Search Results (150)

Wheat is a major staple crop, and storage mold growth poses a severe threat to grain safety and quality stability. Natural mold development in stored wheat exhibits subtle, localized, and highly heterogeneous characteristics. Existing unimodal methods and global fusion approaches generally suffer from insufficient local feature sensitivity, hindering fine-grained mold severity grading. To address this limitation, we propose a Mask-Guided Fine-Grained Fusion Network, a weakly supervised framework based on local RGB–HSI fusion. This framework employs a dynamic parallel A/B experimental design to construct time-matched proxy labels via weakly supervised learning. A standardized preprocessing pipeline including single-kernel extraction, foreground segmentation, and cross-modal registration is established to resolve RGB–HSI spatial misalignment, ensuring physical-level spatial consistency of multimodal features. The model incorporates a Foreground-Aware Spectral Recalibration (FASR) module to suppress background noise, a Mask-Guided Dilated Cross-modal Local Attention (MDCLA) mechanism to establish fine-grained local mappings between RGB visual phenotypes and hyperspectral responses, and a sample-level adaptive fusion strategy to dynamically weight features by modal reliability, enhancing representation of complex samples across all mold stages. Experiments show that the Mask-Guided Fine-Grained Fusion Network achieves 0.9689 classification accuracy, 0.9698 Macro-F1 score, and 0.0593 Mean Absolute Error (MAE), significantly outperforming state-of-the-art unimodal deep models and global attention fusion baselines. This work provides a proof-of-principle framework for fine-grained non-destructive mold risk assessment in stored wheat. Full article

Single-molecule Förster resonance energy transfer (smFRET) enables direct measurement of nanoscale conformational dynamics and heterogeneity in biomolecules, but quantitative interpretation of smFRET data critically depends on well-controlled excitation geometry, low background fluorescence, robust calibration, and reproducible data-analysis workflows. Prism-based total internal reflection fluorescence (pTIRF) microscopy provides important advantages for such measurements by physically separating excitation and emission paths and generating a highly confined evanescent field, yet practical guidance for implementing reproducible, quantitative pTIRF systems remains fragmented. Here we present a comprehensive, standardized framework for the design, alignment, calibration, validation, and operation of a prism-based TIRF microscope optimized for single-molecule fluorescence measurements. We describe the complete optical architecture for dual-color excitation and detection, establish alignment invariants that ensure reproducible evanescent excitation and stable donor–acceptor channel registration, and detail surface preparation, flow control, and photostabilization strategies required for reliable long-term imaging. Quantitative benchmarking protocols are introduced to evaluate signal-to-noise ratio, photobleaching kinetics, and spectral crosstalk, providing objective criteria for defining optimal operating conditions and instrument performance limits. Finally, we integrate these experimental procedures with an end-to-end single-molecule data-analysis workflow encompassing channel registration, automated and manual trajectory selection, FRET calculation, and kinetic analysis using hidden Markov modeling. The utility of the platform is demonstrated through smFRET measurements of conformational dynamics in a model nucleic acid system. Together, this work provides a reproducible and accessible methodology for implementing prism-based TIRF microscopy as a robust quantitative platform for single-molecule fluorescence studies across a wide range of biomolecular systems. Full article

Unmanned aerial vehicles (UAVs) are broadly used for high-throughput plant phenotyping, yet their long-term use in public-sector research is increasingly challenged by regulatory restrictions and reliance on proprietary platforms. This study presented a regulation-compliant, modular multi-sensor unmanned aerial system (UAS) designed to deliver flexible, high-quality phenotyping data without dependence on restricted ecosystems. A dual-mount, open-architecture payload integrated RGB, multispectral, and thermal sensors, enabling simultaneous acquisition of structural, spectral, and thermal information within a unified workflow. Field validation in a lantana (Lantana camara) breeding trial demonstrated high-precision multi-sensor data fusion and reliable trait extraction. Spatial co-registration achieved centimeter-level accuracy, with alignment errors of 0.88 cm (multispectral) and 3.23 cm (thermal) relative to the RGB reference. UAV-derived canopy height closely matched ground measurements (R² up to 0.98; RMSE as low as 1.57 cm), while canopy coverage estimates showed consistency across sensing modalities (R² = 0.99; RMSE = 0.02 m²). Calibrated thermal orthomosaics provided robust canopy temperature estimation (RMSE = 3.13 °C), supporting a quantitative assessment of plant physiological status. Together, these results demonstrate that a regulation-compliant, open-architecture UAV platform can achieve high accuracy in multi-modal phenotyping while maintaining flexibility and cost efficiency. This work demonstrates a scalable and sustainable framework for UAV-based phenotyping, enabling researchers to adapt to evolving regulations while advancing data-driven crop improvement. Full article

With increasing demand for fine-scale ecological management under carbon neutrality frameworks, multi-temporal assessment of carbon stock change (ΔC) at the individual-plant scale has become essential for understanding plant-level carbon dynamics and supporting management decisions. However, methodologies for repeated monitoring at this scale remain fragmented, showing limited cross-temporal comparability, weak cross-scale consistency, and insufficient integration across methods. Existing approaches can be grouped into three pathways: (i) process-based methods derived from CO₂ exchange measurements, (ii) state-based approaches estimating biomass and ΔC, and (iii) sensing-based approaches using structural, spectral, thermal, and fluorescence signals. These approaches offer complementary strengths, yet none simultaneously achieve high accuracy, temporal continuity, and operational scalability for multi-temporal ΔC estimation. Among these, stock-based and structural approaches form the primary estimation pathways, while flux-based and functional sensing methods provide complementary constraints. This review synthesizes and compares these approaches in terms of their theoretical basis, spatial support, temporal characteristics, and uncertainty structures. To address the lack of methodological integration, we propose a structure–function–scale framework that links heterogeneous observations across spatial and temporal domains and emphasizes cross-scale consistency as a prerequisite for reliable ΔC estimation. Within this framework, we further examine how multi-source integration can connect structural and functional observations through segmentation, co-registration, scaling, temporal alignment, and uncertainty propagation. By integrating traditional measurement logic with emerging remote sensing technologies, this review provides a unified methodological framework for ΔC estimation and identifies key directions for advancing fine-scale carbon monitoring, spatiotemporally consistent data fusion, uncertainty-aware inference, and MRV-oriented verification systems. Full article

Objectives: To assess the association of structural biomarkers derived from optical coherence tomography (OCT) and choriocapillaris (CC) flow information with point-wise retinal sensitivity (PWS) measured by microperimetry (MP) in intermediate age-related macular degeneration (iAMD). Methods: Patients with iAMD received imaging with spectral-domain (SD)-OCT (Spectralis, Heidelberg Engineering) and OCT-angiography (OCT-A) (PLEX Elite 9000, ZEISS). In addition, MP examinations in photopic setting (MP-3, NIDEK) and mesopic background illumination (MAIA2, ICare) were performed. The thickness of the ellipsoid-zone (EZ) and the outer nuclear layer (ONL), as well as the volume of drusen and HRF, were segmented using deep-learning (DL)-based approaches. CC flow deficit percentage (FD%) was extracted from OCT-A slabs using a novel binarization method. Semiautomatic co-registration of MP examinations, OCT-A slabs, and OCT volumes was performed. Three exploratory models were calculated using multivariable mixed-effects models: (1) structure–function (SF) using structural OCT biomarkers, (2) flow–function (FF) utilizing OCT-A derived flow information, and (3) structure–flow–function (SFF) incorporating both OCT and OCT-A data. Model performance was evaluated using AIC and BIC criterion. Results: 19 eyes of 19 patients were evaluated, totalling 3297 MP-stimuli, 1873 B-scans, and 19 OCT-A slabs. Mean (SD) age was 76 (7) years, and sensitivity was 26.0 (3.36) dB in the MP-3 and 22.42 (3.64) dB in the MAIA2. Mesopic MAIA2 examinations showed significantly lower PWS values (−3.56 to −3.63 dB; p < 0.001). Drusen and HRF volume decreased PWS (−0.6 [95% CI: −1.04; −0.16] dB/nL; p = 0.007 and −9.56 [95% CI: −12.86; −6.26] dB/nL; p < 0.001), while ONL was positively associated with PWS (0.06 [0.05; 0.07] at an eccentricity of 5.2°; p < 0.001) in the SF model. CC FD% was not significantly associated with PWS in the FF and the SFF model (p > 0.05 in both cases). In the SFF model drusen volume (−1.69 [95% CI: −2.09; −1.29] dB/nL; p < 0.001), EZ (0.04 [95% CI: 0.02; 0.06] dB/µm; p < 0.001), and ONL thickness (0.03 [95% CI: 0.02; 0.04] dB/µm; p < 0.001) were significant predictors for PWS. The SF model exhibited the lowest AIC and BIC indicating best model performance. Conclusions: Structural parameters derived from SD-OCT such as HRF, drusen volume, and outer retinal layer thickness may be more closely associated with PWS, with CC FD% as an OCT-A-derived metric contributing limited additional explanatory benefit in cross-sectional analyses. Full article

Synthetic Aperture Radar (SAR) and optical image fusion technology plays a crucial role in remote sensing applications. It effectively combines the high spatial resolution and rich spectral information of optical images with the all-weather and penetrating observation advantages of SAR images, thereby significantly enhancing image interpretation accuracy and task execution capabilities. This paper systematically reviews deep learning-based fusion methods for SAR and optical images, with a particular focus on recent advances in deep learning models. Furthermore, it summarizes commonly used evaluation metrics for assessing fusion image quality, providing a basis for comparing and analyzing the performance of different methods. In addition, commonly used SAR-optical fusion datasets are briefly reviewed to highlight their roles in algorithm development and performance evaluation. Unlike conventional review articles, this paper further analyzes the guidance and supporting role of fusion algorithms from the perspective of typical and specific applications. Finally, it identifies key challenges and issues faced by current fusion methods, including data registration, model lightweight design, and multimodal feature alignment, and offers perspectives on future research directions. This review aims to provide routes and references for the development of SAR and optical image fusion technology. Full article

Semantic change detection (SCD) in remote sensing images aims not only to localize changed regions but also to identify their specific “from–to” semantic transitions. This task remains challenging due to the inherent semantic ambiguity of spectral changes and the presence of pseudo-change noise. While recent vision–language models have shown promise in remote sensing, existing approaches like RemoteCLIP predominantly focus on static scene classification, lacking the ability to explicitly model dynamic temporal transitions. Other adaptations of foundation models (e.g., AdaptVFMs-RSCD) often rely on heavy backbones, incurring prohibitive computational costs. To address these limitations, this paper proposes LGDENet, a lightweight, end-to-end framework that unifies Language-Guided Temporal Contrastive Learning with a noise-robust difference enhancement mechanism. Specifically, we construct a temporal transition prompt learning strategy that aligns visual difference features with textual descriptions of dynamic processes, thereby resolving directional semantic ambiguities. Furthermore, we introduce a Difference Enhancement Module (DEM) that leverages the channel–spatial decoupling property of depthwise separable convolutions to adaptively isolate and suppress irrelevant variations (e.g., registration errors) before feature fusion. Experiments on the SECOND and Landsat-SCD datasets demonstrate that LGDENet achieves state-of-the-art performance, yielding a semantic F1 score ($F_{scd}$) of 87.90% and 88.71%, respectively. Moreover, with a modest parameter count of 33.45 M, it offers a superior trade-off between accuracy and efficiency compared to heavy foundation model-based approaches. Full article

To address the technical challenge of simultaneously achieving snapshot imaging capability and high spectral resolution in thermal infrared spectral imaging, this paper proposes a computational imaging method based on a snapshot divided-aperture imaging system. In this method, a self-developed divided-aperture snapshot multispectral camera is utilized to simultaneously capture nine low-spectral-resolution images in a single exposure. The precise registration of the sub-channel images is accomplished via a star-point array calibration method. To construct the spectral reconstruction dataset, a Fourier-transform infrared hyperspectral camera (FTIR HCam) is employed to simultaneously acquire hyperspectral data from real-world scenes. Based on this, a neural network model is applied to reconstruct 127-channel hyperspectral information from the low-dimensional multispectral measurements. Experimental results demonstrate that the proposed method effectively achieves hyperspectral reconstruction while maintaining system compactness and snapshot imaging capability, thus providing a viable technical approach for hyperspectral sensing in dynamic thermal infrared scenarios. Full article

Replacing inefficient lighting with energy-efficient alternatives is a proven way to reduce urban energy use, yet evaluating such policies remains challenging. For example, in 2013, New York City (NYC) initiated a program to replace 250,000 high-pressure sodium (HPS) streetlights with light-emitting diodes (LEDs) by 2017, but no subsequent evaluation was published. Here, we employ ground-based hyperspectral imaging (HSI; 0.4–1.0 microns, ∼850 bands) observations from the “Urban Observatory” (UO), obtained in 2013 and 2018, to quantitatively characterize this technological transition. Following co-registration, artifact removal, and source identification, we classified individual light source technologies using both a maximum correlation approach with spectral templates of known lighting types and a one-dimensional Convolutional Neural Network (1D-CNN) trained on 1321 manually labeled spectra, achieving an average precision of ∼92% for the 2013 data and ∼94% for the 2018 data across technology classes. Scene-level mixture modeling indicates a reduction in the HPS-to-LED brightness ratio from 1.15 (2013) to 0.27 (2018), demonstrating the capability of longitudinal HSI for evaluating urban lighting policy outcomes. Full article

Hyperspectral imaging in the long-wave infrared (LWIR) range enables identification of chemical compositions and material properties, but reconstructing 3D models from gimballed pushbroom sensors remains challenging because their unique acquisition geometry is incompatible with conventional photogrammetric software designed for frame cameras. This study presents a workflow for creating explorable 3D models from multi-angle LWIR hyperspectral imagery by co-registering hyperspectral line-scan data with simultaneously acquired RGB frame camera imagery using deep learning-based image matching. The co-registered images are processed in commercial photogrammetric software (Agisoft Metashape), and a texture-to-image mapping algorithm preserves correspondences between 3D model coordinates and original hyperspectral pixels across multiple viewing angles. Quantitative evaluation against reference data demonstrates that co-registration reduces geometric error approaching the accuracy of models built from high-resolution RGB imagery. The resulting models enable the retrieval of 8–50 spectral signatures per surface point, captured from different viewing geometries. This approach facilitates interactive exploration of angular variations in thermal infrared spectra, supporting material identification for non-Lambertian surfaces where single-angle observations may be insufficient for reliable classification. Full article

Background/Objectives: Lymph node dissection during surgeries for cervical and endometrial cancer is associated with significant complications and morbidity. Sentinel lymph nodes (SLNs) mapping using indocyanine green (ICG) has become a promising method for reducing surgical invasiveness and improving patient outcomes. However, the optimal protocol for intraoperative fluorescence mapping of SLNs using ICG, especially regarding the timing of imaging after injection, remains to be fully optimized. This study aimed to evaluate the efficacy of real-time near-infrared (NIR) fluorescence SLN mapping at various time intervals and to investigate the photophysical properties of ICG in human lymph to establish a correlation between fluorescence signals and dye concentration. Methods: A prospective study included 20 patients with cervical and endometrial cancer undergoing laparoscopic or laparotomic surgery. Interstitial ICG injection was administered into the cervical stroma. SLN mapping was conducted using the novel VENERA-green endoscopic system (λ_exc = 800 nm, registration of fluorescence in the range of 830–1000 nm). Spectral fluorescence analysis (λ_exc = 650 nm) was conducted on SLNs and optical phantoms containing human lymph with ICG concentrations from 0 to 40 mg/L. The method made it possible to evaluate ICG absorption/emission properties, as well as to quantify concentration-dependent effects. Results: SLNs were successfully detected in all patients. The average detection time was 15 min with a range of 10 to 25 min. Fluorescence intensity of SLNs was significantly higher 10–15 min after ICG injection compared to 20–25 min. Spectral analysis indicated an absorption peak at 804 nm and an emission peak in the 835–855 nm range for ICG in human lymph. A concentration-dependent redshift of the fluorescence peak was observed and accurately modeled using a logarithmic function (R² = 0.99), which allows for the estimation of ICG concentration in tissue. The bilateral detection rate was 77% for laparoscopy and 100% for laparotomy. Metastases were histologically confirmed in only 2.8% (1/36) of the detected SLNs. Conclusions: Intraoperative NIR fluorescence imaging using ICG is a highly sensitive method for real-time SLN mapping in gynecologic oncology. The optimal detection period is 10 to 15 min after cervical injection to achieve maximum ICG fluorescence intensity, compared to 20 to 25 min. The concentration-dependent fluorescence and absorption properties of ICG in lymph provide the basis for the development of quantitative intraoperative monitoring methods that could improve the accuracy of sentinel lymph node biopsy. Full article

Hyperspectral reconstruction (SR) refers to the computational process of generating high-dimensional hyperspectral images (HSI) from low-dimensional observations. However, the superior performance of most supervised learning-based reconstruction algorithms is predicated on the availability of fully labeled three-dimensional data. In practice, this requirement demands complex optical paths with dual high-precision registrations and stringent calibration. To address this gap, we extend the fully supervised paradigm to a semi-supervised setting and propose SSHSR, a semi-supervised SR method for scenarios with limited spectral annotations. The core idea is to leverage spectrally aware mini-patches (SA-MP) as guidance and form region-level supervision from averaged spectra, so it can learn high-quality reconstruction without dense pixel-wise labels over the entire image. To improve reconstruction accuracy, we replace the conventional fixed-form Tikhonov physical layer with an optimizable version, which is then jointly trained with the deep network in an end-to-end manner. This enables the collaborative optimization of physical constraints and data-driven learning, thereby explicitly introducing learnable physical priors into the network. We also adopt a reconstruction network that combines spectral attention with spatial attention to strengthen spectral–spatial feature fusion and recover fine spectral details. Experimental results demonstrate that SSHSR outperforms existing state-of-the-art (SOTA) methods on several publicly available benchmark datasets, as well as on remote sensing and real-world scene data. On the GDFC remote sensing dataset, our method yields a 6.8% gain in PSNR and a 22.1% reduction in SAM. Furthermore, on our self-collected real-world scene dataset, our SSHSR achieves a 6.0% improvement in PSNR and a 11.9% decrease in SAM, confirming its effectiveness under practical conditions. Additionally, the model has only 1.59 M parameters, which makes it more lightweight than MST++ (1.62 M). This reduction in parameters lowers the deployment threshold while maintaining performance advantages, demonstrating its feasibility and practical value for real-world applications. Full article

This study evaluates the effectiveness of integrating a Residual Shifting (ResShift)-based deep learning super-resolution (SR) technique with the Robust Dense Feature Matching (RoMa) algorithm for high-precision inter-satellite image registration. The key findings of this research are as follows: (1) Enhancement of Structural Details: Quadrupling image resolution via the ResShift SR model significantly improved the distinctness of edges and corners, leading to superior feature matching performance compared to original resolution data. (2) Superiority of Dense Matching: The RoMa model consistently delivered overwhelming results, maintaining a minimum of 2300 correct matches (NCM) across all datasets, which substantially outperformed existing sparse matching models such as SuperPoint + LightGlue (SPLG) (minimum 177 NCM) and SuperPoint + SuperGlue (SPSG). (3) Seasonal Robustness: The proposed framework demonstrated exceptional stability, maintaining registration errors below 0.5 pixels even in challenging summer–winter image pairs affected by cloud cover and spectral variations. (4) Geospatial Reliability: Integration of SR-derived homography with RoMa achieved a significant reduction in geographic distance errors, confirming the robustness of the dense matching paradigm for multi-sensor and multi-temporal satellite data fusion. These findings validate that the synergy between diffusion-based SR and dense feature matching provides a robust technological foundation for autonomous, high-precision satellite image registration. Full article

Real-time monitoring of hazardous gas emissions in open environments remains a critical challenge. Conventional spectrometers and filter wheel systems acquire spectral and spatial information sequentially, which limits their ability to capture multiple gas species and dynamic dispersion patterns rapidly. A High-Resolution Snapshot Multispectral Imaging System (HRSMIS) is proposed to integrate high spatial fidelity with multispectral capability for near real-time plume visualization, gas species identification, and concentration retrieval. Operating across the 7–14 $\mathsf{\mu }$m spectral range, the system employs a dual-path optical configuration in which a high-resolution imaging path and a multispectral snapshot path share a common telescope, allowing for the simultaneous acquisition of fine two-dimensional spatial morphology and comprehensive spectral fingerprint information. Within the multispectral path, two $5\times 5$ microlens arrays (MLAs) combined with a corresponding narrowband filter array generate 25 distinct spectral channels, allowing concurrent detection of up to 25 gas species in a single snapshot. The high-resolution imaging path provides detailed spatial information, facilitating spatio-spectral super-resolution fusion for multispectral data without complex image registration. The HRSMIS demonstrates modulation transfer function (MTF) values of at least 0.40 in the high-resolution channel and 0.29 in the multispectral channel. Monte Carlo tolerance analysis confirms imaging stability, enabling the real-time visualization of gas plumes and the accurate quantification of dispersion dynamics and temporal concentration variations. Full article

Traditionally, sea fog detection technologies have relied primarily on in situ observations. However, point-based observations suffer from limitations in extensive monitoring in marine environments due to the scarcity of observation stations and the limited nature of measurement data. Satellites effectively address these issues by covering vast areas and operating across multiple spectral channels, enabling precise detection and monitoring of sea fog. Despite the increasing adoption of deep learning in this field, achieving further improvements in accuracy and reliability necessitates the simultaneous use of multiple satellite datasets rather than relying on a single source. Therefore, this study aims to achieve higher accuracy and reliability in sea fog detection by employing a deep learning-based advanced co-registration technique for multi-satellite image fusion and autotuning-based optimization of State-of-the-Art (SOTA) semantic segmentation models. We utilized data from the Advanced Meteorological Imager (AMI) sensor on the Geostationary Korea Multi-Purpose Satellite 2A (GK2A) and the GOCI-II sensor on the Geostationary Korea Multi-Purpose Satellite 2B (GK2B). Swin Transformer, Mask2Former, and SegNeXt all demonstrated balanced and excellent performance across overall metrics such as IoU and F1-score. Specifically, Swin Transformer achieved an IoU of 77.24 and an F1-score of 87.16. Notably, multi-satellite fusion significantly improved the Recall score compared to the single AMI product, increasing from 88.78 to 92.01, thereby effectively mitigating the omission of disaster information. Ultimately, comparisons with the officially operational GK2A AMI Fog and GK2B GOCI-II Marine Fog (MF) products revealed that our deep learning approach was superior to both existing operational products. Full article