Search Results (338)

Eggshell cracks are a critical factor affecting egg quality and food safety, with traditional detection methods often struggling to detect fine cracks, especially under multi-colored shells and complex backgrounds. To address this issue, we propose a non-destructive detection approach based on an enhanced three-dimensional convolutional neural network (3D-CNN), named 3D-CrackNet, integrated with hyperspectral imaging (HSI) for high-precision identification and localization of eggshell cracks. Operating within the 1000–2500 nm spectral range, the proposed framework employs spectral preprocessing and optimal band selection to improve discriminative feature representation. A residual learning module is incorporated to mitigate gradient degradation during deep joint spectral-spatial feature extraction, while a parameter-free SimAM attention mechanism adaptively enhances crack-related regions and suppresses background interference. This architecture enables the network to effectively capture both fine-grained spatial textures and contiguous spectral patterns associated with cracks. Experiments on a self-constructed dataset of 400 egg samples show that 3D-CrackNet achieves an F1-score of 75.49% and an Intersection over Union (IoU) of 60.62%, significantly outperforming conventional 1D-CNN and 2D-CNN models. These findings validate that 3D-CrackNet offers a robust, non-destructive, and efficient solution for accurately detecting and localizing subtle eggshell cracks, demonstrating strong potential for intelligent online egg quality grading and micro-defect monitoring in industrial applications. Full article

This study investigates typhoon-induced oscillations within Youngan Harbor, southwestern Taiwan, which frequently compromise port operations and cause dock overtopping. Two representative wave-resolving models, the Boussinesq-type FUNWAVE-TVD and the non-hydrostatic XBeach-NH, were applied to simulate a typhoon event and evaluate their predictive performance against field observations. Both models underestimated significant wave height across all frequency bands. Spectral analysis revealed that FUNWAVE-TVD generated higher energy in the infragravity and very-low-frequency ranges, whereas XBeach-NH exhibited greater energy in the swell and wind-wave bands. Spatial resonance patterns further indicated that a berth, located in a nodal region, experienced reduced tranquility due to considerable horizontal currents. Conversely, wave overtopping at a dock was driven by amplified vertical water-level oscillations in an antinodal region. These contrasting responses highlight the sensitivity of the models to nonlinear wave interactions and underscore the critical role of simulating harbor currents, emphasizing the need for careful model selection in harbor tranquility assessment. Full article

In hyperspectral image classification (HSIC), each pixel contains information across hundreds of contiguous spectral bands; therefore, the ability to perform long-distance modeling that stably captures and propagates these long-distance dependencies is critical. A selective structured state space model (SSM) named Mamba has shown strong capabilities for capturing cross-band long-distance dependencies and exhibits advantages in long-distance modeling. However, the inherently high spectral dimensionality, information redundancy, and spatial heterogeneity of hyperspectral images (HSI) pose challenges for Mamba in fully extracting spatial–spectral features and in maintaining computational efficiency. To address these issues, we propose S²GL-MambaResNet, a lightweight HSI classification network that tightly couples Mamba with progressive residuals to enable richer global, local, and multi-scale spatial–spectral feature extraction, thereby mitigating the negative effects of high dimensionality, redundancy, and spatial heterogeneity on long-distance modeling. To avoid fragmentation of spatial–spectral information caused by serialization and to enhance local discriminability, we design a preprocessing method applied to the features before they are input to Mamba, termed the Spatial–Spectral Gated Attention Aggregator (SS-GAA). SS-GAA uses spatial–spectral adaptive gated fusion to preserve and strengthen the continuity of the central pixel’s neighborhood and its local spatial–spectral representation. To compensate for a single global sequence network’s tendency to overlook local structures, we introduce a novel Mamba variant called the Global_Local Spatial_Spectral Mamba Encoder (GLS²ME). GLS²ME comprises a pixel-level global branch and a non-overlapping sliding-window local branch for modeling long-distance dependencies and patch-level spatial–spectral relations, respectively, jointly improving generalization stability under limited sample regimes. To ensure that spatial details and boundary integrity are maintained while capturing spectral patterns at multiple scales, we propose a multi-scale Mamba encoding scheme, the Hierarchical Spectral Mamba Encoder (HSME). HSME first extracts spectral responses via multi-scale 1D spectral convolutions, then groups spectral bands and feeds these groups into Mamba encoders to capture spectral pattern information at different scales. Finally, we design a Progressive Residual Fusion Block (PRFB) that integrates 3D residual recalibration units with Efficient Channel Attention (ECA) to fuse multi-kernel outputs within a global context. This enables ordered fusion of local multi-scale features under a global semantic context, improving information utilization efficiency while keeping computational overhead under control. Comparative experiments on four publicly available HSI datasets demonstrate that S²GL-MambaResNet achieves superior classification accuracy compared with several state-of-the-art methods, with particularly pronounced advantages under few-shot and class-imbalanced conditions. Full article

Low-light image enhancement (LLIE) degradation arises from insufficient illumination, reflectance occlusion, and noise coupling, and it manifests in the frequency domain as suppressed amplitudes with relatively stable phases. To address the fact that pure spatial mappings struggle to balance brightness enhancement and detail fidelity, whereas pure frequency-domain processing lacks semantic modeling, we propose AFMNet—a dual-domain collaborative enhancement network guided by an information-theoretic frequency prior. This prior regularizes global illumination, while spatial branches restore local details. First, a Multi-Scale Amplitude Estimator (MSAE) adaptively generates fine-grained amplitude-modulation maps via multi-scale fusion, encouraging higher output entropy through adaptive spectral-energy redistribution. Next, a Dual-Branch Spectral–Spatial Attention (DBSSA) module—comprising a Frequency-Modulated Attention Block (FMAB) and a Scale-Variable Depth Attention Block (SVDAB)—is employed: FMAB injects the modulation map as a frequency-domain prior into the attention mechanism to conditionally modulate the amplitude of value features while keeping the phase unchanged, thereby helping to preserve structural information in the enhanced output; SVDAB uses multi-scale depthwise-separable convolutions with scale attention to produce adaptively enhanced spatial features. Finally, a Spectral-Gated Feed-Forward Network (SGFFN) applies learnable spectral filters to local features for band-wise selective enhancement. This collaborative design achieves a favorable balance between illumination correction and detail preservation, and AFMNet delivers state-of-the-art performance on multiple low-light enhancement benchmarks. Full article

Reliable decoding of motor imagery (MI) from electroencephalographic signals remains a challenging problem due to their nonlinear, noisy, and non-stationary nature. To address this issue, this work proposes an end-to-end deep learning model, termed TEKTE-Net, that integrates time embeddings with a kernelized Transfer Entropy estimator to infer directed functional connectivity in MI-based brain–computer interface (BCI) systems. The proposed model incorporates a customized convolutional module that performs Takens’ embedding, enabling the decoding of the underlying EEG activity without requiring explicit preprocessing. Further, the architecture estimates nonlinear and time-delayed interactions between cortical regions using Rational Quadratic kernels within a differentiable framework. Evaluation of TEKTE-Net on semi-synthetic causal benchmarks and the BCI Competition IV 2a dataset demonstrates robustness to low signal-to-noise conditions and interpretability through temporal, spatial, and spectral analyses of learned connectivity patterns. In particular, the model automatically highlights contralateral activations during MI and promotes spectral selectivity for the beta and gamma bands. Overall, TEKTE-Net offers a fully trainable estimator of functional brain connectivity for decoding EEG activity, supporting MI-BCI applications, and promoting interpretability of deep learning models. Full article

This research presents a comprehensive analysis of the spatial extent and temporal change in benthic habitats within the Puck Lagoon in the southern Baltic Sea, utilizing integrated machine learning classification and multi-sourced remote sensing. Object-based image analysis was integrated with Random Forest, Support Vector Machine, and K-Nearest Neighbors algorithms for benthic habitat classification based on airborne bathymetric LiDAR (ALB), multibeam echosounder (MBES), satellite bathymetry, and high-resolution aerial photography. Ground-truth data collected by 2023 field surveys were supplemented with long temporal datasets (2010–2023) for seagrass meadow analysis. Boruta feature selection showed that geomorphometric variables (aspect, slope, and terrain ruggedness index) and optical features (ALB intensity and spectral bands) were the most significant discriminators in each classification case. Binary classification models were more effective (93.3% accuracy in the presence/absence of Zostera marina) compared to advanced multi-class models (43.3% for EUNIS Level 4/5), which identified the inherent equilibrium between ecological complexity and map validity. Change detection between contemporary and 1957 habitat data revealed extensive Zostera marina loss, with 84.1–99.0% cover reduction across modeling frameworks. Seagrass coverage declined from 61.15% of the study area to just 9.70% or 0.63%, depending on the model. Seasonal mismatch may inflate loss estimates by 5–15%, but even adjusted values (70–94%) indicate severe ecosystem degradation. Spatial exchange components exhibited patterns of habitat change, whereas net losses in total were many orders of magnitude larger than any redistribution in space. These findings recorded the most severe seagrass habitat destruction ever described within Baltic Sea ecosystems and emphasize the imperative for conservation action at the landscape level. The methodology framework provides a reproducible model for analogous change detection analysis in shallow nearshore habitats, creating critical baselines to inform restoration planning and biodiversity conservation activities. It also demonstrated both the capabilities and limitations of automatic techniques for habitat monitoring. Full article

(1) Background: Understanding neural dynamics during human movement is a core neuroscience objective, yet there are fundamental challenges to the collection of high-fidelity neuroelectric signals during motion. We investigated the effects of electroencephalography (EEG) electrode design for cleaning high-density EEG, using an electrical testbed that mimicked the human head. (2) Methods: We used a 60-channel high-density array of tripolar concentric ring electrodes and conventional disk electrodes to compare the recovery of simulated brainwave activity in the presence of electrical neck muscle artifacts during walking. Simulated brainwave activity consisted of randomly occurring sinusoidal bursts with unique frequency content within human EEG spectral bands (5–37 Hz). Electrical neck muscle activity was recorded from a human subject during walking and broadcast into the head phantom device at scaled surface recording amplitudes (0× 0.5× 0.67×, 1×, 1.5×, 2×). We compared the number and spatial distribution of detected neural sources among electrode channels based on spectral power. (3) Results: At low muscle activation amplitudes, conventional electrodes identified more spectral power peaks (p ≤ 0.01) among more electrodes (p < 0.05) compared to tripolar concentric ring electrodes, indicating poorer spatial selectivity. At greater muscle artifact amplitudes, conventional electrodes identified fewer neural spectral power peaks (p < 0.05) with lesser localization accuracy (p < 0.05) compared to tripolar concentric ring electrodes. (4) Conclusions: We identified improved myoelectric artifact removal from tripolar concentric ring electrode recordings compared to conventional electrodes, offering a promising approach for recovering high-fidelity electrocortical activity from human subjects during locomotion. Full article

Non-destructive assessment of kiwifruit quality is critical for postharvest preservation and grading. This paper proposes a novel quantitative evaluation method for the kiwifruit comprehensive quality index (KCQI) during shelf life, based on hyperspectral imaging (HSI) combined with a one-dimensional convolutional neural network (1D-CNN). Hyperspectral images of two kiwifruit cultivars were acquired at four shelf-life stages using an HSI system, and six quality parameters were measured as reference standards. Based on correlation and factor analyses, five key parameters—soluble solids content, firmness, L*, b*, and chroma—were selected to construct the KCQI. Three spectral band selection methods and three modeling algorithms were compared, with the competitive adaptive reweighted sampling (CARS)–1D-CNN model yielding the highest prediction accuracy ($R_P^2$ = 0.82, RMSEP = 0.26, RPD_P = 2.39). Subsequently, a spatial distribution map was generated to visualize the KCQI. These results demonstrate the potential of the HSI–1D-CNN approach for accurate postharvest quality monitoring and intelligent grading. Full article

The scarcity of labeled hyperspectral video samples has motivated existing methods to leverage RGB-pretrained networks; however, many existing methods of hyperspectral object tracking (HOT) select only three representative spectral bands from hyperspectral images, leading to spectral information loss and weakened target discrimination. To address this issue, we propose the Band and Context Refinement Network (BCR-Net) for HOT. Firstly, we design a band importance learning module to partition hyperspectral images into multiple false-color images for pre-trained backbone network. Specifically, each hyperspectral band is expressed as a non-negative linear combination of other bands to form a correlation matrix. This correlation matrix is used to guide an importance ranking of the bands, enabling the grouping of bands into false-color images that supply informative spectral features for the multi-branch tracking framework. Furthermore, to exploit spectral–spatial relationships and contextual information, we design a Contextual Feature Refinement Module, which integrates multi-scale fusion and context-aware optimization to improve feature discrimination. Finally, to adaptively fuse multi-branch features according to band importance, we employ a correlation matrix-guided fusion strategy. Extensive experiments on two public hyperspectral video datasets show that BCR-Net achieves competitive performance compared with existing classical tracking methods. Full article

Weeding in melon and watermelon fields requires selective and pinpoint operation because the crop plants are sensitive to herbicides and tend to grow on the ground in all directions. Hyperspectral images have high spectral and spatial resolution, enabling an object’s classification according to its spectral properties. Spectral band selection is a common practice with hyperspectral images, as it reduces the number of bands in use with only a minor effect on the results. This study’s innovative contribution is the development and validation of a practical methodology to simplify complex hyperspectral data for real-world robotic weed management. This includes the introduction of the ‘normalized crop sample index’ (NCSI) to guide band selection and the use of machine learning methods, which revealed a set of four spectral bands—480 nm, 550 nm, 686 nm and 750 nm—that hold sufficient discriminating information between weeds and watermelon crop. This minimal set of bands enables the simulation and future development of a low-cost, high-speed multispectral camera system. An XGBoost model showed the lowest misclassification error level of 2–14%. The selected spectral bands were used to extract single-band images from the hyperspectral cube. In these images, vegetation pixels were separated using a normalized difference vegetation index filter, and each pixel was classified into a crop or weed class. The classified pixels were grouped into segmented objects, and weeding points were selected, suitable for robotic pinpoint operation. Full article

Satellite remote sensing has emerged as a reliable and cost-effective approach for monitoring inland water quality, offering spatial and temporal advantages over traditional in situ methods. Lake Victoria, the largest tropical lake and a critical freshwater resource for East Africa, faces increasing eutrophication driven by nutrient inflows from agriculture, urbanization, and industrial activities. This study assessed the spatiotemporal dynamics of water quality along Uganda’s Lake Victoria coast by integrating field measurements (2014–2024) with Landsat 8/9 imagery. Chlorophyll-a, a proxy for algal blooms, and Secchi disk depth, an indicator of water clarity, were selected as key parameters. Cloud-free satellite images were processed using the Dark Object Subtraction method, and spectral reflectance values were correlated with field data. Linear regression models from single bands and band ratios showed strong performance, with adjusted R² values of up to 0.88. When tested on unseen data, the models achieved R² values above 0.70, confirming robust predictive ability. Results revealed high algal concentrations for nearshore and clearer offshore waters. These models provide an efficient framework for monitoring eutrophication, guiding restoration priorities, and supporting sustainable water management in Lake Victoria. Full article

Quercetin, a key flavonoid in Anoectochilus roxburghii (Wall.) Lindl., plays an important role in determining the pharmacological value of this medicinal herb. However, traditional methods for quercetin quantification are destructive and time-consuming, limiting their application in real-time quality monitoring. This study investigates the hyperspectral response characteristics of quercetin using near-infrared hyperspectral imaging and establishes a feature-based model to explore its detectability in A. roxburghii leaves. We scanned standard quercetin solutions of known concentration under the same imaging conditions as the leaves to produce a dilution series. Feature-selection methods used included the successive projections algorithm (SPA), Pearson correlation, and competitive adaptive reweighted sampling (CARS). A 1D convolutional neural network (1D-CNN) trained on SPA-selected wavelengths yielded the best prediction performance. These key wavelengths—particularly the 923 nm band—showed strong theoretical and statistical relevance to quercetin’s molecular absorption. When applied to plant leaf spectra, the standard-trained model produced continuous predicted quercetin values that effectively distinguished cultivars with varying flavonoid contents. PCA visualization and ROC-based classification confirmed spectral transferability and potential for functional evaluation. This study demonstrates a non-destructive, spatially resolved, and biochemically interpretable strategy for identifying bioactive markers in plant tissues, offering a methodological basis for future hyperspectral inversion studies and intelligent quality assessment in herbal medicine. Full article

Accurate segmentation of medical images is essential for clinical diagnosis and treatment planning. Hyperspectral imaging (HSI), with its rich spectral information, enables improved tissue characterization and structural localization compared with traditional grayscale or RGB imaging. However, the effective modeling of both spatial and spectral dependencies remains a significant challenge, particularly in small-scale medical datasets. In this study, we propose GSA-Net, a 3D segmentation framework that integrates Gated Spectral-Axial Attention (GSA) to capture long-range interband dependencies and enhance spectral feature discrimination. The GSA module incorporates multilayer perceptrons (MLPs) and adaptive LayerScale mechanisms to enable the fine-grained modulation of spectral attention across feature channels. We evaluated GSA-Net on a hyperspectral cholangiocarcinoma (CCA) dataset, achieving an average Intersection over Union (IoU) of 60.64 ± 14.48%, Dice coefficient of 74.44 ± 11.83%, and Hausdorff Distance of 76.82 ± 42.77 px. It outperformed state-of-the-art baselines. Further spectral analysis revealed that informative spectral bands are widely distributed rather than concentrated, and full-spectrum input consistently outperforms aggressive band selection, underscoring the importance of adaptive spectral attention for robust hyperspectral medical image segmentation. Full article

As climate extremes increasingly threaten global food security, precision tools for early detection of crop stress have become vital, particularly for root crops such as potato (Solanum tuberosum L.) and sweet potato (Ipomoea batatas L. Lam.), which are especially susceptible to environmental stressors throughout their life cycles. In this study, plants were monitored from the initial onset of seasonal stressors, including spring drought, heat, and episodes of excessive rainfall, through to harvest, capturing the full range of physiological and biochemical responses under seasonal, simulated conditions in greenhouses. The spectral data were obtained from regions of interest (ROIs) of each cultivar’s leaves, with over 3000 data points extracted per cultivar; these data were subsequently used for model development. A comprehensive classification framework was established by employing machine learning models, Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), and Partial Least Squares-Discriminant Analysis (PLS-DA), to detect stress across various growth stages. Furthermore, severity levels were objectively defined using photoreflectance indices and principal component analysis (PCA) data visualizations, which enabled consistent and reliable classification of stress responses in both individual cultivars and combined datasets. All models achieved high classification accuracy (90–98%) on independent test sets. The application of the Successive Projections Algorithm (SPA) for variable selection significantly reduced the number of wavelengths required for robust stress classification, with SPA-PLS-DA models maintaining high accuracy (90–96%) using only a subset of informative bands. Furthermore, SPA-PLS-DA-based chemical imaging enabled spatial mapping of stress severity within plant tissues, providing early, non-invasive insights into physiological and biochemical status. These findings highlight the potential of integrating hyperspectral imaging and machine learning for precise, real-time crop monitoring, thereby contributing to sustainable agricultural management and reduced yield losses. Full article

Cloud detection is an indispensable step in satellite remote sensing of cloud properties and objects under the influence of cloud occlusion. Nevertheless, interfering targets such as snow and haze pollution are easily misjudged as clouds for most of the current algorithms. Hence, a robust cloud detection algorithm is urgently needed, especially for regions with high latitudes or severe air pollution. This paper demonstrated that the passive satellite detector Advanced Geosynchronous Radiation Imager (AGRI) onboard the FY-4A satellite has a great possibility to misjudge the dense aerosols in haze pollution as clouds during the daytime, and constructed an algorithm based on the spectral information of the AGRI’s 14 bands with a concise and high-speed calculation. This study adjusted the previously proposed cloud mask rectification algorithm of Moderate-Resolution Imaging Spectroradiometer (MODIS), rectified the MODIS cloud detection result, and used it as the accurate cloud mask data. The algorithm was constructed based on adjusted Fisher discrimination analysis (AFDA) and spectral spatial variability (SSV) methods over four different underlying surfaces (land, desert, snow, and water) and two seasons (summer and winter). This algorithm divides the identification into two steps to screen the confident cloud clusters and broken clouds, which are not easy to recognize, respectively. In the first step, channels with obvious differences in cloudy and cloud-free areas were selected, and AFDA was utilized to build a weighted sum formula across the normalized spectral data of the selected bands. This step transforms the traditional dynamic-threshold test on multiple bands into a simple test of the calculated summation value. In the second step, SSV was used to capture the broken clouds by calculating the standard deviation (STD) of spectra in every 3 × 3-pixel window to quantify the spectral homogeneity within a small scale. To assess the algorithm’s spatial and temporal generalizability, two evaluations were conducted: one examining four key regions and another assessing three different moments on a certain day in East China. The results showed that the algorithm has an excellent accuracy across four different underlying surfaces, insusceptible to the main interferences such as haze and snow, and shows a strong detection capability for broken clouds. This algorithm enables widespread application to different regions and times of day, with a low calculation complexity, indicating that a new method satisfying the requirements of fast and robust cloud detection can be achieved. Full article