Search Results (351)

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

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

High-precision 3D reconstruction of non-cooperative space targets is a critical technology for on-orbit servicing (OOS) and situational awareness, driven by the growing number of OOS missions. However, traditional visual algorithms struggle to acquire accurate geometric information due to the unique high-dynamic-range lighting and strong specular reflections characteristic of the space environment. This paper proposes Space-Gaussian, a compact 3D Gaussian reconstruction method tailored for complex lighting environments. Built upon the 3D Gaussian Splatting (3DGS) framework, the method incorporates a physically based rendering pipeline and a microfacet bidirectional reflectance distribution function model. By decoupling geometric structure from material properties and utilizing deferred rendering, it effectively suppresses geometric artifacts and specular highlights arising from non-Lambertian surface reflections. Comparative experiments on a high-fidelity simulation dataset demonstrate that Space-Gaussian outperforms mainstream methods—including Neural Radiance Fields (NeRF), Instant-NGP, GaussianShader, and 3DGS—in geometric reconstruction accuracy, novel view synthesis quality, and real-time rendering. On our self-created dataset, our approach achieves a significant performance boost over existing 3DGS methods. The results highlight its potential for high-fidelity, real-time 3D perception on resource-constrained spacecraft platforms. Full article

Due to phenomena, such as refraction, reflection, and light scattering, the three-dimensional (3D) reconstruction of transparent objects with complex geometric symmetry or contours is confronted with the challenges of insufficient extraction of feature points and recognition of contour detail. To solve this challenge, a novel reconstruction method based on multi-cooperative Neural Radiance Fields (NeRF) is proposed in the paper. This method incorporates angular offset fields and local reconstruction fields, explicitly modeling the effects of refraction and reflection during light propagation. The angular offset field simulates the internal refractive deflection within transparent materials, while the localized reconstruction field performs secondary reconstruction in regions affected by specular reflection. This approach effectively captures surface contours of transparent objects and accurately reconstructs scene details. Experimental results demonstrate that our method achieves approximately 10% improvement in reconstruction accuracy compared to traditional neural radiance field techniques, with a PSNR of 25, an increased SSIM of 0.87, and a reduced LPIPS value of 0.365. The proposed method offers a new perspective for reconstructing transparent objects and scenes containing such materials, holding significant theoretical and practical value. Full article

The SMART-P (Spectral Measurements for Atmospheric Radiative Transfer–Polarimeter) algorithm was developed to retrieve aerosol and ocean parameters from PolCube measurements. The PolCube is a multi-angular polarimeter (MAP) aboard the BusanSat-B CubeSat scheduled for launch in 2026, which measures polarized radiances at 410, 555, 670, and 865 nm from four viewing angles. This study presents the theoretical basis of the algorithm and conducts a sensitivity analysis of aerosol inversions over the ocean processed by SMART-P under the expected measurement conditions for PolCube observations. The results indicate that the degree of linear polarization (DoLP) significantly increases the information content of the real part of the refractive index and of the fine-mode particle-size parameters relative to radiance-only measurements. Enhanced measurement sensitivity enables more accurate retrieval of fine-dominated aerosol properties, such as smoke and sulfate. The sensitivity analysis also shows that the ocean surface reflectivity is the most critical forward-model parameter affecting aerosol-property retrievals. The SMART-P algorithm will support the BusanSat-B mission to understand the role of aerosol particles in the climate system and air quality. Full article

Water quality observations are vital for effectively managing coastal resources and influencing decisions from emergency beach closures to aquaculture leasing agreements. This study focuses on deriving two water quality parameters—Chlorophyll a (Chl-a) and suspended particulate matter (SPM)—through the high-resolution multispectral imager (MSI) onboard the Sentinel 2A&B satellites, specifically for the Ningde coastal region, which is a crucial aquaculture hub in China. Since more than 90% of the signals captured by satellites are affected by atmospheric interference, it is crucial to apply a process called “atmospheric correction” (AC) to isolate the water contribution, known as water leaving reflectance, from the radiance measured at the top of the atmosphere. Our research assesses five published AC models and various algorithms designed to accurately estimate Chl-a and SPM from water leaving reflectance. We determine the most effective combination by comparing these findings against in situ data gathered from eleven locations in the Ningde coastal region (POLYMER-SOLID with lowest metric RMSLE (0.29), and MAE (1.68) and POLYMER-MDN with the lowest metric RMSLE (0.59), and MAE (0.56)). Our study underscores the importance of selecting locally validated AC models and algorithms for generating water quality products, as this enhances the utility of remote sensing data in monitoring water quality. Moreover, we conduct a spatiotemporal analysis of the water quality parameters from 2016 to 2021, revealing significant interannual variability that underlines the need for continuous monitoring and robust data analysis in coastal management efforts. Full article

Developing effective evaluation frameworks for urban regeneration in non-metropolitan areas is increasingly challenging, particularly for small-scale projects where conventional administrative indicators are often insufficient on their own. This study examines 46 regeneration projects in Republic of Korea and integrates nighttime lights (NTL), Sentinel-2 indices, and administrative statistics to identify how different project types produce observable changes. The results show that NTL is effective mainly in economy-based and central commercial area projects, where increases in radiance correspond to the expansion of commercial functions, higher business activity, and stronger evening economic operations. In contrast, NTL shows limited responsiveness in residential-support projects, reflecting the low baseline illumination and weak lighting elasticity of residential environments. For these areas, Sentinel-2 NDVI and NDBI provide clearer evidence of improvements, capturing localized changes in vegetation, built surfaces, and pedestrian environments that are not detectable through nighttime radiance. Comparative assessments indicate that most changes are concentrated within project boundaries, though external development projects occasionally influence spectral patterns in adjacent areas. These findings demonstrate that combining NTL and Sentinel-2 data offers a more context-sensitive approach to evaluating small-scale regeneration and highlights the importance of selecting indicators suited to specific project types. The study provides an empirical foundation for more adaptable, data-driven evaluation frameworks in non-metropolitan regeneration policy. Full article

Synthesizing photo-realistic images of a scene from arbitrary viewpoints and under arbitrary lighting environments is one of the important research topics in computer vision and graphics. In this paper, we propose a method for synthesizing photo-realistic images of a scene with fluorescent objects from novel viewpoints and under novel lighting colors and spectra. In general, fluorescent materials absorb light with certain wavelengths and then emit light with longer wavelengths than the absorbed ones, in contrast to reflective materials, which preserve wavelengths of light. Therefore, we cannot reproduce the colors of fluorescent objects under arbitrary lighting colors by combining conventional view synthesis techniques with the white balance adjustment of the RGB channels. Accordingly, we extend the novel-view synthesis based on the neural radiance fields by incorporating the superposition principle of light; our proposed method captures a sparse set of images of a scene from varying viewpoints and under varying lighting colors or spectra with active lighting systems such as a color display or a multi-spectral light stage and then synthesizes photo-realistic images of the scene without explicitly modeling its geometric and photometric models. We conducted a number of experiments using real images captured with an LCD and confirmed that our method works better than the existing methods. Moreover, we showed that the extension of our method using more than three primary colors with a light stage enables us to reproduce the colors of fluorescent objects under common light sources. Full article

Aesthetic mesotherapy—the subcutaneous injection of key ingredients for cellular function—has gained popularity as a skin rejuvenation treatment. We developed a cosmetic serum, incorporating 11 ingredients frequently used in meso-injections that are partially encapsulated in multilamellar vesicles. We evaluated the ingredients, and their formulation into a topical serum and in mesotherapy injections, for their efficacy at modulating skin rejuvenation in vitro, ex vivo and in vivo. Proteomic profiling of skin explants subjected to a meso-injection identified 47 differentially regulated proteins, whereas topical ingredient applications modulated 149 proteins, predominantly by upregulating them. These proteins mapped to gene ontology pathways relating to ER-Golgi transport, protein trafficking, energy metabolism, integrin signalling, extracellular matrix organisation, and regulation of cell proliferation. The impact of some ingredient classes appeared pathway-specific, while broader responses possibly reflected synergistic interactions. Consistently, topical ingredient application increased ATP levels in reconstructed skin, suggesting enhanced metabolic activity. Clinically, twice-daily serum applications over 63 days yielded improvements in skin smoothness, complexion radiance and complexion homogeneity comparable to those observed after three meso-injections. However, results appeared to vary with age, and the combination of serum application with meso-injection may offer benefits, particularly for skin firmness, acting in combination with mesotherapy to improve skin quality. Full article

Space-based imaging spectrometers that monitor the Earth’s surface generate vast amounts of data, the processing of which requires fast and accurate retrieval algorithms. Estimating scientifically relevant surface properties from remotely measured radiance data typically involves first inferring spectral surface reflectance from the observed radiance, followed by discipline-specific algorithms to derive scientifically relevant properties. Probabilistic reflectance retrieval algorithms, such as the commonly used optimal estimation (OE), are computationally expensive. Furthermore, the Gaussian assumptions associated with OE have not been fully validated in the context of hyperspectral retrievals. To address these challenges, we introduce accelerated optimal estimation (AOE), a Bayesian algorithm that speeds up the OE reflectance inversion process by up to two orders of magnitude compared to a reference OE implementation (ROE), while also providing improved convergence over a number of selected test targets. We also demonstrate that, under given atmospheric conditions, Gaussian uncertainty estimates from OE-type algorithms are accurate. This is achieved by comparing the OE-type posterior distributions to non-Gaussian ones obtained with Markov chain Monte Carlo (MCMC). Finally, we demonstrate how AOE scales to a larger AVIRIS-NG scene, showcasing its ability to handle complex, large-scale data. Full article

In recent years, digital technologies have become indispensable tools for the preservation and documentation of architectural and cultural heritage. Traditional 3D modeling methods, such as photogrammetry and laser scanning, require specialized equipment and extensive manual processing. Neural Radiance Field, an AI-based technique, enables photorealistic 3D reconstructions from a limited set of 2D images. NeRF excels in cultural heritage documentation by effectively rendering reflective and translucent surfaces, which often pose challenges to conventional methods. These approaches significantly accelerate workflows, reduce costs, and minimize manual intervention, making them ideal for inaccessible or fragile sites. The application of NeRF combined with drone-acquired high-resolution images, as demonstrated in the Katolička Porta project in Novi Sad, produces highly detailed and accurate digital replicas. This integration also supports virtual restoration and texture enhancement, enabling non-invasive exploration of conservation scenarios. Katolička Porta, a historically significant site that has evolved over centuries, benefits from these advanced digital preservation techniques, which help maintain its unique architectural and cultural identity. This integration of technologies represents the future of cultural heritage conservation, offering innovative possibilities for visualization, research, and protection. Full article

The successful launch of Korea Multipurpose Satellite-3/Advanced Earth Imaging Sensor System (KOMPSAT-3/AEISS) on 18 May 2012 allowed the Republic of Korea to meet the growing demand for high-resolution satellite imagery. However, like all satellite sensors, KOMPSAT-3/AEISS experienced temporal changes post-launch and thus requires ongoing evaluation and calibration. Although more than a decade has passed since launch, the KOMPSAT-3/AEISS mission and its multi-year data archive remain widely used. This study focused on the cross-calibration of KOMPSAT-3/AEISS with Sentinel-2A/Multispectral Instrument (MSI) by comparing the radiometric responses of the two satellite sensors under similar observation conditions, leveraging the linear relationship between Digital Numbers (DN) and top-of-atmosphere (TOA) radiance. Cross-calibration was performed using near-simultaneous satellite images of the same region, and the Spectral Band Adjustment Factor (SBAF) was calculated and applied to account for differences in spectral response functions (SRF). Additionally, Bidirectional Reflectance Distribution Function (BRDF) correction was applied using MODIS-based kernel models to minimize angular reflectance effects caused by differences in viewing and illumination geometry. This study aims to evaluate the radiometric consistency of KOMPSAT-3/AEISS relative to Sentinel-2A/MSI over Baotou scenes acquired in 2022–2023, derive band-specific calibration coefficients and compare them with prior results, and conduct a side-by-side comparison of cross-calibration and vicarious calibration. Furthermore, the cross-calibration yielded band-specific gains of 0.0196 (Blue), 0.0237 (Green), 0.0214 (Red), and 0.0136 (NIR). These findings offer valuable implications for Earth observation, environmental monitoring, and the planning and execution of future satellite missions. Full article

The NASA CERES project conducts an independent assessment of the calibration stability of MODIS and VIIRS reflective solar bands to ensure consistency in CERES-derived clouds and radiative flux products. The assessment includes the use of tropical deep convective cloud invariant targets (DCC-IT), identified using a simple brightness temperature threshold. For visible bands, the collective DCC pixel radiance probability density function (PDF) was negatively skewed. By tracking the bright inflection point, rather than the PDF mode, and applying an anisotropic adjustment suited for the brightest DCC radiances, the lowest trend standard errors were obtained within 0.26% for NPP-VIIRS and within 0.36% for NOAA20-VIIRS and Aqua-MODIS. A kernel density estimation function was used to infer the PDF, which avoided discretization noise caused by sparse sampling. The near 10° regional consistency of the anisotropic corrected PDF inflection point radiances validated the DCC-IT approach. For the shortwave infrared (SWIR) bands, the DCC radiance variability is dependent on the ice particle scattering and absorption and is band-specific. The DCC radiance varies regionally, diurnally, and seasonally; however, the inter-annual variability is much smaller. Empirical bidirectional reflectance distribution functions (BRDFs), constructed from multi-year records, were most effective in characterizing the anisotropic behavior. Due to the distinct land and ocean as well as regional radiance differences, land, ocean, and regional BRDFs were evaluated. The regional radiance variability was mitigated by normalizing the individual regional radiances to the tropical mean radiance. Because the DCC pixel radiances have a Gaussian distribution, the mean radiance was used to track the DCC response. The regional BRDF-adjusted DCC-IT mean radiance trend standard errors were within 0.38%, 0.46%, and 1% for NOAA20-VIIRS, NPP-VIIRS, and Aqua-MODIS, respectively. Full article

Bi-phasic (with a local minimum) response of fluorescence to scattering when probed by a single fiber (SF) was first observed in 2003. Subsequent experiments and Monte Carlo studies have shown the bi-phasic turning of SF fluorescence to occur at a dimensionless reduced scattering of ~1 and vary with absorption. The bi-phase of SF fluorescence received semi-empirical explanations; however, better understandings of the bi-phase and its dependence on absorption are necessary. This work demonstrates a quasi-analytical projection of a bi-phasic pattern comparable to that of SF fluorescence via photon-transport analyses of fluorescence in a center-illuminated-area-detection (CIAD) geometry. This model-approach is principled upon scaling of the diffuse fluorescence between CIAD and a SF of the same size of collection, which expands the scaling of diffuse reflectance between CIAD and a SF discovered for steady-state and time-domain cases. Analytical fluorescence for CIAD is then developed via radial-integration of radially resolved fluorescence. The radiance of excitation is decomposed to surface, collimated, and diffusive portions to account for the surface, near the point-of-entry, and diffuse portion of fluorescence associated with a centered illumination. Radiative or diffuse transport methods are then used to quasi-analytically deduce fluorescence excited by the three portions of radiance. The resulting model of fluorescence for CIAD, while limiting to iso-transport properties at the excitation and emission wavelengths, is compared against the semi-empirical model for SF, revealing bi-phasic turning [0.5~2.6] at various geometric sizes [0.2, 0.4, 0.6, 0.8, 1.0 mm] and a change of three orders of magnitude in the absorption of the background medium. This model projects a strong reduction in fluorescence versus strong absorption at high scattering, which differs from the semi-empirical SF model’s projection of a saturating pattern unresponsive to further increases in the absorption. This framework of modeling fluorescence may be useful to project frequency-domain and lifetime pattens of fluorescence in an SF and CIAD. Full article