Search Results (19,165)

Cotton is a globally strategic crop that plays a crucial role in sustaining national economies and livelihoods. To address the challenges of accurate cotton field extraction in the complex planting environments of Xinjiang’s Alaer reclamation area, a cotton field identification model was developed that integrates multi-source satellite remote sensing data with machine learning methods. Using imagery from Sentinel-2, GF-1, and Landsat 8, we performed feature fusion using principal component, Gram–Schmidt (GS), and neural network techniques. Analyses of spectral, vegetation, and texture features revealed that the GS-fused blue bands of Sentinel-2 and Landsat 8 exhibited optimal performance, with a mean value of 16,725, a standard deviation of 2290, and an information entropy of 8.55. These metrics improved by 10,529, 168, and 0.28, respectively, compared with the original Landsat 8 data. In comparative classification experiments, the endmember-based random forest classifier (RFC) achieved the best traditional classification performance, with a kappa value of 0.963 and an overall accuracy (OA) of 97.22% based on 250 samples, resulting in a cotton-field extraction error of 38.58 km². By enhancing the deep learning model, we proposed a U-Net architecture that incorporated a Convolutional Block Attention Module and Atrous Spatial Pyramid Pooling. Using the GS-fused blue band data, the model achieved significantly improved accuracy, with a kappa coefficient of 0.988 and an OA of 98.56%. This advancement reduced the area estimation error to 25.42 km², representing a 34.1% decrease compared with that of the RFC. Based on the optimal model, we constructed a digital map of continuous cotton cropping from 2021 to 2023, which revealed a consistent decline in cotton acreage within the reclaimed areas. This finding underscores the effectiveness of crop rotation policies in mitigating the adverse effects of large-scale monoculture practices. This study confirms that the synergistic integration of multi-source satellite feature fusion and deep learning significantly improves crop identification accuracy, providing reliable technical support for agricultural policy formulation and sustainable farmland management. Full article

Accurate cerebral vasculature segmentation using digital subtraction angiography (DSA) is critical for diagnosing and treating cerebrovascular diseases. However, conventional single-frame analysis methods often fail to capture fine vascular structures due to background noise, overlapping anatomy, and dynamic contrast flow. In this study, we propose a novel vessel-enhancing preprocessing technique using temporal differencing of DSA sequences to improve cerebrovascular segmentation accuracy. Our method emphasizes contrast flow dynamics while suppressing static background components by computing absolute differences between sequential DSA frames. The enhanced images were input into state-of-the-art deep learning models, U-Net++ and DeepLabv3+, for vascular segmentation. Quantitative evaluation of the publicly available DIAS dataset demonstrated significant segmentation improvements across multiple metrics, including the Dice Similarity Coefficient (DSC), Intersection over Union (IoU), and Vascular Connectivity (VC). Particularly, DeepLabv3+ with the proposed preprocessing achieved a DSC of 0.83 ± 0.05 and VC of 44.65 ± 0.63, outperforming conventional methods. These results suggest that leveraging temporal information via input enhancement substantially improves small and complex vascular structure extraction. Our approach is computationally efficient, model-agnostic, and clinically applicable for DSA. Full article

Soil water content (SWC) critically governs the physical and mechanical behavior of soils. However, conventional methods such as oven drying are laborious, time-consuming, and difficult to replicate in the field. To overcome these limitations, we developed an image-based interpolation framework that leverages histogram statistics from 12 soil surface photographs spanning 3.83% to 19.75% SWC under controlled lighting. For each image, pixel-level values of red, green, blue (RGB) channels and hue, saturation, value (HSV) channels were extracted to compute per-channel histograms, whose empirical means and standard deviations were used to parameterize Gaussian probability density functions. Linear interpolation of these parameters yielded synthetic histograms and corresponding images at 1% SWC increments across the 4–19% range. Validation against the original dataset, using dice score (DS), Bhattacharyya distance (BD), and Earth Mover’s Distance (EMD) metrics, demonstrated that the interpolated images closely matched observed color distributions. Average BD was below 0.014, DS above 0.885, and EMD below 0.015 for RGB channels. For HSV channels, average BD was below 0.074, DS above 0.746, and EMD below 0.022. These results indicate that the proposed method reliably generates intermediate SWC data without additional direct measurements, especially with RGB. By reducing reliance on exhaustive sampling and offering a cost-effective dataset augmentation, this approach facilitates large-scale, noninvasive soil moisture estimation and supports machine learning applications where field data are scarce. Full article

Accurate postharvest quality evaluation of soybeans is essential for preserving product value and meeting industry standards. Traditional inspection methods are often inconsistent, labor-intensive, and unsuitable for high-throughput operations. This study presents a non-destructive soybean classification approach using a simplified reflectance-mode hyperspectral imaging system equipped with a single light source, eliminating the complexity and maintenance demands of dual-light configurations used in prior studies. A spectral–spatial data fusion strategy was developed to classify harvested soybeans into four categories: normal, split, diseased, and foreign materials such as stems and pods. The dataset consisted of 1140 soybean samples distributed across these four categories, with spectral reflectance features and spatial texture attributes extracted from each sample. These features were combined to form a unified feature representation for use in classification. Among multiple machine learning classifiers evaluated, Linear Discriminant Analysis (LDA) achieved the highest performance, with approximately 99% accuracy, 99.05% precision, 99.03% recall and 99.03% F1-score. When evaluated independently, spectral features alone resulted in 98.93% accuracy, while spatial features achieved 78.81%, highlighting the benefit of the fusion strategy. Overall, this study demonstrates that a single-illumination HSI system, combined with spectral–spatial fusion and machine learning, offers a practical and potentially scalable approach for non-destructive soybean quality evaluation, with applicability in automated industrial processing environments. Full article

Background/Objectives: Cardiac sarcoidosis (CS) is an inflammatory cardiomyopathy with a strong clinical impact on patients affected by the disease and a challenging diagnosis. Methods: This comprehensive narrative review evaluates the role of [18F]fluorodesoxyglucose ([18F]FDG) positron emission tomography (PET)-based radiomics and machine learning (ML) analyses in the assessment of CS. Results: The value of [18F]FDG PET-based radiomics and ML has been investigated for the clinical settings of diagnosis and prognosis of patients affected by CS. Even though different radiomics features and ML models have proved their clinical role in these settings in different cohorts, the clear superiority and added value of one of them across different studies has not been demonstrated. In particular, textural analysis and ML showed high diagnostic value for the diagnosis of CS in some papers, but had controversial results in other works, and may potentially provide prognostic information and predict adverse clinical events. When comparing these analyses with the classic semiquantitative evaluation, a conclusion about which method best suits the final objective cannot be drawn with the available references. Different methodological issues are present when comparing different papers, such as image segmentation and feature extraction differences that are more evident. Furthermore, the intrinsic limitations of radiomics analysis and ML need to be overcome with future research developed in multicentric settings with protocol harmonization. Conclusions: [18F]FDG PET-based radiomics and ML show preliminary promising results for CS evaluation, but remain investigational tools since the current evidence is insufficient for clinical adoption due to methodological heterogeneity, small sample sizes, and lack of standardization. Full article

We present some numerical results on piston and tilt detection by using the Young experiment with Genetic Algorithms (GAs). We have simulated the cophasing of a flat surface by following the experimental setup and the mathematical model for Optical Path Difference (OPD) in the Young experiment to characterize piston and tip–tilt misalignment images in the order of a few nanometers, considering diffraction effects and random noise of 5%. Thus, the best fitness obtained by the genetic algorithm is considered as a determining factor to decide a complete error measurement because the proposed algorithm is capable of extracting the values of piston and tilt separately, regardless of which error is present or both. As a result, we have developed a study on piston detection from (0.001, 10) mm with a tilt present in the same pattern from (0, $\lambda /2$) by using GAs embedded in a computational application. Full article

Optical coherence tomography (OCT) is a leading imaging technique for diagnosing retinal disorders such as age-related macular degeneration and diabetic retinopathy. Its ability to detect structural changes, especially in the optic nerve head, has made it vital for early diagnosis and monitoring. This paper surveys techniques for ocular disease prediction using OCT, focusing on both hand-crafted and deep learning-based feature extractors. While the field has seen rapid growth, a detailed comparative analysis of these methods has been lacking. We address this by reviewing research from the past 20 years, evaluating methods based on accuracy, sensitivity, specificity, and computational cost. Key diseases examined include glaucoma, diabetic retinopathy, cataracts, amblyopia, and macular degeneration. We also assess public OCT datasets widely used in model development. A unique contribution of this paper is the exploration of adversarial attacks targeting OCT-based diagnostic systems and the vulnerabilities of different feature extraction techniques. We propose a practical, robust defense strategy that integrates with existing models and outperforms current solutions. Our findings emphasize the value of combining classical and deep learning methods with strong defenses to enhance the security and reliability of OCT-based diagnostics, and we offer guidance for future research and clinical integration. Full article

Pipelines play a vital role in material transportation within industrial settings. This review synthesizes detection technologies for early-stage small gas leaks from pipelines in the industrial sector, with a focus on acoustic-based methods, optical gas imaging (OGI), and multimodal fusion approaches. It encompasses detection principles, inherent challenges, mitigation strategies, and the state of the art (SOTA). Small leaks refer to low flow leakage originating from defects with apertures at millimeter or submillimeter scales, posing significant detection difficulties. Acoustic detection leverages the acoustic wave signals generated by gas leaks for non-contact monitoring, offering advantages such as rapid response and broad coverage. However, its susceptibility to environmental noise interference often triggers false alarms. This limitation can be mitigated through time-frequency analysis, multi-sensor fusion, and deep-learning algorithms—effectively enhancing leak signals, suppressing background noise, and thereby improving the system’s detection robustness and accuracy. OGI utilizes infrared imaging technology to visualize leakage gas and is applicable to the detection of various polar gases. Its primary limitations include low image resolution, low contrast, and interference from complex backgrounds. Mitigation techniques involve background subtraction, optical flow estimation, fully convolutional neural networks (FCNNs), and vision transformers (ViTs), which enhance image contrast and extract multi-scale features to boost detection precision. Multimodal fusion technology integrates data from diverse sensors, such as acoustic and optical devices. Key challenges lie in achieving spatiotemporal synchronization across multiple sensors and effectively fusing heterogeneous data streams. Current methodologies primarily utilize decision-level fusion and feature-level fusion techniques. Decision-level fusion offers high flexibility and ease of implementation but lacks inter-feature interaction; it is less effective than feature-level fusion when correlations exist between heterogeneous features. Feature-level fusion amalgamates data from different modalities during the feature extraction phase, generating a unified cross-modal representation that effectively resolves inter-modal heterogeneity. In conclusion, we posit that multimodal fusion holds significant potential for further enhancing detection accuracy beyond the capabilities of existing single-modality technologies and is poised to become a major focus of future research in this domain. Full article

Accurate and non-destructive assessment of fruit firmness is critical for evaluating quality and ripeness, particularly in postharvest handling and supply chain management. This study presents the development of an image-based vibration analysis system for evaluating the firmness of kiwifruit using computer vision and machine learning. In the proposed setup, 120 kiwifruits were subjected to controlled excitation in the frequency range of 200–300 Hz using a vibration motor. A digital camera captured surface displacement over time (for 20 s), enabling the extraction of key dynamic features, namely, the damping coefficient (damping is a measure of a material’s ability to dissipate energy) and natural frequency (the first peak in the frequency spectrum), through image processing techniques. Results showed that firmer fruits exhibited higher natural frequencies and lower damping, while softer, more ripened fruits showed the opposite trend. These vibration-based features were then used as inputs to a feed-forward backpropagation neural network to predict fruit firmness. The neural network consisted of an input layer with two neurons (damping coefficient and natural frequency), a hidden layer with ten neurons, and an output layer representing firmness. The model demonstrated strong predictive performance, with a correlation coefficient (R²) of 0.9951 and a root mean square error (RMSE) of 0.0185, confirming its high accuracy. This study confirms the feasibility of using vibration-induced image data combined with machine learning for non-destructive firmness evaluation. The proposed method provides a reliable and efficient alternative to traditional firmness testing techniques and offers potential for real-time implementation in automated grading and quality control systems for kiwi and other fruit types. Full article

Nanoparticle-embedded polymeric materials are an important subject in advanced structural applications due to their advantageous combination of low weight and high mechanical performance. Optical coherence tomography (OCT) is a high-resolution imaging technique that enables subsurface defect visualization, which can be used as one of the methods to reveal defects resulting from decomposition pathways or mechanisms of polymers. Nevertheless, the low contrast of polymeric materials, particularly PEEK-based polymers, does not allow for automatic geometry extraction for analytical input. To address the constraint of weak contrast, localized surface plasmon resonance (LSPR) of plasmonic nanoparticle-reinforced polymer materials has been used as an OCT contrast agent to provide the necessary contrast. The backscattering efficiency of light was also theoretically investigated, based on the Lorenz–Mie theory, with a single spherical nanoparticle embedded in a PEEK matrix as a non-absorptive, isotropic and homogeneous medium. In this study, the cases of a single homogeneous $Ti{O}_2$ nanoparticle and a hybrid $Ti{O}_2/Au$ core/shell nanoparticle configuration were considered separately. An examination of the influence of nanoparticle diameter and gold shell thickness on backscattering efficiencies of these nanostructures was performed. The results indicate that $Ti{O}_2/Au$ nanoshells demonstrate superior near-infrared (NIR) light backscattering capabilities at typical OCT operating wavelengths (830–1310 nm). Additionally, the potential of these nanoparticles for application in non-destructive testing-based light backscattering methods was investigated. The findings suggest that $Ti{O}_2/Au$ nanoshells have the ability to effectively backscatter near-infrared light in OCT operating central wavelengths, making them suitable to serve as effective NIR contrast-enhancing agents for OCT within the domain of NDT. Full article

This study presents a novel deep learning framework for classifying visual images based on brain responses recorded through electroencephalogram (EEG) signals. The primary challenge in EEG-based visual pattern recognition lies in the inherent spatiotemporal variability of neural signals across different individuals and recording sessions, which severely limits the generalization capabilities of classification models. Our work specifically addresses the task of identifying which image category a person is viewing based solely on their recorded brain activity. The proposed methodology incorporates three primary components: first, a brain hemisphere asymmetry-based dimensional reduction approach to extract discriminative lateralization features while addressing high-dimensional data constraints; second, an advanced channel selection algorithm utilizing Fisher score methodology to identify electrodes with optimal spatial representativeness across participants; and third, a Dynamic Temporal Warping (DTW) alignment technique to synchronize temporal signal variations with respect to selected reference channels. Comprehensive experimental validation on a visual image classification task using a publicly available EEG-based visual classification dataset, ImageNet-EEG, demonstrates that the proposed disambiguation framework substantially improves classification accuracy while simultaneously enhancing model convergence characteristics. The integrated approach not only outperforms individual component implementations but also accelerates the learning process, thereby reducing training data requirements for EEG-based applications. These findings suggest that systematic spatiotemporal disambiguation represents a promising direction for developing robust and generalizable EEG classification systems across diverse neurological and brain–computer interface applications. Full article

The graphite mineral processing workshop involves complex procedures and generates a large amount of dust and smoke during operation. This particulate matter significantly degrades the quality of indoor surveillance video frames, thereby affecting subsequent tasks such as image segmentation and recognition. Existing image dehazing algorithms often suffer from insufficient feature extraction or excessive computational cost, which limits their real-time applicability and makes them unsuitable for deployment in graphite processing environments. To address this issue, this paper proposes a CNN-based dehazing algorithm tailored for dust and haze removal in graphite mineral processing workshops. Experimental results on a synthetic haze dataset constructed for graphite processing scenarios demonstrate that the proposed method achieves higher PSNR and SSIM compared to existing deep learning-based dehazing approaches, resulting in improved visual quality of dehazed images. Full article

Traditional information distillation networks using single-scale convolution and simple feature fusion often result in insufficient information extraction and ineffective restoration of high-frequency details. To address this problem, we propose a lightweight image super-resolution reconstruction network based on multi-order information optimization. The core of this network lies in the enhancement and refinement of high-frequency information. Our method operates through two main stages to fully exploit the high-frequency features in images while eliminating redundant information, thereby enhancing the network’s detail restoration capability. In the high-frequency information enhancement stage, we design a self-calibration high-frequency information enhancement block. This block generates calibration weights through self-calibration branches to modulate the response strength of each pixel. It then selectively enhances critical high-frequency information. Additionally, we combine an auxiliary branch and a chunked space optimization strategy to extract local details and adaptively reinforce high-frequency features. In the high-frequency information refinement stage, we propose a multi-scale high-frequency information refinement block. First, multi-scale information is captured through multiplicity sampling to enrich the feature hierarchy. Second, the high-frequency information is further refined using a multi-branch structure incorporating wavelet convolution and band convolution, enabling the extraction of diverse detailed features. Experimental results demonstrate that our network achieves an optimal balance between complexity and performance, outperforming popular lightweight networks in both quantitative metrics and visual quality. Full article

Image captioning is an important task in cross-modal research in numerous applications. Image captioning aims to capture the semantic content of an image and express it in a linguistically and contextually appropriate sentence. However, existing models mostly trend to focus on a topic generated by the most conspicuous foreground objects. Thus, other topics in the image are often ignored. To address these limitations, we propose a model that can generate richer semantic content and more diverse captions. The proposed model can capture not only main topics using coarse-grained objects but also finds fine-grained visual information from background or minor foreground objects. Our image captioning system combines the ResNet, LSTM, and topic feature models. The ResNet model extracts fine-grained image features and enriches the description of objects. The LSTM model provides a longer context for semantics, increasing the fluency and semantic completeness of the generated sentences. The topic model determines multiple topics based on the image and text content. The topics provide directions for the model to generate different sentences. We evaluate our model on the MSCOCO dataset. The results show that compared with other models, our model achieves a certain improvement in higher-order BLEU scores and a significant improvement in CIDEr score. Full article

Desertification poses critical threats to agricultural productivity and socio-economic stability, particularly in vulnerable regions like Thatta and Badin districts of Sindh, Pakistan. Traditional monitoring methods lack the accuracy and temporal resolution needed for effective early warning systems. This study presents a novel Spatio-Temporal Desertification Predictor (STDP) framework that integrates deep learning with next-generation satellite imaging for time-series desertification forecasting. The proposed encoder–decoder architecture combines Convolutional Neural Networks (CNNs) for spatial feature extraction from high-resolution satellite imagery with modified Long Short-Term Memory (LSTM) networks enhanced by multi-head attention to capture temporal dependencies. Environmental variables are fused through an adaptive data integration layer, and hyperparameter optimization is employed to enhance model performance for edge computing deployment. Experimental validation on a 15-year satellite dataset (2010–2024) demonstrates superior performance with MSE = 0.018, MAE = 0.079, and $R^2=0.94$, outperforming traditional CNN-only, LSTM-only, and hybrid baselines by 15–20% in prediction accuracy. The framework forecasts desertification trends through 2030, providing actionable signals for environmental management and policy-making. This work advances the integration of AI with satellite-based Earth observation, offering a scalable path for real-time environmental monitoring in IoT and edge computing infrastructures. Full article