent global mental health issues and the limitations of traditional psychological services in terms of early warning latency and coverage. A three-level cascaded strategy combining RetinaFace, MTCNN, and OpenCV is first employed for face detection and localization, and facial expression features are extracted via the DeepFace framework. By integrating Russell’s valence–arousal model with Lazarus’s cognitive appraisal theory, an emotion–stress mapping rule is constructed to convert seven-category emotion probability distributions into 1–5 scale stress values. The method employs a cloud–edge collaborative flow, with feature extraction performed at the edge and original images promptly destroyed to mitigate privacy risks. Experiments on public expression datasets indicate that the method achieves above 99% face detection accuracy, 84.99% emotion recognition accuracy, and 86.09% stress assessment consistency grounded in the emotion–stress mapping rule, with an average response time per frame of approximately 200 ms. Based on 233 multi-scenario surveys, some respondents show limited stress self-awareness, suggesting traditional self-reporting may have blind spots, and thus this method serves as a useful supplement. Full article

Backpack mobile mapping systems (MMS) equipped with LiDAR and RGB cameras, as well as an optional GNSS/INS direct georeferencing unit, are increasingly utilized in forest inventory applications. In general, LiDAR point clouds provide detailed structural information, whereas imagery offers visual specifics of surface features. However, cameras typically operate at lower acquisition rates compared to LiDAR. In proximal mapping, another challenge is the inconsistent reception of GNSS signals beneath forest canopies. Additionally, georeferencing accuracy may differ between LiDAR and imagery due to biases in the system calibration parameters and variations in post-processing approaches. To address these challenges, this study introduces a Backpack MMS that uses cameras configured at elevated frame rates to enhance image overlap. Concurrently, this study presents an algorithmic approach to addressing georeferencing issues by integrating imagery and LiDAR data, thereby enhancing system calibration and improving platform trajectory. The method is based on the hypothesis that forest environments are rich with geometrically well-defined features, such as tree trunks and ground patches. By identifying conjugate primitives in point clouds from both imagery and LiDAR, the procedure optimizes feature models while simultaneously minimizing calibration biases and/or trajectory errors. The proposed approach is validated using multiple field datasets collected in diverse forest environments. Quantitative results show that the procedure reduces image–LiDAR feature misalignment across all datasets from up to 1.1 m in the planimetric direction and 2 m in the vertical direction to within 5 cm in both. The feature fitting accuracy also improves from 2.9 cm to 0.85 cm for LiDAR point clouds and from 10 cm to 0.9 cm for image-based point clouds. However, the results indicate that despite increased data availability, imagery alone remains less reliable than LiDAR for extracting structural information. Nevertheless, the proposed image–LiDAR alignment strategy represents a crucial step toward developing a comprehensive tree inventory. Full article

Effective monitoring of surface damage in mining areas is vital for ecological restoration. Unmanned aerial vehicles (UAVs) have been widely used to obtain ground subsidence data owing to their low cost and ease of operation. The images captured by UAVs can generate dense image matching (DIM) point clouds, which, after screening, can be used to create a digital elevation model (DEM) required for deformation analysis. Existing filtering algorithms mainly rely on the spatial geometric features of point clouds and rarely utilize color information, which limits their accuracy in areas with vegetation coverage. To address this issue, this study proposes a H-PTD method that combines visible light with progressive triangulated irregular network densification (PTD). First, initial ground seeds are selected based on the H value in the HSV space. Subsequently, a triangulated irregular network (TIN) is constructed, and iterative densification is performed by evaluating the relationship between the target point and adjacent triangular faces, thereby achieving an accurate distinction between ground and non-ground. Evaluated on three terrain datasets and against five classical methods, the results indicate that the Total error in the H-PTD cross-matrix is controlled between 2.9% and 7.8%, and remains below 8% overall. The standard deviation of the DEM difference is around 0.02 m. Compared to other methods, H-PTD shows higher filtering accuracy and better terrain adaptability, making it more promising for monitoring mining areas and providing a more reliable tool for subsidence detection based on UAVs. Full article

There are numerous benefits associated with creating digital copies of anatomical structures, which can be used during patient diagnosis. Such models can be used not only for visualization, but also in order to assess the condition of the patient. As advances in both medical imaging and 3D graphics are made, it is necessary to determine areas of application of the known reconstruction algorithms. Specifically, it is crucial to find advantages and disadvantages of known approaches to mesh generation, depending on the properties of the object and compare the quality of their results. In order to provide reliable ground-truth data, three 3D models with features resembling those identified in anatomical structures have been created. Based on these meshes, sets of CT-like DICOM images have been generated. Five different reconstruction approaches were proposed: using 3D occupancy information directly, two ways of obtaining point clouds and two methods that utilize Signed Distance Field. A neural network architecture for the SDF upsampling has also been presented. The obtained results justify the popularity of the Marching Cubes algorithm, as it produced accurate reconstructions most reliably. However, for certain scenarios, promising alternatives have been found. The presented outcomes make it clear that the approach to reconstruction must be tailored to the specific problem. Full article

This study presents a comparative evaluation of three modern surveying techniques—UAV photogrammetry, static tripod-based LiDAR scanning, and handheld mobile LiDAR—applied in the context of historic monument restoration. The focus is on analysing workflow efficiency, data accuracy, and adaptability to complex architectural features, including interior wall paintings, which are integral to the monument’s heritage value. Particular attention is given to how each technique captures surface texture, color fidelity, and material deterioration. The study also examines performance around intricate architectural elements such as vaulted ceilings, apses, cornices, columns, and carved stone portals, where occlusions, tight clearances, and fine ornamentation challenge coverage and resolution. By evaluating the strengths and limitations of each approach, the research highlights methodological considerations relevant for conservation professionals. The results indicate that the Static TLS is the most demanding workflow, requiring complex total station integration for control and station points. It produced the highest data density, with acquisition rates of one million points per second, making it the most hardware-intensive and difficult to manipulate. UAV photogrammetry provided a balanced middle-ground; it required minimal physical effort during acquisition and produced datasets that were significantly easier to manage. Handheld SLAM LiDAR emerged as the most productive solution for rapid coverage. While the handheld scanner’s image quality was lower than the photogrammetry, it still provided enough detail for the structural assessment and documentation needed. Although the point cloud lacked the extreme geometric detail provided by the TLS, the FARO Connect software made georeferencing and data manipulation significantly more efficient. Full article

Aerosol optical depth (AOD) is a key indicator for quantifying aerosol radiative effects and evaluating air quality. However, atmospheric chemical transport models often exhibit systematic AOD biases, and model capability for column-integrated optical properties is not always consistent with that for near-surface particulate matter concentrations. Here, we evaluate AOD simulated by the Community Multiscale Air Quality (CMAQ) model over five major urban clusters in China, including the Beijing-Tianjin-Hebei (BTH) region, Fenwei Plain (FWP), Sichuan Basin (SCB), Yangtze River Delta (YRD), and Pearl River Delta (PRD), using satellite retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS), ground-based retrievals from the Aerosol Robotic Network (AERONET), and vertical extinction profiles from the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO). CMAQ reproduces the major spatial patterns and exhibits relatively small biases in near-surface PM_2.5. However, it persistently underestimates AOD relative to MODIS, with the largest negative bias occurring in April (i.e., a typical spring month). This contrast indicates a pronounced inconsistency between column-integrated aerosol amount and surface mass density. Relative to AERONET, CMAQ shows a negative bias (NMB = −38%), whereas MODIS shows a positive bias (NMB = 56%), suggesting that both model and retrieval uncertainties contribute to the CMAQ–MODIS disagreements. CALIPSO-constrained vertical analysis further suggests that insufficient extinction above the planetary boundary layer (PBL) is an important contributor to the negative AOD bias, although the relative roles of boundary-layer and upper-layer contributions vary across regions, underscoring the importance of accurately representing aerosol vertical transport and optical processes. These results indicate that evaluations based solely on surface observations may fail to fully capture the overall structure of AOD errors, particularly given the clear differences between near-surface mass concentrations and column optical properties, which vary across regions. This also highlights the importance of improving the representation of aerosol vertical transport and optical processes in chemical transport models. Full article

Soil health monitoring is critical for the sustainable management of the black soil region, a key resource for global food security. However, traditional field surveys are constrained by high operational costs, limited spatial coverage, and low temporal frequency, making them inadequate for high-resolution and time-sensitive soil monitoring. The recently launched ZY1-02E satellite, equipped with an advanced hyperspectral imager, offers a new potential data source, yet its capability for quantitative soil modelling requires rigorous cross-sensor validation. This study conducts a cross-sensor evaluation of ZY1-02E and its predecessor, ZY1-02D, for mapping soil organic matter (SOM) and soil texture (sand, silt, and clay) in Northeast China. Optimal spectral indices were constructed through exhaustive band combination and correlation screening, and quantitative inversion models were established using a hybrid framework integrating Random Frog feature selection with Gaussian Process Regression (GPR) and Boosting Trees, based on synchronous ground observations. Results demonstrate strong cross-sensor consistency, with spectral indices showing significant linear correlations (${\mathrm{R}}^2>0.65$) between ZY1-02E and ZY1-02D. Furthermore, the quantitative retrieval models applied to ZY1-02E imagery achieved robust performance, with cross-sensor retrieval consistency exceeding ${\mathrm{R}}^2=0.60$ for all parameters and SOM exhibiting the highest agreement (${\mathrm{R}}^2=0.74$). These findings confirm the radiometric stability and algorithm transferability of ZY1-02E, demonstrating its capability to generate soil parameter products comparable to ZY1-02D without extensive model recalibration. The validated interoperability of the twin-satellite constellation substantially enhances temporal observation capacity during the narrow bare-soil window, effectively mitigating cloud-induced data gaps in high-latitude agricultural regions. Importantly, the enhanced monitoring framework provides a scalable technical paradigm for high-frequency hyperspectral soil mapping, offering critical spatial decision support for precision fertilization, soil degradation mitigation, and conservation tillage management in the Mollisol belt. Full article