Search Results (138)

Integration of LiDAR and thermal sensing has become increasingly important in robotics, infrastructure diagnostics, environmental monitoring, and autonomous perception systems. LiDAR sensors provide accurate three-dimensional geometric information but do not directly capture thermal properties of observed objects, whereas thermal cameras provide temperature distributions without explicit spatial structure. Fusion of both sensing modalities enables thermally augmented 3D scene reconstruction and spatial localization of temperature anomalies. This paper presents a practical LiDAR–thermal fusion framework for three-dimensional localization of heat sources using an Ouster OS1 LiDAR sensor and a FLIR A70 thermal camera. The proposed framework includes intrinsic thermal-camera calibration, extrinsic LiDAR–thermal calibration, multimodal data synchronization, projection of LiDAR points onto the thermal image plane, and assignment of temperature values to spatial points. Additionally, a dedicated thermally distinguishable calibration target is proposed to enable reliable multimodal feature extraction under low-contrast LWIR imaging conditions. The developed framework was experimentally validated using real radiometric thermal data and LiDAR point clouds acquired under laboratory conditions. Quantitative evaluation demonstrated reprojection errors below 1 pixel and a mean hottest-point localisation error of approximately 4.1 cm at a distance of 12.3 m. The results confirm that accurate spatial localisation of thermal anomalies can be achieved using a geometry-based multimodal fusion approach without relying on computationally expensive learning-based methods. The proposed framework emphasises practical deployment, deterministic calibration, and applicability in scenarios where limited training data or constrained computational resources make learning-based approaches difficult to apply. The proposed system may be applied to building energy diagnostics, industrial inspection, technical infrastructure monitoring, and robotic perception systems that require reliable spatial localisation of heat sources under real measurement conditions. Full article

Spaceborne detection of solar-induced chlorophyll fluorescence (SIF) requires extremely high radiometric accuracy, and the polarization characteristics of an ultra-wide swath spaceborne fluorescence ultraspectral camera directly affect the accuracy of SIF retrieval. This study takes an ultra-wide swath camera based on an off-axis three-mirror anastigmat telescope combined with a Littrow–Offner spectrometer as the research object. A full-field-of-view (FOV), full-spectral, pixel-by-pixel polarization testing system was established based on the Stokes–Muller formalism, achieving for the first time fine characterization and calibration of the pixel-level polarization properties of such a payload. The results show that: (1) polarization sensitivity (LPS) exhibits a strong linear positive correlation with wavelength (R² > 0.97), with good uniformity (fluctuation < 1%) across the full ±15° FOV; (2) the polarization sensitive axis (PSA) shows a symmetric distribution across the FOV and gradually approaches 90° as the wavelength increases, with a clear deviation in the short-wavelength bands and stabilization in the mid-to-long wavelength bands; (3) through multiple sets of cross-validation and Monte Carlo statistics, the calibration accuracy reaches 0.1%, and the system uncertainty is better than 0.05%. This study can provide data support and a reference basis for high-accuracy spaceborne SIF retrieval, payload polarization correction, and optical design optimization. Full article

Diameter at breast height (DBH) is a fundamental structural parameter for forest inventory and ecological analysis. However, field-based measurements (e.g., diameter tape surveys) are labor-intensive and inefficient for large-scale applications. Airborne light detection and ranging (LiDAR) provides an efficient alternative for individual-tree DBH estimation. Nevertheless, LiDAR-derived features—defined as statistical descriptors of point cloud structure and radiometric properties—are typically high-dimensional and redundant, which may degrade model performance. To address this issue, this study proposes an integrated framework combining Multi-Stage Feature Selection (MSFS) and Extreme Gradient Boosting (XGBoost) for DBH estimation. A total of 104 variables, including LiDAR-derived features (height, density, intensity, and canopy structure metrics) and structural parameters (tree height, crown diameter, and crown area), were used as predictors. The MSFS framework was applied to progressively reduce feature redundancy and identify an optimal subset, which was then used to train the XGBoost model. The results demonstrate that the MSFS–XGBoost model achieved the best performance, with a coefficient of determination (R²) of 0.901 and a root mean square error (RMSE) of 1.647 cm. Compared with models using the original feature set, R² increased by 0.384 and RMSE decreased by 1.146 cm. These findings indicate that the proposed framework effectively improves DBH estimation accuracy and provides a reliable approach for individual-tree parameter estimation and large-scale forest resource monitoring using airborne LiDAR data. Full article

Accurate brightness temperature (TB) measurements and microwave emissivity retrieval in passive microwave sensing conventionally rely on absolute radiometric calibration, which often requires additional hardware and complex procedures. Under well-defined geometric and environmental conditions, this study proposes a mirror-reflection-based method for measuring the microwave emissivity of flat scenes using ground-based radiometers without conventional absolute calibration. The method employs a simplified four-step observation sequence, in which the radiometer measures the pure flat scene, the flat scene with mirror reflection, the reference wall, and the cold sky. A geometric model is developed to determine the effective incidence-angle range, and an analytical framework is developed to evaluate retrieval accuracy. Numerical simulations are conducted to examine the effects of scene material, reference-wall property, operating frequency, polarization, and radiometric sensitivity. Outdoor experiments are further performed to assess feasibility under practical measurement conditions. The results show that, within moderate incidence-angle ranges and under stable radiometric conditions, the retrieved emissivities of flat scenes agree well with theoretical predictions. These findings indicate that the proposed mirror-reflection-based approach provides a feasible supplementary or alternative solution for emissivity estimation of flat targets in ground-based measurements when absolute calibration is unavailable or impractical, rather than a replacement for conventional calibration techniques. Full article

Accurate modelling of reflection, transmission, absorption, and emission at material interfaces is essential for infrared imaging, rendering, and the simulation of optical and sensing systems. This need is particularly pronounced across the short-wave to long-wave infrared (SWIR–LWIR) spectrum, where many materials exhibit dispersion- and wavelength-dependent attenuation described by complex refractive indices. In this work, we introduce a unified formulation of the full Fresnel equations that directly incorporates wavelength-dependent complex refractive-index data and provides physically consistent interface behaviour for both dielectrics and conductors. The approach reformulates the classical Fresnel expressions to eliminate sign ambiguities and numerical instabilities, resulting in a stable evaluation across incidence angles and for strongly absorbing materials. We demonstrate the model through spectral-rendering simulations that illustrate realistic reflectance and transmittance behaviour for materials with different infrared optical properties. To assess its suitability for thermal-infrared applications, we also compare the simulated long-wave emission of a heated glass sphere with measurements from a LWIR camera. The agreement between measured and simulated radiometric trends indicates that the proposed formulation offers a practical and physically grounded tool for wavelength-parametric interface modelling in infrared imaging, supporting applications in spectral rendering, synthetic data generation, and infrared system analysis. Full article

Ensuring good air quality in indoor environments of historical and artistic significance is essential not only for protecting valuable artworks but also for safeguarding human health. While many studies in this field tend to focus on the preservation of cultural heritage, fewer have addressed the impact on visitors and worshippers. Yet, places such as museums, galleries, churches, and other religious sites attract large numbers of people, making indoor air quality a key factor for their well-being. This study focused on evaluating air quality within the Santuario della Beata Vergine dei Miracoli in Saronno, Italy, a religious site that welcomes large numbers of visitors and worshippers each year. A detailed analysis of particulate matter was conducted, including chemical characterization by ICP-MS for metals, ion chromatography for water-soluble ions, and thermal–optical analysis for the carbonaceous fraction, as well as assessments of size distribution and radiometric properties. The results indicated overall good air quality conditions: concentrations of heavy metals were below levels of concern (<35 ng m⁻³), and gross alpha, beta, and ¹³⁷Cs activity concentrations remained below the minimum detectable thresholds. Hence, no significant health risks were identified. Full article

Automobile license plate image generation represents a pivotal technology for the development of intelligent transportation systems. However, existing methods are constrained by their inability to simultaneously preserve geometric structure and radiometric properties of both license plates and characters. To overcome this limitation, we propose a novel framework for generating geometrically and radiometrically consistent license plate images. The proposed radiometric enhancement framework integrates two specialized modules, which are precise geometric rectification and radiometric property learning. The precise geometric rectification module exploits the perspective transformation consistency between character regions and license plate boundaries. By employing a feature matching algorithm based on character endpoint correspondence, this module achieves precise plate rectification, thereby establishing a geometric foundation for maintaining character structural integrity in generated images. The radiometric property learning module implements a precise character inpainting strategy with fluctuation compensation inpainting to reconstruct background regions, followed by a character-wise style transfer approach to ensure both geometric and radiometric consistency with realistic automobile license plates. Furthermore, we introduce a physical validation and evaluation method to quantitatively assess image quality. Comprehensive evaluation on real-world datasets demonstrate that our method achieves superior performance, with a peak signal-to-noise ratio (PSNR) of 13.83 dB and a structural similarity index measure (SSIM) of 0.57, representing significant improvements over comparative methods in preserving both structural integrity and radiometric properties. This framework effectively enhances the visual fidelity and reliability of generated automobile license plate images, thereby providing high-quality data for intelligent transportation recognition systems while advancing license plate image generation technology. Full article

Atmospheric correction (AC) over inland and coastal waters remains a key challenge in ocean color remote sensing, often limiting the effective use of satellite data for aquatic monitoring. AC algorithm performance is highly sensitive to water type and optical properties. To address this, we systematically evaluated six state-of-the-art AC algorithms—ACOLITE, C2RCC, iCOR, L2GEN, OC-SMART, and POLYMER—using Landsat-8/9 OLI data. This study leverages 440 high-quality in situ radiometric matchups spanning a wide range of aquatic environments, including inland lakes from China’s Satellite-Ground Synchronous Campaign and coastal waters from the globally distributed GLORIA dataset. These complementary datasets provide a robust benchmark for evaluating AC algorithm performance. A unified Optical Water Type (OWT) classification framework ensured consistency across environmental conditions. Results highlight significant variability in algorithm performance based on water type. In coastal waters, L2GEN demonstrated the lowest errors in visible bands, whereas OC-SMART achieved superior overall accuracy in inland waters. Notably, ACOLITE exhibited better performance than other algorithms in the blue spectral region (443 and 482 nm) for inland waters. OWT-specific analysis showed that OC-SMART maintained robust accuracy across the turbidity gradient, while ACOLITE and iCOR excelled in highly turbid waters (OWTs 5–6). In contrast, L2GEN, C2RCC, and POLYMER were more effective in clearer waters (OWTs 3–4). The study further discusses the applicability of each algorithm and offers recommendations for mitigating adjacency effects (AE) to improve AC accuracy. These findings provide valuable guidance for selecting and optimizing AC strategies for inland and coastal water monitoring. Full article

Fixed automated (unmanned) above-water radiometric measurements are subject to unavoidable sky conditions and surface perturbations, leading to significant uncertainties in retrieved water surface remote sensing reflectances (R_rs(λ), sr⁻¹). This study evaluates various above-water R_rs(λ) glint correction methods using a comprehensive dataset collected at the Royal Netherlands Institute for Sea Research (NIOZ) Jetty Station located in the Marsdiep tidal inlet of the Dutch Wadden Sea, the Netherlands. The dataset includes in-situ water constituent concentrations (2006–2020), inherent optical properties (IOPs) (2006–2007), and above-water hyperspectral (ir)radiance observations collected every 10 min (2006–2023). The bio-optical models were validated using in-situ IOPs and utilized to generate glint-free remote sensing reflectances, R_rs,ref(λ), using a robust IOP-to-R_rs forward model. The R_rs,ref(λ) spectra were used as a benchmark to assess the accuracy of glint correction methods under various environmental conditions, including different sun positions, wind speeds, cloudiness, and aerosol loads. The results indicate that the three-component reflectance model (3C) outperforms other methods across all conditions, producing the highest percentage of high-quality R_rs(λ) spectra with minimal errors. Methods relying on fixed or lookup-table-based glint correction factors exhibited significant errors under overcast skies, high wind speeds, and varying aerosol optical thickness. The study highlights the critical importance of surface-reflected skylight corrections and wavelength-dependent glint estimations for accurate above-water R_rs(λ) retrievals. Two showcases on chlorophyll-a and total suspended matter retrieval further demonstrate the superiority of the 3C model in minimizing uncertainties. The findings highlight the importance of adaptable correction models that account for environmental variability to ensure accurate R_rs(λ) retrieval and reliable long-term water quality monitoring from hyperspectral radiometric measurements. Full article

Accurate hyperspectral simulations are critical for the vicarious calibration of next-generation space-based sensors and for ensuring the long-term consistency of climate data records. This study presents a refined methodology to generate simulated radiometric calibration references over bright desert pseudo-invariant calibration sites, specifically designed to meet the stringent accuracy requirements of hyperspectral observations. Building on metrology principles, and in the absence of SI-traceable references, the approach leverages simulated reflectance over stable desert targets as a community-accepted calibration reference. Key advancements include improved surface reflectance modelling using the Rahman–Pinty–Verstraete model and the Combined Inversion of Surface and Aerosol (CISAR) algorithm, enhanced atmospheric property characterization from multiple state-of-the-art datasets, and the use of the Eradiate Monte Carlo-based radiative transfer model. These refinements reduce uncertainty in simulated top-of-atmosphere reflectance, achieving an accuracy within ±3% in high-transmittance spectral regions. Validation against both multispectral and hyperspectral satellite data (i.e., EMIT, EnMAP, and PRISMA) confirms the robustness of the methodology. This work establishes a reliable framework for hyperspectral sensor calibration and intercalibration, addressing the pressing need for traceable, high-fidelity reference data in Earth observation. Full article

Biomass burning (BB) aerosols are the largest source of absorbing aerosols on Earth. Coupled with marine stratocumulus clouds (MSC), their radiative effects are enhanced and can cause cloud property changes (first indirect effect) or cloud burn-off and warm up the atmospheric column (semi-direct effect). Nevertheless, the derivation of their quantity and optical properties in the presence of MSC clouds is confounded by the uncertainties in the retrieval of the underlying cloud properties. Therefore, a robust methodology is needed for the coupled retrievals of absorbing aerosol above clouds. Here, we present a new retrieval approach implemented for a Spectro radiometric multi-angle polarimetric airborne platform, the research scanning polarimeter (RSP), during the ORACLES campaign over the Southeast Atlantic Ocean. Our approach transforms the 1D measurements over multiple angles and wavelengths into a 3D image-like input, which is then processed using various deep learning (DL) schemes to yield aerosol single scattering albedos (SSAs), aerosol optical depths (AODs), aerosol effective radii, and aerosol complex refractive indices, together with cloud optical depths (CODs), cloud effective radii and variances. We present a comparison between the different DL approaches, as well as their comparison to existing algorithms. We discover that the Vision Transformer (ViT) scheme, traditionally used by natural language models, is superior to the ResNet convolutional Neural-Network (CNN) approach. We show good validation statistics on synthetic and real airborne data and discuss paths forward for making this approach flexible and readily applicable over multiple platforms. Full article

Compared with traditional single-view and radiometric-only observations, multiangle polarimetric observations of water-leaving radiation play a crucial role in enhancing the retrieval of ocean constituents and aerosol microphysical properties. In this study, the impacts of uncertainties in the degree of polarization (DOP) of water-leaving radiance (L_w) on the retrieval of oceanic constituents and inherent optical properties (IOPs) were investigated via global radiative transfer (RT) simulations and the fully connected U-Net (FCUN) model. The uncertainties in the retrieval of oceanic constituents and IOPs were further investigated with various sensor azimuth angles. The results indicated that the global mean absolute percentage errors (MAPEs) for differing oceanic constituents and IOPs significantly decreased as the number of observation angles increased. Taking the retrieval of Chla as an example, the global MAPEs between the FCUN predictions and RT simulation inputs for Chla concentrations under differing observation angles were 7.41%, 3.76%, 2.70%, 2.44%, 2.62%, and 1.82%. Moreover, the MAPEs at sensor azimuth angles of 0° and 30° were significantly lower than those at other azimuth angles for the single-view observations. As the number of observation angles increased, the variation in MAPEs with the sensor azimuth angle gradually weakened. Furthermore, the impact of errors in the L_w DOP on the retrieval uncertainties decreased as the number of observation angles increased, and the global MAPEs of Chla after adding the various random instrument noises were 46.56% (46.91%), 6.59% (7.21%), 5.21% (5.79%), 4.72% (4.98%), 3.99% (4.52%), and 3.64% (4.03%). Overall, the multiangle polarimetric observations can suppress or balance the impact of uncertainties in the L_w DOP on the retrieval of oceanic constituents and IOPs. Full article

Over the past quarter-century, the enhanced availability of satellite imagery, characterized by improved temporal, spectral, radiometric, and spatial resolutions, has enabled valuable insights into the spatial soil variability of annual croplands and orchards. This study investigates the impact of spatial resolution on classifying three-year, multi-temporal vegetation indices derived from satellites with coarse (30 m, Landsat 8), medium (10 m, Sentinel-2), and fine spatial resolutions (3.7 m, PlanetScope). The classification was performed using the fuzzy c-means algorithm, with the fuzziness performance index (FPI) and normalized classification entropy (NCE), which were used to determine the optimal number of management zones (MZs). Our results revealed that the Landsat 8-based NDVI images produced the highest number of clusters (nine for annual cropland and six for orchards), while the finer resolutions from PlanetScope reduced this to three clusters for both cultivation types, more accurately capturing the intra-parcel variability. Except for Landsat 8, the NDVI means of MZs generated based on Sentinel-2 and PlanetScope using the fuzzy c-means algorithm showed statistically significant differences from each other, as determined by a one-way and Welch’s ANOVA (p < 0.05). The use of PlanetScope imagery demonstrated its superiority in generating zones that reflect inherent variability, offering farmers actionable insights at a reconnaissance scale. Multi-temporal satellite imagery has proved effective in monitoring plant growth responses to edaphological soil properties. In our study, the PlanetScope satellites, which offer the highest spatial resolution, consistently produced effective zones for orchard areas. These zones have the potential to enhance farmers’ discovery of knowledge at a reconnaissance scale. With the increasing spatial resolution and enhanced spectral resolution of newer satellite sensors, using cluster analysis with insights from soil scientists promise to help farmers better understand and manage the fertility of their fields in a cost-effective manner. Full article

Plasma discharge at atmospheric pressure can be used to modify the surface electrical strength of insulators made of dielectric materials, thermosets and thermoplastics. The methodology of surface treatment by plasma discharge was published and applied to typical samples of materials with dimensions of 100 × 100 mm. To make the methodological procedure for influencing and predicting the desired change in the dielectric surface industrially applicable, it was necessary to develop a diagnostics methodology for applying plasma discharge at atmospheric pressure. This method was tested on previously selected and tested types of materials on samples of two types (thermoset, thermoplastic). The effects of measuring and evaluating the RF spectrum, along with the corresponding long-term change in the surface strength E_p of the dielectric sample, were demonstrated. This work presents repeated extension tests with statistical evaluation of the effects of surface treatment of dielectric samples using a slot plasma chamber in Ar, N₂ and O₂ atmospheres. The surface structure was modified using precursor-free plasma discharge according to the developed methodologies with the addition of radiometric evaluation of the plasma discharge. Changes in surface properties were measured and evaluated as a function of exposure time and the stability of the modification was evaluated with a prediction of the expected long-term surface properties. Full article

In order to improve the current standard of analysis-ready Synthetic Aperture Radar (SAR) backscatter data, we introduce a machine learning-based approach to estimate the slope of the backscatter–incidence angle relationship from several backscatter statistics. The method requires information from radiometric terrain-corrected gamma nought time series and overcomes the constraints of a limited orbital coverage, as exemplified with the Sentinel-1 constellation. The derived slope estimates contain valuable information on scattering characteristics of different land cover types, allowing for the correction of strong forward-scattering effects over water bodies and wetlands, as well as moderate surface scattering effects over bare soil and sparsely vegetated areas. Comparison of the estimated and computed slope values in areas with adequate orbital coverage shows good overall agreement, with an average RMSE value of 0.1 dB/° and an MAE of 0.05 dB/°. The discrepancy between RMSE and MAE indicates the presence of outliers in the computed slope, which are attributed to speckle and backscatter fluctuations over time. In contrast, the estimated slope excels with a smooth spatial appearance. After correcting backscatter values by normalising them to a certain reference incidence angle, orbital artefacts are significantly reduced. This becomes evident with differences up to 5 dB when aggregating the normalised backscatter measurements over certain time periods to create spatially seamless radar backscatter composites. Without being impacted by systematic differences in the illumination and physical properties of the terrain, these composites constitute a valuable foundation for land cover and land use mapping, as well as bio-geophysical parameter retrieval. Full article