Search Results (11,166)

Semantic segmentation of high-resolution remote sensing imagery is a critical technology for the intelligent interpretation of sensor data, supporting automated environmental monitoring and urban sensing systems. However, processing data from dense urban scenarios remains challenging due to sensor signal occlusions (e.g., shadows) and the complexity of parsing multi-scale targets from optical sensors. Existing approaches often exhibit a trade-off between the accuracy of global semantic modeling and the precision of complex boundary recognition. While the Segment Anything Model (SAM) offers powerful zero-shot structural priors, its direct application to remote sensing is hindered by domain gaps and the lack of inherent semantic categorization. To address these limitations, we propose a dual-branch cooperative network, PriorSAM-DBNet. The main branch employs a Densely Connected Swin (DC-Swin) Transformer to capture cross-scale global features via a hierarchical shifted window attention mechanism. The auxiliary branch leverages SAM’s zero-shot capability to exploit structural universality, generating object-boundary masks as robust signal priors while bypassing semantic domain shifts. Crucially, we introduce a parameter-efficient Scaled Subsampling Projection (SSP) module that employs a weight-sharing mechanism to align cross-modal features, freezing the massive SAM backbone to ensure computational viability for practical sensor applications. Furthermore, a novel Attentive Cross-Modal Fusion (ACMF) module is designed to dynamically resolve semantic ambiguities by calibrating the global context with local structural priors. Extensive experiments on the ISPRS Vaihingen, Potsdam, and LoveDA-Urban datasets demonstrate that PriorSAM-DBNet outperforms state-of-the-art approaches. By fine-tuning only 0.91 million parameters in the auxiliary branch, our method achieves mIoU scores of 82.50%, 85.59%, and 53.36%, respectively. The proposed framework offers a scalable, high-precision solution for remote sensing semantic segmentation, particularly effective for disaster emergency response where rapid feature recognition from sensor streams is paramount. Full article

Modifications in leaf architecture disrupt optical properties and internal light-scattering dynamics. Accurate modeling of leaf-scale light scattering is therefore essential not only for understanding how disease affects the availability of light for chlorophyll absorption, but also for evaluating its potential as an early optical marker for plant disease detection prior to visible symptom development. Conventional ray-tracing and radiative-transfer models rely on high-frequency approximations and thus fail to capture diffraction and coherent multiple-scattering effects when internal leaf structures are comparable to optical wavelengths. To overcome these limitations, we present a GPU-accelerated finite-difference time-domain (FDTD) framework for full-wave simulation of light propagation within plant leaves, using anatomically realistic dicot and monocot leaf cross-section geometries. Microscopic images acquired from publicly available sources were segmented into distinct tissue regions and assigned wavelength-dependent complex refractive indices to construct realistic electromagnetic models. The proposed FDTD framework successfully reproduced characteristic reflectance and transmittance spectra of healthy leaves across the visible and near-infrared (NIR) ranges. Quantitative agreement between the FDTD-computed spectral reflectance and transmittance and those predicted by the reference PROSPECT leaf optical model was evaluated using Lin’s concordance correlation coefficient. Higher concordance was observed for dicot leaves ($C_b=0.90$) than for monocot leaves ($C_b=0.79$), indicating a stronger agreement for anatomically complex dicot structures. Furthermore, simulations mimicking an early-stage fungal infection in a dicot leaf—modeled by the geometric introduction of melanized hyphae penetrating the cuticle and upper epidermis—revealed a pronounced reduction in visible green reflectance and a strong suppression of the NIR reflectance plateau. These trends are consistent with experimental observations reported in previous studies. Overall, this proof-of-concept study represents the first full-wave FDTD-based optical modeling of internal light scattering in plant leaves. The proposed framework enables direct electromagnetic analysis of pre- and post-penetration light-scattering dynamics during early fungal infection and establishes a foundation for exploiting leaf-scale light scattering as a next-generation, pre-symptomatic diagnostic indicator for plant fungal diseases. Full article

Module-level free-space optical interconnects require actuators to combine both large stroke and high stability. To address this core trade-off that plagues traditional folded-beam actuators, we have developed a millimeter-scale MEMS electromagnetic actuator integrating a Differential Motion Rejection (DMR) unit with a rigid frame. Its performance was systematically evaluated through magnetic–structural coupling modeling, finite element simulation, and experiments. The actuator achieved millimeter-scale stroke under sinusoidal drive, with a primary resonant frequency of approximately 31 Hz. The introduction of the DMR and frame proved highly effective: the out-of-plane displacement at resonance was reduced by about 97%, the static Z-direction stiffness increased by over 50 times, and the displacement crosstalk decreased to 0.265%. Optical testing yielded a stable deflection angle of approximately ±21°. These results demonstrate that this design successfully combines large stroke with high stability, significantly suppressing out-of-plane parasitic motion and crosstalk, making it suitable for module-level optical interconnect systems with stringent space and stability requirements. Full article

Accurate estimation of above-ground biomass (AGB) is vital for carbon accounting, biodiversity conservation, and sustainable forest management, especially in tropical regions under strong anthropogenic pressure. This study estimated and mapped AGB in the Atacora Mountain Chain, Togo, using a multi-source remote sensing approach within Google Earth Engine (GEE). Field data from 421 plots of the 2021 National Forest Inventory were combined with Sentinel-1 Synthetic Aperture Radar, Sentinel-2 multispectral imagery, bioclimatic variables from WorldClim, and topographic data. A Random Forest regression model evaluated the predictive capacity of different variable combinations. The best model, integrating SAR, optical, and climatic variables (S1S2allBio), achieved R² = 0.90, MAE = 13.42 Mg/ha, and RMSE = 22.54 Mg/ha, outperforming models without climate data. Dense forests stored the highest biomass (124.2 Mg/ha), while tree/shrub savannas had the lowest (25.38 Mg/ha). Spatially, ~60% of the area had biomass ≤ 50 Mg/ha. Precipitation correlated positively with AGB (r = 0.55), whereas temperature showed negative correlations. This work demonstrates the effectiveness of integrating multi-sensor satellite data with climatic predictors for accurate biomass mapping in complex tropical landscapes. The approach supports national forest monitoring, REDD+ programs, and ecosystem restoration, contributing to SDGs 13, 15, and 12 and offering a scalable method for other tropical regions. Full article

Direct retrieval of Total Phosphorus (TP) from remote sensing is not possible because TP is not optically active. Unlike optically active parameters, TP does not exhibit spectral signals and relies on indirect correlations with Optically Active Constituents (OACs) such as Chl-a and suspended solids. Existing approaches often rely solely on spectral reflectance while neglecting the environmental variables, such as temperature, that can affect the correlations between OACs such as Chl-a and temperature. To address this, this study integrates satellite-derived Land Surface Temperature (LST) with Landsat 8/9 spectral features, utilizing LST as a spatial proxy for the aquatic thermodynamic environment. Focusing on the Dongjiang River, a subtropical river in China, a machine learning framework was constructed based on in situ measurements collected from 2020 to 2023. Feature selection using Pearson’s correlation and Random Forest importance identified the optimal combination of spectral bands and thermal inputs. The results from the model revealed the following: (1) annual mean TP concentrations in the delta were higher than in the main channel, with more pronounced seasonal fluctuations; (2) statistical verification (Wilcoxon signed-rank test, p < 0.01) confirmed that incorporating LST yielded a certain reduction in retrieval error compared to the spectral-only model; (3) the most influential predictors for TP estimation were a combination of the blue, green, and red spectral bands along with LST; (4) models incorporating LST achieved significantly higher accuracy than those based solely on spectral reflectance, with improved R² and RMSE values across most TP concentration ranges (except for 0.04–0.06 mg/L). These findings demonstrate that integrating LST with spectral features enhances the accuracy of remote sensing-based TP retrieval in rivers, offering new opportunities for improved large-scale water quality monitoring. Full article

Objectives: This study presents the development of a bilingual, expert-evaluated question–answer (Q&A) dataset, named PMR-Q&A, designed for training large language models (LLMs) in the field of Physical Medicine and Rehabilitation (PMR). Methods: The dataset was created through a systematic and semi-automated framework that converts unstructured scientific texts into structured Q&A pairs. Source materials included eight core reference books, 2310 academic publications, and 323 theses covering 15 disease categories commonly encountered in PMR clinical practice. Texts were digitized using layout-aware optical character recognition (OCR), semantically segmented, and distilled through a two-pass LLM strategy employing GPT-4.1 and GPT-4.1-mini models. Results: The resulting dataset consists of 143,712 bilingual Q&A pairs, each annotated with metadata including disease category, reference source, and keywords. A representative subset of 3000 Q&A pairs was extracted for expert validation to evaluate the dataset’s reliability and representativeness. Statistical analyses showed that the validation sample accurately reflected the thematic and linguistic structure of the full dataset, with an average score of 1.90. Conclusions: The PMR-Q&A dataset is a structured and expert-evaluated resource for developing and fine-tuning domain-specific large language models, supporting research and educational efforts in the field of physical medicine and rehabilitation. Full article

This study systematically compares three unsupervised segmentation algorithms (Simple Linear Iterative Clustering (SLIC), Felzenszwalb’s graph-based method, and the Watershed algorithm) in combination with two classification approaches: Random Forest using histogram-based features and Convolutional Neural Networks (CNNs). The study employs Bayesian optimization to jointly tune segmentation parameters and model hyperparameters, investigating how segmentation quality impacts downstream classification performance. The methodology is validated using light optical microscopy images of a high-strength steel sample, with performance evaluated through stratified cross-validation and independent test sets. The findings demonstrate the critical importance of segmentation algorithm selection and provide insights into the trade-offs between feature-engineered and end-to-end learning approaches for microstructure analysis. Full article

The remote monitoring and quantification of industrial gas emissions, such as sulfur dioxide (SO₂), are critical for environmental protection. This research demonstrates an integrated methodology for estimating SO₂ emission rates (kg/s) from an industrial chimney using passive Fourier transform infrared (FTIR) spectroscopy combined with atmospheric dispersion modeling. Infrared spectra were acquired at a stand-off distance of 570 m within the 7–14 $\mathsf{\mu }$m spectral range at a resolution of 4 cm⁻¹. Path-integrated SO₂ concentrations were determined through cross-sectional scanning of the gas plume. To translate these optical measurements into an emission rate, the atmospheric dispersion of the plume was modeled using the Pasquill–Briggs approach, incorporating source parameters and meteorological data. Over two experimental series, the calculated average SO₂ emission rates were 15 kg/s and 22 kg/s. While passive FTIR spectroscopy has long been applied to remote gas detection, this work demonstrates a consolidated framework for retrieving industrial emission rates from stand-off, line-integrated measurements under real industrial conditions. The proposed approach fills a niche between local in-stack measurements and large-scale remote sensing systems, which contributes to the development of flexible ways to monitor industrial emissions. Full article

Due to increasing human activities underwater, there is a growing demand for high-speed underwater optical communication (UOWC) data links for security surveillance, environmental monitoring, pipeline inspection, and other applications. Line-of-sight communication is impossible under certain conditions due to misalignment, physical obstructions, irregular usage, and difficulty adjusting the receiver orientation, especially when used in environments with mobile users or submerged sensor networks. Therefore, non-line-of-sight (NLOS) optical communication is used in this study. Advanced modulation schemes—quadrature amplitude modulation and orthogonal frequency-division multiplexing (QAM-OFDM)—were used to transmit the signal underwater between two network nodes. QAM increases the data transfer rate, while OFDM reduces dispersion and inter-symbol interference (ISI). The proposed UOWC system is investigated using a 532 nm green laser diode (LD). Reliable high-speed data transmission of up to 15 Gbps is achieved over horizontal distances of 134 m, 43 m, 21 m, and 5 m in four different aquatic environments—pure water (PW), clear ocean (CLO), coastal ocean (COO), and harbor II (HarII), respectively. The system achieves effectively error-free performance within the simulation duration (BER < 10⁻⁹), with a received optical signal power of approximately −41.5 dBm. Clear constellation patterns and low BER values are observed, confirming the robustness of the proposed architecture. Despite the limitations imposed by non-line-of-sight (NLOS) communication and the diversity aquatic environments, our proposed architecture excels at underwater long-distance data transmission at high speeds. Full article

We report the use of σ-alkynyl d⁶ electron-rich transition metal complexes as electron-releasing end-groups in octupolar molecules designed for nonlinear optical (NLO) applications, specifically, N,N′,N″-triarylisocyanurates (5,7,8,10,12) and 1,3,5-triarylbenzenes (6,9,11) functionalized by Fe(II) and Ru(II) organometallic moieties, and their NLO properties, as assessed by hyper-Rayleigh scattering (HRS) and Z-scan. The redox properties are briefly investigated through isolation of the corresponding Fe(III) trications 5[PF₆]₃ and 6[PF₆]₃. The second-harmonic generation (SHG) or two-photon absorption (2PA) performance of the Fe(II) and Ru(II) parents is compared with the help of TD-DFT calculations performed on models. Comparison with tris-ferrocenyl isocyanurate 4 reveals that the σ-connection of the metallic centers to the π-manifold is superior to the η⁵-connection for enhancing NLO properties. The positive effect of organometallic end-groups on NLO properties relative to purely organic electron-releasing substituents is established. The mechanism by which NLO enhancement occurs is complex and possibly connected to the polarizable π-electrons in the ligands surrounding the metal alkynyl units, but in most cases, the observed NLO enhancement must arise from the transition metal centers interacting with the central π-manifold. Full article

Accurately reconstructing high-resolution (HR) images remains challenging in scenarios where HR observations cannot be captured due to optical, hardware, or cost constraints. To address this limitation, we introduce an image super-resolution (SR) framework that reconstructs HR content solely from multiple low-resolution (LR) measurements, without relying on any HR reference images. The proposed method formulates a unified degradation model that describes how HR pixels contribute to LR observations under subpixel shifts and anisotropic downsampling. Based on this model, we develop an adaptive search algorithm capable of identifying the minimal and most informative combination of LR images required to equivalently represent the latent HR image. The selected LR images are then used to construct a solvable linear system whose solution directly yields the HR pixel values. Experiments conducted on the USAF 1951 resolution target demonstrate that the proposed approach improves Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) by 27.33% and 44.64%, respectively, achieving a resolvable spatial frequency of 228 line pairs per millimeter. In semiconductor chip inspection, PSNR and SSIM increase by 22.36% and 40.38%. These results verify that the proposed LR-combination-based strategy provides a physically interpretable and highly practical alternative for applications in which HR reference images cannot be obtained. Full article

Laser local forming is an effective method for reshaping optical glass, yet the deformation of the material during the cooling phase remains poorly understood. This study investigates the dynamic evolution of the molten pool, specifically focusing on the transition from an initial convex shape to a final “M-shaped” profile. A combined approach using thermal-fluid simulation and high-speed imaging experiments was employed to track the surface changes throughout the heating and cooling cycles. The results show that while the surface bulges outward during laser irradiation, the material redistributes after the laser is switched off due to non-uniform cooling and volumetric shrinkage. The specific roles of viscosity and surface tension in driving this reverse flow were identified. Furthermore, the study established a quantitative model linking laser parameters to the final surface dimensions, providing a reliable tool for predicting and controlling the precision of glass forming. Full article

Accurate estimation of tree biomass and volume is essential for sustainable forest management, climate change mitigation, and ecosystem service assessment. Recent advances in unmanned aerial vehicle (UAV) technology enable the acquisition of ultra-high-resolution optical and three-dimensional data, providing a resource-efficient alternative to traditional field-based inventories. This review synthesizes 181 peer-reviewed studies on UAV-based estimation of tree biomass and volume across forestry, agricultural, and urban ecosystems, integrating bibliometric analysis with qualitative literature review. The results reveal a clear methodological shift from early structure-from-motion photogrammetry toward integrated frameworks combining three-dimensional canopy metrics, multispectral or LiDAR data, and machine learning or deep learning models. Across applications, tree height, crown geometry, and canopy volume consistently emerge as the most robust predictors of biomass and volume, enabling accurate individual-tree and plot-level estimates while substantially reducing field effort and ecological disturbance. UAV-based approaches demonstrate particularly strong performance in orchards, plantation forests, and urban environments, and increasing applicability in complex systems such as mangroves and mixed forests. Despite significant progress, key challenges remain, including limited methodological standardization, insufficient uncertainty quantification, scaling constraints beyond local extents, and the underrepresentation of biodiversity-rich and structurally complex ecosystems. Addressing these gaps is critical for the operational integration of UAV-derived biomass and volume estimates into sustainable land management, carbon accounting, and climate-resilient monitoring frameworks. Full article

Accurate river network mapping is essential for hydrological modeling, flood risk assessment, and watershed environment management. However, conventional methods based on either optical imagery or digital elevation models (DEMs) often suffer from river network discontinuity and poor representation of morphologically complex rivers. To overcome this limitation, this study proposes a novel method integrating the river-oriented Gradient Boosting Tree model (RGBT) and adaptive stream burning algorithm for high-precision and topologically consistent river network extraction. Water-oriented multispectral indices and multi-scale linear geometric features are first fused and input for a river-oriented Gradient Boosting Tree model to generate river probability maps. A direction-constrained region growing strategy is then applied to derive spatially coherent river vectors. These vectors are finally integrated into a spatially adaptive stream burning algorithm to construct a conditional DEM for hydrological coherent river network extraction. We select eight representative regions with diverse topographical characteristics to evaluate the performance of our method. Quantitative comparisons against reference networks and mainstream hydrographic products demonstrate that the method achieves the highest positional accuracy and network continuity, with errors mainly focused within a 0–40 m range. Significant improvements are primarily for narrow tributaries, highly meandering rivers, and braided channels. The experiments demonstrate that the proposed method provides a reliable solution for high-resolution river network mapping in complex environments. Full article

Within the traditional electronic neural network framework, Generative Adversarial Networks (GANs) have achieved extensive applications across multiple domains, including image synthesis, style transfer and data augmentation. Recently, several studies have explored the use of optical neural networks represented by the diffractive deep neural network (D²NN) for GANs. However, most of these focus on applications of the generative network, and there is currently no well-established D²NN architecture that simultaneously implements generative adversarial functionality. Here, we propose a novel implementation scheme for generative adversarial networks based on all-optical diffraction layers, demonstrating a complete all-optical adversarial architecture that simultaneously realizes both the generative network and the adversarial network (D²NN-GAN). We validated this method on the MNIST handwritten digit dataset, achieving Nash equilibrium convergence with the discriminator accuracy stabilizing around 50%. Concurrently, the average SSIM parameter of generated images reached 0.9573, indicating that the generated samples possess high quality and closely resemble real samples. Furthermore, we extended the framework to the KTH human action dataset, successfully reconstructing the “running” action with a discriminator accuracy of approximately 75%. The D²NN-GAN architecture introduces a fully optical generative adversarial model, providing a practical path for future optical modeling methods, such as image generation and video synthesis. Full article