Search Results (8,482)

Cloud detection is essential for quantitative land-surface remote sensing and cloud-climate research. However, existing methods often prioritize spatial features over spectral features, which limits thin-cloud detection. To address this issue, this paper proposes a Thin-Cloud-Sensitive Network (TCSNet) for hyperspectral imagery. TCSNet employs an encoder–decoder architecture with a dual-branch design: a convolutional neural network (CNN) extracts multi-scale local features, while a PVTv2-B2 Transformer captures long-range spectral dependencies. To effectively integrate the complementary representations from both branches, a Cross-Modal Fusion (CMF) module with a lightweight single-channel gate is introduced at each stage, followed by a channel attention mechanism (SE) for feature recalibration. Subsequently, a Multi-Scale Fusion (MSF) module is used to integrate multi-level features through a top-down pathway, enabling deep semantic information to guide shallow feature expression. Furthermore, to enhance the decoder’s feature representation capability, a Combined Attention Mechanism (CAM) is incorporated at each decoder stage. This design enables the network to simultaneously focus on important channels, salient regions, and cloud boundaries, effectively alleviating spectral confusion between thin clouds and the underlying surface. Experimental results on Gaofen-5 01 hyperspectral data demonstrate that TCSNet achieves the highest recall (92.98%), ${\mathrm{R}\mathrm{e}\mathrm{c}\mathrm{a}\mathrm{l}\mathrm{l}}_{thin}$ (85.59%), and ${\mathrm{R}\mathrm{e}\mathrm{c}\mathrm{a}\mathrm{l}\mathrm{l}}_{thick}$ (99.75%), thereby validating its superiority for thin-cloud detection. Full article

Under low-light conditions, visible-spectrum images are prone to detail loss and contrast degradation, which substantially limits object detection performance. Although infrared imagery can provide complementary cues, direct fusion often introduces noise interference and thus undermines detection stability. To address this issue, this paper proposes a discriminative enhancement and selective fusion method for low-light cross-spectral object detection. Specifically, a task-oriented discriminative Retinex enhancement module is introduced at the front end to mitigate illumination interference while strengthening structural information. Meanwhile, a spectral-selective cross-scale fusion module is designed to suppress noise propagation through adaptive weighting and cross-scale interaction. In addition, mutual information loss and cross-scale consistency constraints are incorporated to enhance cross-spectral feature representation and prediction stability. Experimental results on multiple public datasets demonstrate that the proposed method can consistently improve the accuracy and robustness of object detection under low-light conditions. Full article

Native cellulose I can be converted into crystalline polymorphs II and III_I, while cellulose II can be further converted into III_II through chemical treatments that induce significant structural, physical, and chemical changes. Accurate identification and differentiation of these polymorphs is essential for predicting fiber reactivity and processing behavior, but current methods are time-consuming. This study demonstrates the potential of near-infrared hyperspectral imaging (HSI-NIR) combined with linear discriminant analysis as a rapid, non-destructive tool for polymorph discrimination. Cellulose I, II, III_I, and III_II were produced from bleached kraft pulps of eucalyptus and pine and from cotton linters using NaOH (20% w/v) and ethylenediamine treatments. HSI-NIR successfully differentiated polymorphs based on spectral signatures in the 1480–1600 nm range, regardless of botanical source. Complementary characterization by XRD confirmed polymorph conversions, showing crystallinity reductions of 10–15% for cellulose I→II and I→III_I conversions, with crystallite size decreasing from 7.2 nm (cotton CI) to 3.2–3.4 nm in all CIII_II samples. XPS analysis revealed increased C-O surface accessibility in cellulose II and III, with complete disappearance of COOH groups in cellulose III samples. These results establish HSI as a promising screening tool for cellulose polymorph identification (>95% classification accuracy) and provide comprehensive baseline data on structural and chemical transformations that govern fiber reactivity in chemical and enzymatic processes. Full article

This study presents the development and implementation of a web-based geospatial platform for the quantitative assessment of land use and land cover change (LULCC) based on multispectral satellite images. The system operationalizes the Rao spectral diversity metric (Rao’s Q) to detect and quantify LULCC resulting from different environmental agents. The platform supports single-band (classic mode) or multi-band (multidimensional mode) processing. Its main functionalities include the interactive de-limitation of areas of interest (AOI) and calendar-based temporal selection, allowing analyses to be performed at discrete time points or at defined intervals. Among the tools available in the application are the automated calculation of Rao’s Q surfaces and maps of change between pairs of dates. Additionally, the platform allows the selection of several spectral indices, with the aim of supporting ecosystem monitoring and the characterization of the Earth’s surface. In the use case demonstration (Reykjanes Peninsula volcanic eruption of February 2024), the Rao’s Q method applied to Sentinel-2 SWIR imagery demonstrated strong performance in lava flow detection, with the multidimensional approach (bands 11 + 12) achieving the most balanced results (OA = 83.0%, PA = 84.0%, UA = 82.4%), while band 11 alone yielded the highest precision (UA = 97.4%). By integrating spatiotemporal analysis, spectral diversity metrics, and spectral indices into an accessible and extensible framework, the platform constitutes a robust tool for monitoring LULCC and assessing environmental impacts. Full article

Background and Clinical Significance: To describe two cases within the spectrum of non-myopic foveomacular retinoschisis, including stellate non-hereditary idiopathic foveomacular retinoschisis (SNIFR) and central anomalous retinoschisis with mid-peripheral traction (CARPET), and to highlight the role of multimodal imaging in identifying vitreoretinal traction in their pathogenesis and management. Case Presentation: First Case Report: A 57-year-old man presenting with bilateral visual decline. Multimodal imaging, including spectral-domain and en face optical coherence tomography (OCT), demonstrated characteristic features of SNIFR, with schisis at the Henle fibre layer and outer plexiform layer and persistent posterior hyaloid adhesion. Medical treatment was ineffective. Over two years, complete posterior vitreous detachment occurred, followed by spontaneous anatomical resolution of the schisis and full visual recovery. Second Case Report: A 63-year-old man with severe unilateral visual loss. Imaging revealed marked mid-peripheral vitreoretinal traction extending toward the posterior pole; associated with foveoschisis, central neurosensory detachment, and an outer lamellar macular hole, consistent with CARPET syndrome. The patient underwent pars plana vitrectomy with traction release. Postoperatively, complete anatomical resolution of both macular and peripheral schisis was achieved, with partial visual recovery. Conclusions: These cases support vitreoretinal traction as an important pathogenic mechanism in selected forms of non-myopic foveomacular retinoschisis. SNIFR may resolve spontaneously following posterior vitreous detachment, whereas CARPET represents a more severe tractional phenotype that may require surgical intervention. Careful multimodal imaging assessment of the vitreoretinal interface is essential for accurate diagnosis and management. These findings further characterise CARPET and expand the clinical spectrum of traction-related non-myopic foveomacular retinoschisis. Full article

Disease-specific alpha-synuclein (αsyn) strains have been linked to different synucleinopathies. Current αsyn biomarkers are limited to binary detection of pathogenic αsyn in peripheral tissue biopsies or fluids, limiting differential diagnosis. Hence, there is an urgent need for methods that allow strain-specific detection and characterization of αsyn strain architecture. Notably, luminescent conjugated oligothiophenes (LCOs) have been successfully used to detect distinct protein strain conformers in prion diseases and Alzheimer’s disease, highlighting their utility in differentiating disease-specific amyloid structures. Species-dependent differences in αsyn structure are increasingly recognized as one of the critical aspects that shape how fibrils form, propagate and interact with molecular LCO probes. Here, we evaluate the potential of the LCO h-FTAA to differentiate species-specific αsyn strains and conduct a translational investigation using peripheral cardiac tissue of a gut-first synucleinopathy rodent model. Our in vitro data demonstrate strain-specific probe–fibril interactions, reflecting a differential strain architecture and cellular micro-environment. While h-FTAA binds with comparable efficiency to mouse (mo-) and human (hu-) pre-formed fibrils (PFFs), h-FTAA exhibits markedly lower quantum yield when bound to moPFFs versus huPFFs. Spectral imaging revealed h-FTAA-moPFF binding produces blue-shifted maxima (505–550 nm), contrasting with the red-shifted maxima (545–580 nm) of huPFFs. Fluorescence lifetime imaging microscopy confirmed h-FTAA’s intrinsic sensitivity to species-dependent variations through distinct temporal fluorescence signatures (moPFFs: ~0.60–1.5 ns vs. huPFFs: ~0.65–1.0 ns). Our translational investigation showed h-FTAA binding to peripheral cardiac pathology exhibits comparable red-shifted emission, but distinct fluorescence lifetimes of h-FTAA-bound aggregates in moPFF-injected (~1.0–1.4 ns) versus huPFF-injected (~0.69–0.8 ns) rats. Interestingly, we observed distinct blue-shifted emission profiles in a few selected regions of the heart of moPFF-injected rodents, further characterized by extra-long fluorescence decay shifts (~1.5–1.9 ns), reflecting differences in both aggregate conformation and maturity in moPFF-induced compared with huPFF-induced rats. Taken together, our findings underscore the potential of LCO ligands, like h-FTAA, to enable more precise disease staging and diagnosis through peripheral biopsies, complementing existing αsyn biomarker methods. Full article

Spectral remote sensing enables efficient acquisition of large-scale land surface information and is a key approach for monitoring soil salinity content (SSC). However, surface mulching significantly alters the spectral reflectance responses of croplands, increasing the uncertainty of SSC retrieval using remote sensing. This study aimed to systematically identify SSC-sensitive spectral features under different mulching conditions and to evaluate the effects of spatial resolution on SSC–spectral relationships. Multi-resolution datasets were constructed based on plastic mulch geometric parameters, and SSC–spectral relationships were analyzed using correlation methods and recursive feature elimination (RFE). Results indicate that under near-ground ultra-high-resolution conditions, the correlation between inter-mulch bare soil spectral features and SSC was weakly influenced by mulch type, and distinguishing mulch types provides limited improvement in inter-variable relationships. Pearson’s r exceeded 0.40 for both white- and black-mulched samples, and distinguishing mulch types provided only marginal gains in model accuracy (RFR–RFE R² = 0.9524 for white-mulched and 0.9252 without distinguishing; R² = 0.9387 for black-mulched). In contrast, under multi-resolution settings at the field scale, separating black-mulched, white-mulched, and non-mulched fields significantly enhanced the correlation between spectral indices (SIs) and SSC, with the coefficient of determination (R²) based on the recursive feature elimination (RFE) algorithm increasing by up to 0.28. The highly sensitive SIs of non-mulched farmland are generally consistent with those of white-mulched farmland but differ markedly from those of black-mulched farmland. Scale optimization analysis further indicated that the optimal spatial resolution was 1.35 m for white-mulched and non-mulched farmland. Black-mulched farmland performed best at 5.4 m, likely because stronger spectral masking by black mulch increases mixed-pixel dominance and benefits from spatial aggregation. These findings provide methodological guidance and practical approaches to accurately retrieve SSC in plastic-mulched croplands and to determine the optimal image spatial resolution. Full article

Background: Multi-center resting-state functional magnetic resonance imaging (rs-fMRI) is important for neurodevelopmental disorder diagnosis, but cross-site differences in repetition time (TR) can cause temporal feature misalignment. In addition, blood-oxygen-level-dependent (BOLD) signals are non-stationary, so disease-related information may be distributed across multiple time scales. Existing methods usually do not explicitly model physical sampling intervals or coordinate temporal and spectral information across scales, which may limit cross-site generalization in heterogeneous multi-center settings. Methods: We propose Spec-RWKV, a spectrum-guided linear recurrent framework for multi-site rs-fMRI diagnosis. It includes three components: PrismTimeMix, which models temporal dynamics using decay rates derived from physical half-lives and converts them adaptively across TRs; a TR-adaptive continuous wavelet transform, which aligns spectral representations across sites by adjusting frequency coverage; and spectrum-guided adaptive temporal aggregation, which uses spectral context to weight temporal features. Results: On ABIDE-I and ADHD-200, Spec-RWKV achieved AUCs of 75.86% and 76.31%, respectively. Under leave-one-site-out validation, it achieved the best mean AUC on ABIDE-I and the best mean accuracy and AUC on ADHD-200. Conclusions: Spec-RWKV explicitly models sampling-rate differences and multi-scale spectral structure, with results supporting strong cross-site generalizability. Full article

Terahertz technology has demonstrated immense potential across a wide range of applications, particularly in the realm of THz photodetection. However, state-of-the-art detectors typically face fundamental trade-offs among sensitivity, response speed, operating temperature, and spectral bandwidth. While previous studies have shown that graphene field-effect transistors (GFETs) exhibit a broadband, room-temperature photoresponse to THz radiation—often attributed to photothermoelectric (PTE) and plasma-wave rectification effects—the similar functional dependence of these mechanisms on the gate voltage has historically made it challenging to disentangle their individual contributions. In this study, we leverage monolayer graphene as the photoactive material to overcome these limitations within a single device architecture. We present a novel THz photodetector driven predominantly by the PTE effect, facilitated by a precisely designed counterclockwise spiral antenna. The demonstrated device achieves exceptional room-temperature sensitivity, featuring a minimum noise equivalent power (NEP) of 80.7 $\mathrm{p}\mathrm{W}/\sqrt{\mathrm{H}\mathrm{z}}$ alongside a rapid response time of less than 11 μs. Furthermore, by systematically analyzing the temporal response dynamics, we unambiguously identify the PTE effect as the dominant operating mechanism. These results provide a robust strategy for the development of high-performance, room-temperature THz optoelectronics, paving the way for advanced practical applications in high-capacity wireless communications and real-time THz imaging. Full article

This study investigates the thermal and vibration-attenuation performance of a novel 7-inch FPV drone frame manufactured from cast AZ31 magnesium alloy (MG), compared to 6061-T6 aluminum (AL) and carbon fiber (CF) composite structures under an extreme payload of 2 kg. Using quantitative spectral analysis of Blackbox flight logs, the research demonstrates that the MG frame provides superior system-level vibration damping, particularly under high-stress conditions. Under a 2 kg payload, the MG frame exhibited a 49% reduction in vibration power compared to the AL frame. Spectral data identified primary resonance peaks for the MG frame at 147 Hz (0 kg) and 204 Hz (2 kg), whereas the AL frame showed significantly higher frequency peaks at 179.5 Hz (0 kg) and 239.4 Hz (2 kg). Comparative modal hammer tests further validated these findings, with the magnesium design exhibiting lower impulse energy (0.22 mW/Hz) and faster decay than aluminum (0.24 mW/Hz). Thermal imaging analysis showed better motor cooling for the metallic frames; average motor temperatures on the magnesium frame (51.8 °C) and AL frame (50.3 °C) were significantly lower than on the CF structure (77.5 °C). The findings establish that AZ31 magnesium alloy offers an excellent synergy of lightweight stiffness and damping capacity, making it a viable alternative for heavy-duty FPV platforms requiring high signal integrity. Full article

Carotid artery disease, including atherosclerotic stenosis and non-atherosclerotic abnormalities, substantially increases ischemic stroke risk and motivates accessible tools for early screening. Current diagnostic pathways rely on clinic-based imaging and skilled operators, creating barriers to frequent monitoring and scalable deployment. We present a non-invasive diagnostic approach using a wearable MEMS accelerometer patch to capture mechano-acoustic vibrations generated by carotid blood flow at the neck. The miniature device integrates a hermetically sealed wideband accelerometer with out-of-plane sensitivity and micro-g resolution to detect subtle flow-induced vibrations. We validated the approach in a carotid flow phantom and a clinical study of 74 patients. Time–frequency representations were computed using the continuous wavelet transform (CWT), from which interpretable spectral and scalogram-derived candidate biomarkers were extracted. Six non-redundant features were then selected for multivariate classification, distinguishing pathology, defined as 50% or greater stenosis or a non-atherosclerotic abnormality, from non-pathology, defined as less than 50% stenosis. Finally, model interpretability was assessed using SHapley Additive exPlanations (SHAP) to quantify the contribution of each biomarker to predicted disease probability. These findings resulted in an AUROC of 0.97 and AUPR of 0.947, with 81.7% sensitivity and 93.6% specificity at the prespecified threshold (precision 85.4%, F1 83.5%, accuracy 89.8%), highlighting the potential of wearable seismic sensing combined with interpretable machine learning for fast screening and longitudinal monitoring of the right and left carotid arteries. Full article

Spectral interrogation of fiber Bragg gratings (FBGs) in the ~850 nm band remains relatively uncommon, largely due to the limited availability of commercial instruments and the restricted applicability of conventional interrogation schemes in this wavelength range. This work presents a practical and high-precision wavelength demodulation method for 850 nm FBG sensing based on an imaging Charge-Coupled Device (CCD) spectrometer. A Czerny–Turner (C–T) optical configuration is employed for spatial spectral dispersion, and the optical system is theoretically analyzed and optimized using ZEMAX to balance spectral resolution, optical throughput, and compactness. A polynomial wavelength–pixel calibration model is established, and Gaussian fitting is adopted for robust peak-position extraction under multimode fiber conditions. Experimental validation is carried out using four serially cascaded FBGs distributed over 830–880 nm. The wavelength–pixel calibration yields an RMS residual of 0.46 nm. Within a strain range of 0–2000 με, the average wavelength demodulation bias of a single FBG is 6.8 pm, with a wavelength demodulation RMS error of 86.9 pm and a measured strain sensitivity of 0.72 pm/με. The results demonstrate that the proposed CCD-based imaging interrogation scheme is feasible for 850 nm FBG sensing and enables accurate wavelength demodulation in this relatively underexplored band. Since the system is implemented using standard off-the-shelf components, it also provides a practical technical route for the deployment of FBG sensing systems in engineering applications. Full article

To improve the low sensitivity of electromagnetic acoustic testing (EMAT) to micro-debonding defects in metal–rubber bonded structures, this study proposes a detection framework combining a magnetic-field-enhanced focusing EMAT with entropy-weighted multi-feature fusion imaging. First, a Halbach-type focusing magnet was designed and evaluated through finite element simulations, showing a substantial enhancement of the effective bias magnetic field in the working region. Then, three complementary echo features, namely amplitude (AMP), time-domain integral (TDI), and power spectral density (PSD), were extracted from the acquired resonance signals and integrated using an adaptive entropy-weighted fusion strategy. Comparative and ablation analyses were further conducted to distinguish the respective contributions of probe enhancement and feature fusion, and to compare entropy-weighted fusion with single-feature imaging and equal-weight fusion. The results indicate that the focused probe mainly improves the defect-response strength at the hardware level, whereas feature fusion mainly improves image contrast, background suppression, and segmentation consistency at the image level. Among the compared methods and under the present experimental conditions, entropy-weighted fusion provides the best overall imaging performance. Under the present experimental conditions, the proposed framework enables reliable detection of 5 mm debonding defects in aluminum-alloy–rubber bonded specimens and 10 mm debonding defects in titanium-alloy–rubber bonded specimens. These results suggest that the combined use of magnetic-field focusing and adaptive multi-feature fusion is a promising approach for the detection and quantitative characterization of micro-debonding defects in metal–rubber bonded structures. Full article

Object detection in optical remote sensing imagery has emerged as a crucial task in computer vision, with applications ranging between environmental monitoring to disaster management, precision agriculture, and urban planning. This review systematically examines current methodologies, categorising them into four principal approaches: (1) template matching-based methods, which leverage predefined patterns for object identification; (2) knowledge-based methods, which incorporate geometric and contextual information to enhance detection accuracy; (3) object-based image analysis (OBIA), which segments images into meaningful objects using spectral and spatial properties; (4) machine learning-based methods, particularly deep convolutional neural networks (CNNs), which have revolutionised the field through automatic feature learning. Each methodology’s performance characteristics, computational requirements, and suitability for different remote sensing applications are analysed. Our systematic review, following PRISMA guidelines, analysed 189 studies published from 2010 to 2025, of which 73 provided quantitative results on standard benchmarks. The three most critical challenges identified are as follows: (1) annotation bottleneck, as dense bounding box labelling of remote sensing imagery remains highly labour-intensive for deep learning approaches, (2) extreme scale variation spanning 2–3 orders of magnitude within single scenes, and (3) domain adaptation failures when models encounter new geographic regions or sensor characteristics. This review identifies critical research gaps and proposes prioritised future directions, emphasising foundation models for zero-shot detection, efficient architectures for resource-constrained deployment, and standardised benchmarks with size-specific metrics. The analysis provides practitioners with evidence-based decision frameworks for method selection and researchers with a roadmap for advancing object detection in remote sensing applications. Full article

Accurate extraction of impervious surface areas (ISA) is essential for urban environmental monitoring, yet severe spectral confusion among complex urban land-cover types limits the performance of classifications based solely on optical imagery. To address this issue within a localized context, this study proposes a multi-source framework integrating UAV-based LiDAR (UAV-LiDAR) and high-resolution visible imagery for fine-scale ISA extraction. An improved segmentation optimization strategy, termed EGS-Optimizer, is developed to enhance boundary delineation within the object-based image analysis (OBIA) framework by coupling edge detection with global segmentation quality evaluation. A comprehensive feature set including spectral, index, texture, geometric, and terrain features is constructed, and Shapley Additive Explanations (SHAP) is applied to select the most informative variables while reducing dimensionality. The proposed framework is validated in a typical 1.45 km² built-up area in Deyang City, Sichuan Province. Experimental results demonstrate that, within this specific study area, multi-source data fusion improves classification accuracy by 3.59–5.79% compared with single-source data, while feature selection reduces the feature dimension from 45 to 21. Among the evaluated classifiers, the random forest (RF) model achieves the highest performance, with an overall accuracy of 97.24% (Kappa = 0.96). While the high accuracy highlights the efficacy of synergizing spectral and structural information for micro-landscape mapping, these findings are constrained to the demonstrated fine-scale local environment. The results provide an effective, interpretable solution for detailed neighborhood-level ISA mapping, though further validation is required before the framework can be generalized to larger or more heterogeneous urban scenarios. Full article