Search Results (361)

Snapshot compressive multi-view spectral imaging (SC-MVSI) multiplexes view-spectral information into a single coded measurement, enabling compact acquisition with a two-dimensional detector. Because each reconstructed channel corresponds to both a selected spectral response and a view direction, direct cross-channel modeling at identical pixel coordinates can introduce structural mismatch caused by view-dependent displacement. This paper proposes a reference-guided view-aligned nonlocal low-rank tensor reconstruction method for SC-MVSI. The reconstruction is formulated as a coded inverse problem and solved using the alternating direction method of multipliers (ADMM) in a variable-splitting framework. In the prior update, a reference tensor guides block-level patch alignment before nonlocal tensor grouping, and the resulting fourth-order tensor groups are regularized by canonical polyadic (CP) low-rank approximation. Experiments on eight synthesized multispectral light-field scenes show that the proposed method achieves the highest average PSNR of 33.61 dB and the lowest average CAE of 5.69 degrees among the compared baselines, while obtaining the second-highest average SSIM of 0.8823. Real-system experiments further provide a qualitative demonstration of applying the proposed reconstruction framework to captured coded measurements. Full article

Timely and reliable mapping of landslide-affected areas from high-spatial-resolution optical imagery is essential for disaster investigation and post-event assessment. However, this task remains challenging because landslides usually exhibit large-scale variations, irregular boundaries, and strong spectral–textural similarities with surrounding bare-surface objects, which often cause missed detections, false positives, incomplete delineation, and inaccurate boundary localization. To address these problems, this paper presents a Scale-View Interactive Attention Network, named SIA-Net, for RGB-based landslide segmentation. First, a Multi-Scale Attention Module (MSAM) is constructed to encourage information exchange among features with different spatial resolutions. By doing so, the network can better represent both small scattered landslide patches and large continuous landslide bodies. Second, a Multi-View Attention Module (MVAM) is introduced to aggregate contextual cues from multiple receptive field views. This design strengthens the model’s ability to distinguish landslides from visually confusing objects, including bare soil, roads, riverbanks, and terrain shadows. In addition, a Convolutional Block Attention Module (CBAM) is incorporated during feature reconstruction to enhance landslide-related channel and spatial responses, thereby improving segmentation completeness and boundary localization. Experiments on the CAS Landslide Dataset (CLD) and GVLM Dataset show that SIA-Net provides more accurate landslide masks than the compared segmentation networks under the adopted benchmark settings. These results indicate that integrating scale-level interaction, view-level contextual modeling, and attention-guided decoding can effectively improve landslide extraction in complex optical remote sensing scenes. Full article

Submarine sand waves are widespread on shallow continental shelves. Their complex morphology and potential mobility create challenges for engineering surveys, navigation safety, and seabed stability assessment. Multibeam surveys provide accurate bathymetry but are costly and spatially limited, whereas satellite-based methods offer broader coverage but remain challenging in complex sand wave fields. Here, we propose a spatial-sequential 2DCNN–LSTM model for retrieving submarine sand wave bathymetry from Sentinel-2 surface reflectance imagery. The model represents each target point as a sequence of local multispectral image patches, allowing convolutional layers to extract two-dimensional textural features and LSTM layers to learn profile-scale rhythmic continuity associated with sand wave morphology. The model was trained using multibeam bathymetry and applied to a large extrapolation area of approximately 4000 km² on the Taiwan Banks. Evaluation on the large extrapolated area against in situ bathymetric data achieved a root mean square error (RMSE) of 3.78 m, a mean absolute error (MAE) of 2.99 m, and a mean relative error (MRE) of 9.1%. The results demonstrate that sand wave-induced optical textures can provide useful information for broad-scale bathymetric reconstruction, although model performance remains dependent on image texture visibility controlled by hydrodynamic, illumination, and atmospheric conditions. This framework offers a cost-effective approach for satellite-based monitoring of large submarine sand wave fields, providing a new perspective for engineering applications. Full article

High-precision 3D reconstruction of on-orbit non-cooperative targets is essential for space situational awareness. However, extreme space environments induce severe imaging degradations, including high-dynamic-range (HDR) illumination, rapid motion blur, and platform jitter. Traditional 3D Gaussian Splatting (3DGS) conflates these optical distortions with geometric optimization, leading to pathological structural inflation and the loss of thin appendages like solar panels. To overcome this, we propose OrbitGS, a physically decoupled 3DGS framework. OrbitGS integrates physical imaging priors via a Kinematics-Driven Degradation Synthesizer (KDDS) to deterministically extract view-specific degradation kernels. Furthermore, a blur-decoupled rendering strategy with intensity-aware weighting mitigates HDR variations, while a semantic-aware densification scheme mathematically penalizes abnormal primitive expansion. Evaluations on the SPE3R dataset demonstrate that OrbitGS effectively disentangles optical degradations from the geometric representation. Quantitatively, evaluated across seven space targets under moderate (200-view) and extreme sparse (50-view) settings, our framework achieves state-of-the-art robustness against extreme degradations. Notably, it avoids the catastrophic structural blow-ups observed in baseline methods, yielding an average geometric F1-score of 0.80 and a Chamfer Distance of 0.818, alongside a rendering Structural Similarity Index (SSIM) of 0.82 and a Learned Perceptual Image Patch Similarity (LPIPS) of 0.16. By preserving delicate structures under severe degradation, OrbitGS provides a robust, high-fidelity 3D reconstruction solution for complex orbital environments. Full article

Background: Energy dissipation reflects the viscoelastic behavior of biological tissues and plays a key role in arterial elastic recoil and diastolic flow support. In the native aorta, efficient storage and release of mechanical energy are essential for maintaining ventriculo–aortic coupling. The energy dissipation ratio (EDR) quantifies the proportion of mechanical energy lost during a loading–unloading cycle and may provide insight into the biomechanical performance of aortic substitutes. Bovine pericardial patches (BPPs) are widely used in cardiovascular surgery for arterial reconstruction, patch angioplasty, and tubular replacement. Today, EDR has not been systematically investigated in BPPs. Methods: Forty glutaraldehyde-treated BPPs from four commercial manufacturers (n = 10 per supplier) were subjected to low-cycle fatigue testing using a uniaxial tensile system under controlled physiological conditions (37 °C). Standardized bone-shaped specimens were tested at progressive strain percentage levels. Thickness, EDR, and the percentage of specimens failing to reach progressively higher strain levels were evaluated from stress–strain hysteresis loops. Results: BPPs thickness ranged from 0.253 to 0.608 mm, with no significant differences among most groups. For the 10% strain, all BPPs reached the target deformation and demonstrated comparable EDR values. In detail, the mean EDR was 21.40 ± 7.02% for Edwards Lifesciences, 24.95 ± 6.80% for Supple Peri-Guard (Baxter), 24.99 ± 6.04% for Xenosure (LeMaitre), and 23.37 ± 6.12% for Invengenx–Tisgenx, with no statistically significant intergroup differences (p > 0.05). For the 20% strain, only 18.75% of specimens remained structurally intact, and variability increased. At 30% strain, structural failure occurred in nearly all samples. No significant orientation-dependent differences were observed. Conclusions: Commercially available BPPs exhibit similar biomechanical behavior under moderate deformation. However, tolerance to higher strain is limited. EDR analysis provides a clinically relevant parameter to assess elastic performance and may contribute to optimizing aortic substitute selection. Full article

Seismic trace interpolation reconstructs missing traces caused by incomplete spatial sampling while preserving reflection-event continuity, amplitude fidelity, and waveform character. Randomly missing traces introduce an inherent reliability asymmetry: observed positions contain measured amplitudes, whereas missing positions must be inferred from neighboring seismic events. In U-Net-based encoder–decoder architectures, shallow features extracted around zero-filled missing traces may carry sampling artifacts and unreliable high-frequency details through direct skip connections. To address this limitation, this paper proposes MGF-UNet, a mask-guided gated skip fusion network for 2D seismic interpolation with randomly missing traces. The incomplete seismic patch and binary trace mask are used as a dual-channel input, and mask-guided gates regulate encoder features before decoder fusion. Known-trace preservation is then applied to retain measured traces in the final output, while a hybrid objective supervises missing-trace recovery, full-patch fidelity, and structural coherence. In five-run experiments on Marmousi synthetic data, MGF-UNet achieves the lowest mean MissingRMSE at the 30% and 50% missing ratios and remains competitive in global RMSE, SSIM, and SNR; however, the seed-matched paired comparisons do not reach statistical significance. Field-data comparisons and local waveform analyses further show coherent event reconstruction and reduced residual artifacts. These results suggest that mask-guided skip regulation is a promising strategy for moderate-to-severe random missing-trace interpolation. Full article

To address the limited extraction of local fault features, ineffective fusion of multi-scale fault information and interference from redundant noise in vibration-based fault diagnosis of rolling bearings and gears under complex operating conditions, a multi-scale gated feature refinement Vision Transformer (MGFR-ViT), is proposed. First, one-dimensional vibration signals are reconstructed as two-dimensional vibration matrices, making them compatible with patch embedding and Vision Transformer-based feature modeling. A locally enhanced Vision Transformer module is then developed by incorporating a local enhancement mechanism into the standard Vision Transformer architecture, thereby improving the extraction of locally fault-sensitive features while preserving global dependency modeling. Furthermore, a multi-scale gated feature refinement module is introduced to adaptively enhance fault-relevant information and suppress redundant features and noise through parallel multi-scale convolutions with different receptive fields, channel interaction, and gated weighting. Finally, global average pooling and a fully connected classifier are employed for fault classification. Experiments conducted on bearing and gear datasets demonstrated that MGFR-ViT achieved superior diagnostic performance and feature separability compared with several representative fault diagnosis models. Ablation studies further validated the effectiveness and complementarity of the proposed modules. These results indicate that MGFR-ViT provides an effective feature-learning framework for vibration-based fault diagnosis of rotating machinery. Full article

Satellites are critical components of modern space information systems. During long-term on-orbit operation, satellite telemetry often exhibits multi-scale temporal dynamics, heterogeneous channel behavior, and time-varying inter-variable dependencies, which pose substantial challenges to anomaly detection. Existing methods remain limited in adaptively representing anomaly patterns across temporal scales, jointly modeling temporal evolution and dynamic asymmetric channel dependencies, and preventing over-generalized reconstruction of anomalous inputs. To address these limitations, this paper proposes MSGMamba, a multi-scale graph state space model for satellite telemetry anomaly detection. First, a multi-scale temporal patch decomposition and gated fusion mechanism partitions telemetry sequences into patches of different granularities and adaptively integrates their representations at each temporal position, enabling the joint modeling of short-term transients and relatively slow-varying patterns. Second, a graph–sequence alternating propagation mechanism couples selective state space updates with dynamic graph interaction. At each temporal patch, a directed and asymmetric dependency graph with self-connection priors is generated from the temporally encoded features, allowing temporal evolution and time-varying cross-channel dependencies to be modeled within a unified framework. Third, an orthogonal memory-augmented anomaly discrimination mechanism introduces an orthogonality-constrained memory bank to reduce redundancy among nominal prototypes and constrain the reconstruction space. A dual-pathway anomaly score further combines signal-space reconstruction error with encoder–memory discrepancy to improve the separability of nominal and anomalous samples. Experiments on the SMAP, MSL, and EIRSAT-1 datasets show that MSGMamba outperforms representative baseline methods in terms of average PA-F1 and AFF-F1. Full article

Microwave scatterometers are capable of acquiring land surface backscattering coefficients day and night under all-weather conditions, offering advantages for snow cover monitoring. However, the relatively low spatial resolution of traditional scatterometer data limits their application in the fine-scale monitoring of snow cover distribution. To improve the spatial representation of snow cover and mitigate mixed-pixel effects in complex spring snowmelt scenarios, this study proposes an adaptive bilateral filtering scatterometer image reconstruction (SIR-ABF) algorithm based on the rotating fan-beam scanning characteristics of the FengYun-3E Wind Radar (FY-3E WindRAD). The Ku-band data of FY-3E WindRAD were reconstructed from the original 10 km resolution to an enhanced resolution of 3.125 km. Furthermore, by integrating the reconstructed scatterometer backscatter with multi-source auxiliary data, an optimal feature subset was determined through a feature selection strategy that considers both feature-label correlation and inter-feature multicollinearity. Finally, the best feature subset was combined with four machine learning (ML) models for snow cover classification. The results indicate that the Support Vector Machine (SVM) achieved the best performance, yielding an Overall Accuracy (OA), Macro-F1, and Kappa coefficient (Kc) of 91.64%, 86.47%, and 0.730, respectively. Compared with the snow cover classification results derived from the original 10 km Ku-band data, the 3.125 km reconstructed data provided more detailed spatial information and better characterized fragmented snow patches and snow transition boundaries. Further comparison with existing snow cover products demonstrated the spatial consistency and continuity of the proposed classification results, highlighting the potential of high-resolution scatterometer data for fine-scale snow cover monitoring during the spring snowmelt period in Northeast China. Full article

Cotton fiber quality is shaped during boll development, boll opening, fluffing, and harvesting, but current assessment still relies largely on manual field inspection and postharvest laboratory testing. This limits timely harvest scheduling and plot-level quality management. To address this problem, we propose a self-supervised multimodal sensing framework for linking preharvest cotton boll status, environmental conditions, and postharvest fiber quality. First, the Cotton Boll Visual Phenotype Self-Supervised Encoding Module learns maturity-related visual representations by reconstructing masked image patches, so that boll cracking, lint exposure, and surface texture can be captured from unlabeled field images. Second, the Agricultural Sensor Temporal Masked Modeling Module reconstructs masked sensor observations to model temporal patterns in temperature, humidity, light, soil moisture, rainfall, and other environmental variables. Third, the Vision–Environment Cross-Modal Contrastive Fusion Module aligns image features with environmental features and produces a joint representation for downstream prediction. Field experiments were conducted using cotton boll images from different maturity and abnormal states, environmental sensor records, management information, and postharvest fiber quality measurements. The framework was evaluated for maturity classification, harvest-window recognition, and fiber quality prediction. The results showed that the proposed method performed consistently better than representative machine learning, single-modal deep learning, and multimodal fusion baselines, while few-shot and ablation experiments supported the value of self-supervised pretraining and multimodal fusion. These findings indicate that the proposed approach can provide useful information for preharvest cotton maturity assessment and harvest-quality management. Full article

Although unsupervised anomaly detection has shown promising performance in industrial visual inspection, many architectures still struggle with variable input resolutions, and anomaly scores are often difficult for end users to interpret. This study proposes a multi-stage hybrid workflow for pixel-level defect localization and structured reporting in bolt head images. The proposed SA-PatchCore framework customizes PatchCore by extracting multi-scale representations from a frozen deep feature extractor and supporting resolution-adaptive anomaly-map reconstruction through dynamic feature-map sizing. After anomaly detection, a Qwen3-VL-32B-based reporting module, adapted with GRPO, uses both the original image and the anomaly overlay as visual evidence. It generates structured JSON outputs containing defect presence, a 3 × 3 location label, and a concise textual description. On the industrial bolt dataset, SA-PatchCore achieved 98.69% pixel-level AUROC, 29.73% Pixel-AP, and 39.24% oracle Pixel-F1max. Compared with PatchCore, PaDiM, DRÆM, and CS-Flow, the method delivered strong results, especially in Pixel-AUROC. In the reporting stage, defect presence/absence accuracy improved from 76.63% to 96.41%, while defective-sample recall increased from 74.23% to 96.14% over the baseline Qwen3-VL-32B. Exact location match rose from 28.15% to 53.78%, and mean partial location score improved from 32.29% to 65.34%. Overall, the framework combines accurate anomaly localization with structured reporting, improving interpretability and usability. Full article

Background: Rapid, disproportionate expansion of post-infarction left ventricular (LV) aneurysms represents a high-risk remodeling phenotype characterized by progressive mechanical deterioration and severe geometric distortion. Methods: A 54-year-old male presented with late anterior myocardial infarction complicated by a partially thrombosed apical LV aneurysm with an initial left ventricular ejection fraction (LVEF) of 35%. Despite clinical stability under optimal guideline-directed medical therapy, serial multimodality imaging at 7 weeks revealed an aggressive, disproportionate expansion of the aneurysmal component to 120 mL, inducing severe ventricular geometric distortion and secondary functional degradation (LVEF 20%). Multimodality imaging demonstrated a favorable geometry of the functional ventricle with preserved contractile function. Results: The patient underwent prompt surgical ventricular restoration using a double-patch Dor technique, effectively excluding the large aneurysm and restoring physiological ventricular geometry. The postoperative course was uneventful. At 6-month follow-up, cardiovascular magnetic resonance confirmed sustained reverse remodeling and significant recovery of systolic LV function (LVEF 47%). Conclusions: This case illustrates that rapid post-infarction aneurysmal expansion may occur despite apparent clinical stability. Comprehensive multimodality imaging may help identify selected patients in whom a reconstructible myocardial substrate supports surgical ventricular restoration despite severely reduced LVEF, even when conventional clinical indications for aneurysmectomy are absent. Full article

This study introduces a modular approach to the validation of non-invasive coating inspection. It involves RGB imaging, visible and near-infrared spectroscopy, measurements of the physical parameters of the coating, and 3D Gaussian Splatting. As publicly available datasets containing these different types of information for the same coating samples are rare, each module was considered separately. The study also defines the type of paired data required for future data-fusion experiments. During the analysis of the painted-metal image dataset, exact-file hashing identified 68 duplicate images shared between the training and test sets. After removing these images and the duplicates within the test set, the best fixed $48\times 48$ image classifier achieved an accuracy of 78.49% and a balanced accuracy of 78.99% on 93 distinct test images. In addition, an additional sensitivity analysis was conducted using an ImageNet-pretrained EfficientNetB0 model with $224\times 224$ images. This model achieved an accuracy of 66.67%. This result shows that higher-resolution images and a pretrained model do not directly improve performance on a small and domain-specific dataset. Coated-wood defect localization, controlled steel-defect detector comparison, duplicate-grouped ship-coating spectroscopy, public gloss, roughness, scanning-electron-microscopy workbooks, and reflective-scene reconstruction were recomputed as separate validation blocks. The reflective-scene reconstruction achieved a peak signal-to-noise ratio of $25.018\mathrm{dB}$, a structural similarity index of $0.8947$, and a learned perceptual image patch similarity value of $0.1522$. However, camera distortion and unidirectional geometry limitations restrict this module to feasibility analysis. Our framework provides reproducible baselines and defines the paired-data requirements for future calibrated coating-monitoring systems. Full article

Recently, deep learning has enabled unmanned aerial vehicles (UAVs) to detect human bodies in aerial imagery, which is of particular importance in post-disaster situations such as floods and storms. Yet progress in this domain remains constrained by a familiar obstacle: the shortage of annotated training data. Neural networks, while powerful, are highly sensitive to data volume and diversity. Existing augmentation strategies help reduce this gap but typically introduce only incremental novelty, especially with respect to viewpoint variation, thereby limiting dataset richness. In this work, we propose a complementary strategy that leverages three-dimensional human models reconstructed via photogrammetric techniques. By situating these models within a controlled rendering environment, we generate synthetic imagery across a broad range of elevations and camera angles—perspectives that are rarely captured in conventional UAV datasets. These additions are designed to increase both the variability and the resilience of the training corpus. To evaluate the contribution of this approach, a custom CNN deep convolutional neural classifier was trained and benchmarked on a UAV human vs. non-human patch dataset of 4000 baseline images (128 × 128 px; 2800 train, 600 validation, 600 test), expanded with 3000 photogrammetry-derived synthetic patches (balanced by class) to 7000 total images for the 3DG setting. The primary metric was classification accuracy on the held-out test set, consistent with patch-level evaluation practice; detection-style metrics such as AP/IoU were not applicable to this binary classification protocol. Averaged over five independent training runs, the proposed augmentation improved classification accuracy by 3.02 percentage points over the baseline (88.06 ± 0.97% → 91.08 ± 1.03%), with consistent gains in precision, recall, and F1-score. When combined with standard augmentations (rotation, translation, scaling, flipping), accuracy reached 95.21 ± 0.61%, a gain of 7.15 percentage points over the baseline. These results suggest that photogrammetry-based augmentation offers a practical and effective enhancement for UAV-based human detection pipelines where timely, reliable identification is critical. Full article

Over the past three decades, five satellites have succeeded one another on the reference orbit, building the longest continuous climate record of global sea level measurements. Its continuity is ensured thanks to tandem flights between consecutive satellites. In this paper, we demonstrate that the first satellite of the Sentinel-6 series (Sentinel-6 Michael Freilich) has enabled the detection of a processing anomaly in the Jason-1/2/3 ground segment. An inconsistency in the altimeter range reconstruction has been identified, causing its underestimation by 3.65 mm. At certain latitudes, determined by the satellite’s orbital velocity, the altimeter range shows no effect from the anomaly. For the reference orbit, the range is not affected at the poles, around the equator and, for ascending tracks, at 40° S. All Jason Geophysical Data Record (GDR) versions prior to GDR-G are impacted by the described processing anomaly. While a full reprocessing of the Jason data with the GDR-G standard is pending, this paper presents a latitudinal empirical correction to be applied to Jason datasets generated with GDR-F and earlier ground segments. This correction, to be applied on the altimeter range, is derived from one month of patched Jason-3 data and is intended for reference orbit only. Additionally, SWOT Nadir ground processing is also affected by the same processing error and has been corrected from the GDR-S2 version onward. Finally, our analysis shows a negligible impact of this processing anomaly on Jason Level-2-derived products, models and metrics. Full article