Search Results (1,269)

Laser beam combining is essential for achieving high-power and high-radiance output. However, atmospheric turbulence induces independent tip–tilt aberrations across discrete sub-beams in laser array systems, which severely degrades the concentration of far-field energy. Traditional wavefront sensing techniques are primarily designed for the continuous wavefront of a single laser and are not directly applicable to laser array, whereas indirect optimization-based methods often suffer from slow convergence and limited real-time performance. To address these limitations, this study introduces a tip–tilt aberration compensation system for laser array propagation based on cooperative beacons with a shared-aperture transmit–receive configuration. The primary innovation consists of a modified Shack–Hartmann wavefront sensor (SHWFS) tailored to a discrete multi-beam layout, which facilitates the direct, independent, and simultaneous measurement of tip–tilt aberrations for each sub-beam. In conjunction with a segmented deformable mirror (SDM), the architecture can facilitate real-time closed-loop correction with high bandwidth and high precision. Numerical simulations of a 7-, 19-, and 37-beam laser array, together with validation experiments utilizing a 30-beam configuration, demonstrate that the proposed approach effectively suppresses tip–tilt error induced by turbulence. After closed-loop correction, the Strehl ratio (SR) increases above 0.92 ($r_0=5\mathrm{cm}$), while the beam quality factor β reduces below 1.37 ($r_0=5\mathrm{cm}$). Furthermore, the system retains performance stability as the number of sub-beams increases, demonstrating the scalability of the proposed method. In contrast to conventional approaches designed for a continuous wavefront, the proposed method offers a feasible approach for a discrete laser array system, providing robust and scalable tip–tilt correction under varying atmospheric conditions. Full article

A Principal Component-Based Spectral Standardization (PCSS) method was developed to standardize hyperspectral radiance spectra onto a fixed wavelength grid. This enables the direct comparison of radiance or reflectance spectra across different spatial pixels of an imaging spectrometer or between different instruments. The method was validated using simulated Climate Absolute Radiance and Refractivity Observatory (CLARREO) Pathfinder (CPF) spectra. The PCSS approach demonstrated high accuracy: the average root-mean-square uncertainty across all CPF channels remained below 0.07%, with maximum individual-channel uncertainties under 1%. Compared to methods based on spectral interpolation, PCSS produced significantly lower biases with tighter error distributions, particularly in spectrally rich regions. Measured Hyper Spectral Imager for Climate Science (HySICS) balloon data provided further validation. PCSS successfully estimated wavelength shifts that closely matched measured data, even when utilizing approximated Jacobians, demonstrating the method’s robustness. Because it relies on a pre-computed lookup table for model parameters, PCSS bypasses the need for intensive radiative transfer calculations, making it highly computationally efficient. Beyond CPF, this method can easily be adapted for other hyperspectral sensors by substituting their respective wavelength grids and instrument line shape functions, offering a powerful tool to improve cross-calibration between different satellite sensors. Full article

3D scene reconstruction serves as a fundamental technology with widespread applications in virtual reality, structural inspection, and robotic systems. While recent advances in 3D Gaussian Splatting have significantly enhanced scene reconstruction capabilities, the performance of such methods remains suboptimal when applied to highly reflective environments. To overcome this limitation, we introduce ARS-GS, a novel framework that integrates Anisotropic Spherical Gaussian reflection modeling and spherical harmonics diffuse approximation into a physically based rendering pipeline. This architecture incorporates a skip connection between the Anisotropic Spherical Gaussian module and the Gaussian primitives, effectively preserving surface details while maintaining computational efficiency. Comprehensive experimental evaluations validate the efficacy of ARS-GS across multiple datasets. Specifically, our method establishes new state-of-the-art quantitative benchmarks, achieving a peak signal-to-noise ratio of 38.30 and a structural similarity index measure of 0.997 on the neural radiance fields synthetic dataset, alongside a peak signal-to-noise ratio of 46.31 on the Gloss Blender dataset. Furthermore, on the challenging reflective neural radiance fields real-world dataset, our approach secures the highest peak signal-to-noise ratio scores, highlighted by a metric of 26.26 on the Sedan scene. The proposed method also substantially reduces perceptual errors, yielding a learned perceptual image patch similarity as low as 0.204, thereby consistently outperforming existing techniques in the reconstruction of highly specular surfaces with superior geometric fidelity. Full article

Atmospheric temperature and humidity profiles are essential for numerical weather prediction and severe weather monitoring. To effectively utilize data from the Geostationary Interferometric Infrared Sounder (GIIRS) onboard the FY-4 satellite, this study proposes a retrieval method based on a radial basis function (RBF) neural network, which integrates numerical model background profiles with GIIRS simulated radiance errors to construct a mapping from these two inputs to background profile errors. A channel selection strategy is developed using correlations between background errors and radiance errors to identify channels sensitive to temperature and humidity variations at different pressure levels. Experiments are conducted using data from land stations in Zhejiang Province, China, from August to December 2024, including 829 clear-sky and 2109 cloudy profiles. Under clear-sky conditions, the method reduces temperature and humidity root mean square error (RMSE) by approximately 39% and 22.3% compared to background profiles. Under cloudy conditions, despite severe radiation interference, RMSE reductions of 38.5% for temperature and 15.3% for humidity are achieved, with notable improvements below 900 hPa and above 750 hPa for humidity. Compared with the multilayer perceptron (MLP) method, RBF shows superior performance under all test conditions, especially in cloudy-sky humidity retrieval. The proposed approach provides an effective, physically constrained framework for operational GIIRS data application in temperature and humidity retrieval. Full article

The Pushbroom Imager for Cloud and Aerosol Research and Development (PICARD) visible through shortwave infrared imaging spectrometer was developed to carry a calibration laboratory environment to high altitudes, while also providing high-dynamic-range bright cloud-top radiance measurements across a field of view just under 50 degrees. The in-flight performance of this new spectroradiometer was validated in comparison to multiple reference data sources and targets using imagery collected aboard NASA’s ER-2 high-altitude aircraft during the Western Diversity Time Series (WDTS) airborne science campaign in April 2023 and the September 2024 Plankton, Aerosol, Cloud, and ocean Ecosystem (PACE) Postlaunch Airborne eXperiment (PACE-PAX), both operating out of southern California. PICARD measurements from flights over Railroad Valley Playa, Nevada, USA, were compared to high-resolution radiance spectra of the dry lakebed provided by the Radiometric Calibration Network (RadCalNet) Working Group. Direct comparison to satellite cloud radiometry was enabled by the ER-2 flying in coordination with simultaneous overpasses of the Terra, Aqua, and NOAA-20 Earth-observing satellites during WDTS and with the PACE observatory during PACE-PAX. To account for large spectral differences between incandescent laboratory sources and solar illumination, PICARD calibration relies on measurements using the Goddard Laser for Absolute Measurements of Radiance (GLAMR) to characterize and minimize spectral stray light from the instrument’s twin Offner grating spectrometers. Good agreement in comparison to reference measurements demonstrates PICARD’s ability to provide imagery for environmental science or for testing new sensor designs and retrieval algorithms for cloud and aerosol research with verified laboratory calibrations at high altitudes. Full article

We analyze the evolution of atmospheric and surface physical properties over the region of the Earth observed by the Meteosat Second Generation (MSG) satellites during the period 2005–2024. Long-term changes are detected in the observed radiances, with a decrease in the solar domain (−1.3%) and an increase in the thermal infrared domain (+0.4%), consistent with trends reported by independent broadband radiometers such as CERES. The outgoing solar radiance (OSR) exhibits a marked decline, which we associate with a reduction in low-level cloud cover within the nominal Meteosat field of view (MFoV) centered at 0° longitude. Changes in atmospheric CO₂ concentration also contribute to the observed radiative imbalance at the top of the atmosphere (TOA). Instrument calibration stability and inter-satellite homogenization across the MSG series are explicitly addressed, enabling the detection of robust interdecadal signals. By subdividing the MFoV into 60 regional sectors, we characterize spatial variations in cloud amount at low and high atmospheric levels and relate these changes to regional TOA radiative imbalances and concurrent variations in Atlantic sea surface temperature (SSTs). The spectral information provided by SEVIRI allows a more detailed attribution of radiative changes than broadband observations alone from other instruments. In particular, radiances measured in the atmospheric split-window region near 11 µm are shown to be sensitive to variations in low-tropospheric humidity, which exhibits a widespread decadal-scale increase. The results indicate a close coupling between cloud-cover changes, radiative fluxes, and SST evolution on the recent interdecadal time scale. The observed decrease in low-level total cloud cover is independently in line with ECMWF ERA5 reanalysis data. These findings highlight the value of long, stable geostationary observations for investigating atmosphere–ocean interactions and their role in regional climate variability. Full article

During the adaptive reuse of industrial heritage buildings, existing opening systems and envelope performance often pose major constraints. These restrictions make it difficult for the building to meet the requirements of the updated indoor environment, resulting in insufficient daylight and increased energy consumption. Therefore, optimizing lighting and energy performance has become the primary goal of the retrofit design. However, with limited interventions, the retrofit of heritage buildings to achieve significant overall performance improvement is still a challenge. From a sustainability perspective, improving daylight utilization and reducing energy demand are essential strategies for achieving low-carbon and resource-efficient building retrofit. This study proposes a grid-based parametric multi-objective optimization approach to optimize the window openings of the building envelope. The approach defines the position, size and material properties of the roof and facade openings as design variables. Implemented via the Honeybee and Octopus platforms, it integrates a genetic algorithm with EnergyPlus and Radiance simulations to co-optimize daylight performance, circadian frequency, and energy use intensity. Taking a single-story typical industrial heritage building in China’s cold climate zone as a case study, it is shown that coordinated multi-objective constraints significantly improve the overall performance across various evaluation metrics. The optimization results also provide interpretable window configuration strategies and recommended parameter ranges, which fully consider the climate adaptability of the surrounding environment. These findings offer useful guidance for sustainable retrofit design decision-making in similar single-story industrial heritage buildings. Full article

Traditional Chinese paper cutting represents an important intangible cultural heritage. Can artificial intelligence (AI) reactivate the heritage in a new style? The aim of this study was to use AI to reactivate temple arts and paintings by converting them into the style of traditional Chinese paper cuttings. Thirty sets of old images taken 18 years ago and 10 images of ancient paintings from the National Palace Museum were restyled in Nano Banana (Pro)^®. Related design elements included integrated isolated parts, visual depth, details, and solid and void alternation. Three-dimensional stone and wood sculptures were reconstructed using Rodin^® or Meshy^® and converted into AR models in Sketchfab^®. From the generated 2D images and their 3D representations, a reactivated style of Chinese paper cutting was developed that can be interacted with in the AR smartphone platform or RP in the physical world. Approximately 370 images were regenerated, and 167 versions of models were reconstructed. AI should be considered part of culture. Rethinking traditional folk art highlights demand for the cross-reference and cross-reactivation of heterogeneous art forms. This AI model interprets novel 3D structural and visual details and creates a unique 2D and 3D identity for each subject. Full article

Unmanned aerial vehicles (UAVs) provide a flexible platform for surface reflectance measurement at spatial scales between ground observations and satellite remote sensing. This study develops a UAV-based spectroradiometric system for surface reflectance retrieval under natural illumination conditions using non-imaging hyperspectral sensors. The system integrates two stabilized spectroradiometers mounted on a UAV to simultaneously measure hemispherical downwelling irradiance and upwelling surface radiance at flight altitude, enabling reflectance retrieval through a radiance–irradiance ratio framework without relying on ground calibration targets or radiative transfer model inversion. Field experiments were conducted over agricultural plots, and the UAV-derived reflectance was quantitatively validated against ground-based dual-spectroradiometer measurements. The results demonstrate stable irradiance measurements during flight and good agreement between UAV- and ground-derived reflectance across the 400–900 nm spectral range. The proposed system offers a practical and reliable solution for hyperspectral reflectance retrieval using UAV platforms. Full article

With the continued development of spatial information technologies, Digital Surface Models (DSMs) have become fundamental data products for urban planning, virtual reality, geographic information systems, and digital-earth applications. Neural Radiance Fields (NeRFs) have achieved remarkable success in multi-view 3D reconstruction in computer vision. Still, their application to DSM generation from satellite imagery remains challenging because of differences in imaging geometry, complex surface structure, and varying illumination conditions. To address these issues, this paper proposes a Shading and Geometric Constraint (SGC) method tailored to satellite photogrammetry and designed to integrate with existing NeRF-based frameworks such as Sat-NeRF and EO-NeRF. First, a physical imaging model based on Lambertian reflectance and spherical harmonics is introduced to represent the complex illumination variations in satellite images. Synthetic images generated by this model provide auxiliary supervision that improves robustness to illumination inconsistency. Second, inspired by classical shading-based refinement methods, we introduce a bilateral edge-preserving geometric constraint. Unlike standard smoothness terms, this constraint uses photometric discrepancies to weight geometric smoothing, thereby preserving sharp building boundaries while smoothing flat surfaces. We integrate the method into two state-of-the-art baselines, Sat-NeRF and EO-NeRF. EO-NeRF+SGC achieves up to a 57.93% reduction in elevation MAE relative to EO-NeRF, which is the largest relative MAE reduction reported in this study. The method also recovers finer structural details and sharper edges than recently published NeRF-based DSM reconstruction methods. Full article

In recent times, increasing attention has been devoted to the investigation of atmospheric processes through remote sensing in order to improve our understanding of climate dynamics and atmospheric physics. This requires accurate simulation of the spectra emitted by the Earth, from which atmospheric composition and thermodynamic conditions can be retrieved. The FORUM mission focuses on observations of the Earth’s outgoing radiation in the far-infrared spectral region, which has been only sparsely explored due to observational challenges, despite its significant contribution to the characterization of atmospheric processes. As part of the mission activities, dedicated simulations of the measurements expected from the FORUM instrument are required. Different models and codes can be employed for this purpose. Fast radiative transfer models, such as SIGMA-FORUM, efficiently simulate all-sky conditions, whereas detailed line-by-line models, such as KLIMA, have generally been limited to clear-sky applications. In this context, SIGMA-FORUM, an all-sky fast radiative transfer model operating in the 10–2760 cm⁻¹ spectral range and KLIMA, a FORTRAN-based line-by-line algorithm extensively validated under clear-sky conditions, are used to simulate FORUM radiances in both clear and cloudy atmospheres. This study extends the comparison between SIGMA-IASI/F2N and KLIMA to cloudy-sky scenarios by incorporating cloud optical properties into KLIMA using the same parametrization approach adopted in SIGMA-FORUM version 2.4. By combining complementary modeling approaches, this work enables KLIMA to simulate atmospheric radiances under all-sky conditions, thereby broadening its applicability. Full article

Adaptive façade systems are increasingly used to mitigate glare in daylit spaces and improve visual comfort while supporting sustainable daylight utilisation and reduced reliance on electric lighting in buildings. However, their performance is often evaluated using illuminance-based metrics or uncalibrated simulations, limiting the reliability of glare assessment. This study proposes a calibrated experimental–simulation framework for evaluating glare reduction achieved by a kinetic horizontal shading system (KSS) under real daylight conditions. The approach integrates reduced-scale physical measurements with Radiance-based simulations using a digitally reconstructed twin of the experimental setup. Two geometrically identical test chambers positioned side-by-side—a static reference chamber and a kinetic chamber equipped with six adaptive fins (0.63 m real-scale depth)—were investigated using a 1:20 scale mock-up. Internal illuminance measurements were normalised between chambers, and a sky-scaling procedure was applied to calibrate simulated sky luminance distributions against measured data on an hourly basis, enabling photometrically validated HDR renderings for glare evaluation. Glare performance was analysed for three representative clear-sky days during periods of maximum solar exposure (11:00–17:00) under late-summer conditions at approximately 51° N latitude in Wrocław, Poland. Visual comfort was assessed using Daylight Glare Probability (DGP), Daylight Glare Index (DGI), and veiling luminance (L_veil). The kinetic shading system reduced mean DGP from 0.57 to 0.35 (−38%) and peak glare values by nearly half compared with the static configuration, while veiling luminance decreased by 73%, indicating substantial improvement in physiological visual comfort. These results demonstrate that adaptive fin movement effectively suppresses both perceptual and physiological glare during critical daylight hours. The proposed calibrated experimental–simulation workflow provides a robust and transferable methodology for evaluating the glare performance of adaptive façade systems and supports sustainable daylight management by enabling high daylight availability while maintaining acceptable glare levels in buildings. Full article

Accurate weed mapping in cereal fields requires pixel-level segmentation from unmanned aerial vehicle (UAV) imagery that remains reliable across fields, seasons, and illumination. Existing multispectral pipelines often depend on thresholded vegetation indices, which are brittle under radiometric drift and mixed crop–weed pixels, or on single-stream convolutional neural network (CNN) and Transformer backbones that ingest stacked bands and indices, where radiance cues and normalized index cues interfere and reduce sensitivity to small weed clusters embedded in crop canopy. We propose VISA (Vegetation Index and Spectral Attention), a two-stream segmentation network that decouples these cues and fuses them at native resolution. The radiance stream learns from calibrated five-band reflectance using local residual convolutions, channel recalibration, spatial gating, and skip-connected decoding, which preserve fine textures, row boundaries, and small weed structures that are often weakened after ratio-based index compression. The index stream operates on vegetation-index maps with windowed self-attention to model local structure efficiently, state-space layers to propagate field-scale context without quadratic attention cost, and Slot Attention to form stable region descriptors that improve discrimination of sparse weeds under canopy mixing. To support supervised training and deployment-oriented evaluation, we introduce BAWSeg, a four-year UAV multispectral dataset collected over commercial barley paddocks in Western Australia, providing radiometrically calibrated blue, green, red, red edge, and near-infrared orthomosaics, derived vegetation indices, and dense crop, weed, and other labels with leakage-free block splits. On BAWSeg, VISA achieves 75.6% mean Intersection over Union (mIoU) and 63.5% weed Intersection over Union (IoU) with 22.8 M parameters, outperforming a multispectral SegFormer-B1 baseline by 1.2 mIoU and 1.9 weed IoU. Under cross-plot and cross-year protocols, VISA maintains 71.2% and 69.2% mIoU, respectively. The full BAWSeg benchmark dataset, VISA code, trained model weights, and protocol files will be released upon publication. Full article

Agriculture is rapidly becoming a data-driven field where automation relies on transforming 2D images into accurate 3D models. However, selecting the most effective method remains challenging due to the unconstrained nature of the environment. This review assesses the effectiveness of geometry-based, sensor-based, and learning-based reconstruction methodologies in agricultural settings. We analyze photogrammetric pipelines, active sensing, and neural rendering methods based on their geometric accuracy, data processing speed, and field performance against wind or occlusion. Our analysis indicates that while Light Detection and Ranging (LiDAR) is highly accurate, it is too expensive for widespread adoption. Conversely, geometry-based methods are inexpensive but struggle with complex biological structures. Learning-based methods, especially 3D Gaussian Splatting (3DGS), have revolutionized the field by enabling a balance between visual fidelity and real-time inference speed. We conclude that the best chance for scalability and accuracy lies in hybrid pipelines that integrate Vision Foundation Models (VFMs) with geometric priors. We believe that “hybrid intelligence” systems, such as edge-native 3D Gaussian Splatting combined with semantic priors, are the future of 3D reconstruction. These systems will enable the creation of real-time, spatiotemporal (4D) digital twins that drive automated decision-making in precision agriculture. Full article

Aerial remote sensing efficiently surveys large areas, but accurate direct object-level measurement remains difficult in complex natural scenes. Advancements in 3D computer vision, particularly radiance field representations such as NeRF and 3D Gaussian splatting, can improve reconstruction fidelity from posed imagery. Nevertheless, direct aerial measurement of important attributes like tree diameter at breast height (DBH) remains challenging. Trunks in aerial forest scans are distant and sparsely observed in image views; at typical operating altitudes, stems may span only a few pixels. With these constraints, conventional reconstruction methods have inaccurate breast-height trunk geometry. TreeDGS is an aerial image reconstruction method that uses 3D Gaussian splatting as a continuous scene representation for trunk measurement. After SfM–MVS initialization and Gaussian optimization, we extract a dense point set from the Gaussian field using RaDe-GS’s depth-aware cumulative-opacity integration and associate each sample with a multi-view opacity reliability score. Then, we isolate trunk points and estimate DBH using opacity-weighted solid-circle fitting. Evaluated on 10 plots with field-measured DBH, TreeDGS reaches 4.79 cm RMSE (about 2.6 pixels at this GSD) and outperforms a LiDAR baseline (7.66 cm RMSE). This shows that TreeDGS can enable accurate, low-cost aerial DBH measurement. Full article