Search Results (346)

This review analyses and logically structures modern intelligent sensor technologies in the context of animal husbandry, feed production, and veterinary medicine. The main research discussed in the article focuses on machine learning based on modern neural network models, computer vision, and sensor systems that are transforming the methods for assessing the health, behavior, and nutrition of farm animals. The first part examines modern approaches to quality control and optimization of mineral and vitamin premixes, including visual inspection using visual sensors and neural networks. Key roles are played by precise dosing, component stability (minerals, vitamins), and the transition to more bioefficient organic forms of micronutrients to reduce environmental impact. Improvements in feed and premix production are analyzed, including automation, energy management, and the use of machine learning for non-destructive quality control, defect detection, mixing homogeneity assessment, and vitamin stability prediction. The second part analyzes methods for animal location and behavior detection. This article presents computer vision-based systems, including modifications of YOLO, for automatically tracking and classifying key behavioral patterns (lying down, standing, feeding, and aggression) in cattle and pigs, even in crowded conditions. It also discusses the use of ultra-wideband (UWB) systems and accelerometers combined with machine learning for high-precision positioning and detection of specific behavioral anomalies, such as lameness and playfulness. The third section focuses on the application of machine learning in veterinary diagnostics, including the automated interpretation of medical images (X-ray, ultrasound, and MRI) as sensor data streams for the diagnosis of cardiovascular, oncological, and orthopedic diseases in farm and small animals. Furthermore, the article examines the use of machine learning models for proactive disease diagnosis in farm animals and poultry based on multimodal data and image analysis. Considerable attention is given to methods and tools for radiometric diagnosis of animal diseases at an early stage using microwave sensors, as well as laser therapy and surgery in veterinary medicine. The review concludes that the integration of intelligent systems enables a transition to data-driven livestock management, significantly improving animal welfare and, consequently, the efficiency and sustainability of agricultural production. Full article

Remote sensing change detection (RSCD) aims to produce pixel-accurate change maps from bi-temporal images yet is fundamentally challenged by radiometric pseudo-changes (season, illumination, and atmosphere) that cause structure–environment entanglement in deep features. We propose CSD-Net, a framework centered on content–style decoupling (CSD): a physics-inspired feature decomposition mechanism that encourages separation between intrinsic geometric content and extrinsic environmental style. In the CSD module, learnable pseudo-change tokens estimate a spatially invariant global style proxy through cross-attention and broadcast, and subtraction performs feature-level radiometric-bias compensation, yielding pseudo-change-robust content features for change prediction. CSD-Net (Base) alone achieves state-of-the-art performance across four benchmarks (LEVIR-CD, LEVIR-CD+, CDD, and WHU) with favorable accuracy–efficiency trade-off (14.49M parameters and 15.26G FLOPs). We further explore an optional extension, CSD-Net⁺, that employs an MLLM (Qwen2.5-3B, LoRA-tuned) as a semantic refiner and SAM for instance mask refinement, coupled with uncertainty-aware three-way softmax fusion. This exploratory Stage 2 brings modest but consistent IoU improvements of 0.45–2.20% at the cost of significant computational overhead and is designed for offline, quality-critical scenarios. We provide a comprehensive account of both the effectiveness and the limitations of the proposed approach, including the marginal benefit–cost ratio of foundation model integration. Full article

River systems and their transitional zones near estuaries are characterized by strong spatial and temporal variability in both hydro-chemical and optical conditions. These dynamics make the monitoring of key water quality indicators such as chlorophyll-a (Chl-a) particularly demanding. Unmanned aerial systems (UASs) equipped with multispectral sensors have increasingly been used to address these challenges, providing high spatial resolution observations in environments where satellite imagery is often constrained by narrow channel widths and complex optical conditions. This systematic review examines the use of multispectral sensors for the detection, estimation, and mapping of chlorophyll-a in riverine, estuarine and transitional environments. Following the PRISMA 2020 framework, sixteen peer-reviewed studies published between 2016 and 2025 were identified and analyzed, focusing on the observation platforms employed, spectral band configurations, radiometric processing procedures, and the modeling approaches used to retrieve chlorophyll-a concentrations. Across the reviewed literature, most applications rely on empirical spectral indices based on red, red-edge, and near-infrared wavelengths, usually calibrated with concurrent in situ measurements. Machine learning methods appear mainly in more recent publications, yet their performance remains strongly tied to site-specific calibration datasets. Notable differences in radiometric correction workflows, validation protocols, and documentation of results complicate direct comparison among studies. This review highlights the strong potential of UAS multispectral observations for resolving small-scale spatial patterns of chlorophyll-a in dynamic river systems, while underscoring the need for greater methodological consistency in future research. Full article

The Tonglushan ore field is an important component of the polymetallic mineralization belt in the middle and lower reaches of the Yangtze River in China. The skarn-type Cu, Fe, Au, and Mo molybdenum deposits are mainly developed in the contact zone between the rock mass and the strata, as well as in the contact zone between residual and capturing bodies in the rock body. The distribution of ore bodies is controlled by faults and strata, but there is a lack of large-scale geophysical information on the contact relationship between the ore-forming geological body and the host rock and on the deep spatial morphology of the ore-forming structure and intrusion rock. The study uses the JS-WEM2 wide-field electromagnetic instrument and the RS230 spectrometer to conduct the ground frequency domain electromagnetic and radiometric spectrometry measurements on four profiles. The measurement results indicate that the fault distribution in the Tonglushan ore field is predominantly in the NW-trending and NE-trending directions. The NW-trending Tonglushan–Lijiashan fault (F2) is a steeply dipping fault; the NE-trending faults are minor, with steep dips, generally extending no deeper than −1000 m. The Tonglushan stock exhibits the northeastward uplift, characterized by southward overlap and southeastward dip. The deep resistivity is greater than 3000 Ω·m, while the resistivity below −1000 m is less than 2000 Ω·m due to the fault influence. The ore bodies are mainly distributed along the contact zones where variations in the occurrence of the rock intersect with the strata. On resistivity profiles, these zones show the gradient variation in resistivity and the distorted shape of the resistivity contour line. The radioactive element contents of wall rock above the ore bodies are characterized by high U, high Th, and low K. The Wide-Field Electromagnetic Method (WFEM) can effectively detect the distribution and morphology of rocks and faults, and combined with the radioactive characteristics of geological bodies, it can effectively identify concealed faults and the favorable mineralization target areas. Novelty: The study combines the WFEM with radiometric measurements to reduce uncertainty in exploration compared to using only one method. It improves the detection accuracy and target identification ability of deep hidden ore bodies, providing the new technical method for deep mineral exploration in complex structural areas. Full article

Accurately tracking the spatial and temporal variations of water vapor is indispensable for weather forecasting and climate adaptation, yet remains challenging due to the sparse coverage and discontinuity of ground-based observations. Satellite remote sensing, particularly from geostationary satellites like Meteosat Third Generation Imager-1 (MTG-I1), offers continuous, high-resolution data. To the best of our knowledge, MTG-I1 is the first geostationary satellite equipped with a near-infrared (NIR) spectral band specifically designed for detecting water vapor. To address the lack of precipitable water vapor (PWV) data derived from the Flexible Combined Imager (FCI) onboard MTG-I1, a novel semi-empirical (SE) algorithm optimized for PWV retrieval is proposed. Validation against ground-based PWV measurements using an initial test set and a temporally independent test set yielded relative errors of no more than 0.10, indicating stable retrieval performance outside the model-development period. The FCI-derived PWV retrievals were also more accurate than the corresponding MODIS PWV data. Compared to the traditional radiative transfer model (RTM)-based retrieval method, the SE method shows greater adaptability to systematic differences between the observed and RTM-simulated FCI reflectance. After correcting for radiometric degradation, the RTM-based algorithm achieves a 41% reduction in absolute error and a 47% reduction in relative error, bringing its accuracy in line with the SE algorithm. Overall, the proposed SE algorithm demonstrates superior robustness and adaptability, and can provide more reliable remote sensing PWV data to support weather forecasting and climate research. Full article

Reliable cadastral modernization requires distinguishing legally authoritative boundaries, human-interpreted parcel geometry, and image-visible evidence. This study examines the spatial proximity between a human-interpreted parcel boundary layer and unfiltered radiometric edge evidence derived from the same high-resolution orthophoto in Vientiane, Lao PDR. The analysis is not a cadastral accuracy validation and does not treat Canny-derived edges as an independent or higher-accuracy reference. Instead, it quantifies parcel-level boundary-to-radiometric-edge offset as a preliminary image-observability screening layer. For 89,763 parcels, nearest-edge offsets were summarized using mean, median, and upper-tail metrics and examined through spatial clustering, Canny threshold sensitivity testing, and a sample-based visual audit. Across parcels, mean offset values averaged 0.72 m, while median-offset values had a median of 0.21 m. Sensitivity testing showed that absolute offset magnitudes vary with edge-detection thresholds, indicating that the metric should not be interpreted as ISO-style positional accuracy. The mapped clusters, therefore, indicate an elevated boundary-to-radiometric-edge offset, as opposed to a confirmed cadastral error. The workflow is intended to support preliminary prioritization for expert visual review, semantic filtering, cadastral record checking, or field verification. Full article

The detection of small archaeological artifacts in high-resolution aerial imagery is challenged by minimal target size and local spectral and geometric similarity to background soils. This study identifies a failure mode in end-to-end deep learning where radiometrically dominant chromatic signals destabilize gradient-based optimization, leading to rapid training collapse. Using UAV imagery of Maya archaeological sites in Belize, we examine fingernail-sized ceramic sherds characterized by a consistent reddish hue. A Hue-Weighted Loss Function (HWLF) is introduced as a diagnostic instrument. Under severe class imbalance, chromatic gradients suppress geometric feature learning, collapsing detection within 300 iterations. Motivated by this discovery, we propose a staged detection architecture that decouples geometric candidate generation from chromatic validation. Candidates are detected via a transformer-based object detector and validated using hue constraints derived from unmodified 16-bit HSV representations. This approach reduced the Phase I candidate pool (177,148 geometric detections) to 1647 prioritized detections—a 99.1% reduction—while retaining 97.8% of annotated targets (F1 = 0.731). Chromatic priors may be more effective as decoupled post-inference discriminants than as embedded end-to-end optimization signals under severe class imbalance, where their gradient influence risks suppressing geometric feature learning entirely. Full article

Accurate registration of UAV-derived thermal infrared orthophotos and RGB orthophotos is essential for multi-sensor geospatial analysis, but it remains challenging because thermal imagery generally has lower spatial resolution, weaker texture, and less distinct structural information than RGB imagery. This study comparatively evaluated five histogram equalization methods—histogram equalization (HE), contrast-limited adaptive histogram equalization (CLAHE), brightness-preserving bi-histogram equalization (BBHE), dualistic sub-image histogram equalization (DSIHE), and minimum mean brightness error bi-histogram equalization (MMBEBHE)—for improving AKAZE-based registration of land surface temperature (LST) orthophotos to reference RGB orthophotos. High-accuracy RGB orthophotos generated using GNSS-surveyed ground control points were used as the geometric reference. Thermal data were acquired twice at each of two study sites with contrasting surface characteristics and processed into LST orthophotos. Each histogram equalization method was applied to the LST orthophotos, after which keypoints and descriptors were extracted using AKAZE, tentative correspondences were established, outliers were removed using RANSAC, and an affine transformation was estimated from the inlier correspondences. Here, an inlier denotes a tentative match that remained geometrically consistent after RANSAC-based outlier rejection. The estimated transformation was then applied to the source LST raster to preserve radiometric values in the final corrected product. Performance was assessed using the number of detected keypoints, tentative matches, RANSAC-verified inliers, matching efficiency, reproducibility, and exploratory statistical analysis. Among the five methods, BBHE consistently produced the highest number of inliers and the best matching efficiency at both study sites, while also showing the lowest variability between repeated acquisitions. These results indicate that brightness-preserving histogram equalization is particularly effective for thermal–RGB orthophoto registration and can improve the reliability of UAV-derived thermal mapping products for geomatics applications. Full article

The launch of the Flexible Combined Imager (FCI) sensor aboard the Meteosat Third Generation (MTG) satellite enables higher temporal and spatial resolution for geostationary environmental monitoring. This study explores the feasibility of near-real-time fire detection using MTG-FCI data. Two unsupervised approaches are evaluated on data covering the Italian territory: a conventional threshold method, applying fixed radiometric thresholds and brightness temperature differences between 3.8 μm and 10.5 μm, and an experimental Lightweight U-Net autoencoder for anomaly detection. The autoencoder is trained exclusively on fire-free imagery, with fires identified as statistical anomalies in the reconstruction error, refined through local and global z-score analysis. Validation combines high-resolution Sentinel-2 imagery, Fire Radiative Power (FRP) and data from European Forest Fire Information System (EFFIS). Results demonstrate that MTG-FCI can trigger active fire alerts prior to polar overpasses in $67.32\%$ of the synchronized cases, providing a median early detection lead time of 21.00 min and reaching an advance of up to approximately 6 h in exceptional instances. While the spatial resolution limits detailed fire-front mapping, the high temporal resolution enables a robust near-real-time alerting system, providing enhanced detection of transient fire events that are not captured by lower-frequency polar-orbiting sensors. Full article

Surface stain contamination poses a critical barrier to the automated, high-precision fiber identification required for industrial-scale waste textile recycling. In this study, a dataset comprising 120 physical specimens (yielding 1200 regions of interest, ROIs) across 12 contamination categories was constructed by contaminating cotton, polyester, and poly-cotton blend textiles with carbon black, protein, and oil stains. The spectral interference effects of stains—including baseline drift and spectral overlapping induced by physical shielding and chemical absorption—were systematically analyzed. To identify the optimal classification pipeline, three mathematical preprocessing methods (First Derivative, FD; Standard Normal Variate, SNV; and Multiplicative Scatter Correction, MSC) were evaluated alongside Support Vector Machine (SVM) and One-Dimensional Convolutional Neural Network (1D-CNN) models. Results show that among the SVM-based pipelines, the FD-SVM model effectively resolves overlapping absorption peaks, achieved an average accuracy of 98.17% ± 1.33%, but remains highly dependent on mathematical preprocessing. In contrast, the 1D-CNN model employing a progressive stacking architecture of multi-scale convolutional kernels attains a highly robust mean accuracy of 99.58% ± 0.56% under a strict specimen-level 10-fold cross-validation. It achieves this by directly utilizing radiometrically calibrated raw spectra, thereby effectively bypassing manual spectral feature engineering. These findings demonstrate that Hyperspectral Imaging coupled with end-to-end deep learning provides a feasible and industrially deployable solution for simultaneous stain detection and fiber identification in waste textile sorting. Full article

In a context of greenhouse-gas-reduction for climate-change mitigation, Optical Gas Imaging (OGI) is cited by US and EU regulations as a key technology for detecting methane leaks in the oil and gas industry. The paper outlines the principles of OGI, covering specificity of both high-performance cooled cameras and cost-effective thermal infrared uncooled cameras. It explains camera design, the optical-radiometric theory of contrast and sensitivity, and provides a comprehensive description of the key performance indicators (KPIs) such as NETD, NECL, and MDLR; together with parameters that influence them. These theoretical concepts are supported by measurements taken under laboratory conditions and outdoors, with wind and complex scenes. Finally, video-processing methods for visualizing gas leaks are presented, showing how they increase visual sensitivity and reduce the user’s cognitive load. 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

The Ozone Monitor Suite-Nadir (OMS-N) onboard the FY-3F satellite is a key payload for global atmospheric ozone and trace gas detection. The data quality depends on the accuracy of ground calibration. This study presents a systematic ground calibration of OMS-N. The instrument operates over 250–500 nm, with a spatial resolution of 7 × 7 km² and a spectral resolution of 0.5–1 nm. Radiometric calibration was performed using an integrating sphere, spectral calibration using a tunable laser, and geometric calibration using a precision turntable. All tests were conducted under controlled environmental conditions (20 ± 3 °C and 50% ± 10% humidity). The absolute radiometric calibration uncertainty was below 2.33% for UV1/UV2 and 1.69% for VIS, with relative uncertainties ≤1.84%. The spectral wavelength error was ≤0.01 nm for the VIS channel and ≤0.03 nm for the UV1/UV2 channels, and the geometric positioning uncertainty was better than 0.1 pixels. All performance indicators met or exceeded the design requirements. These results provide technical support for the quantitative application of OMS-N data in atmospheric monitoring and establish a reference framework for the ground calibration of similar ultraviolet hyperspectral instruments. 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

Power-system reliability is crucial for sustainable development, but large-scale, long-term monitoring remains challenging. Existing nighttime light (NTL)-based outage detection methods often rely on fixed thresholds or prior information, limiting cross-regional application. To address this, we develop an adaptive thresholding framework using daily NASA Black Marble data. Observations are grouped by view angle to mitigate radiometric instability, and a per-pixel dynamic baseline is constructed from high-radiance statistics, enabling robust anomaly detection without prior outage timing. From the detected anomalies, we formulate a population-weighted NTL power reliability index (NTPRI) to quantify regional electricity service reliability. Validation across six diverse outage events yields an F1 score of 0.807. National-scale analysis shows NTPRI correlates significantly with the World Bank’s System Average Interruption Duration Index (SAIDI). The derived Light Anomaly Rate (LAR) further supports pixel-level frequency analysis. Together, this framework provides a transferable remote-sensing tool for large-scale power-reliability assessment in data-scarce regions, supporting disaster impact evaluation and energy vulnerability analysis. Full article