Search Results (191)

Visual mental imagery, the ability to generate and manipulate internal visual experiences without direct sensory input, links perception with memory, planning, and higher cognition. In this targeted narrative review, we synthesize neuroimaging and lesion evidence on the brain basis of visual imagery, with a focus on neuroradiological correlates of the ventral and dorsal visual pathways. Unlike prior cognitive neuroscience reviews that primarily emphasize functional mechanisms, this review is neuroradiology-oriented and integrates lesion patterns and white-matter disconnection to support clinico-radiological interpretation of imagery complaints. Using a dual-stream framework, we contrast ventral occipito-temporal systems that preferentially support object imagery (appearance-based features such as form, faces/objects, and color, with texture remaining under-studied) with dorsal occipito-parietal systems that preferentially support spatial imagery (relations, transformations, and navigation). Across studies, imagery recruitment is strongly task- and stage-dependent: ventral regions are most often engaged during object-focused imagery, whereas parietal regions are prominent during spatial transformation tasks, with evidence for interaction between pathways when demands require both content and spatial operations. Structural and clinico-radiological findings indicate that imagery impairment can arise from focal posterior lesions and posterior neurodegenerative syndromes but also from network disruption affecting long-range connections that support top-down access to posterior representations. Finally, emerging work on aphantasia and hyperphantasia supports a network-level view in which imagery vividness relates to how effectively higher-order systems engage visual representations. We conclude that standardized, stream-sensitive tasks and multimodal approaches combining functional and structural imaging with lesion-based evidence are key to discovering clinically actionable biomarkers of imagery dysfunction. Full article

In recent years, imaginative techniques have effectively addressed the growing demand for brief, evidence-based treatments applicable in various contexts. Among these, Imagery with Rescripting (ImRs) was developed within the Schema Therapy model. ImRs can be applied individually or in combination with other protocols, demonstrating significant outcomes even after just one session. This narrative review aims to provide an overview of the applications of ImRs, with a specific focus on its effectiveness in trauma-related disorders. The search string used was “(‘imagery with Rescripting’) AND ((‘Trauma’ OR ‘PTSD’ OR ‘dissociation’))”. The following databases were utilized: PubMed, Scopus, Web of Science, Medline, Embase, and PsychInfo. The research included English-language and Italian-language studies, encompassing experimental and observational designs, case reports, and case series. Samples consisted of healthy participants or clinical populations aged 18 years and older, with no temporal limitations. A total of 56 articles were selected. The results highlight the efficacy of this intervention, whether administered individually or as part of combined protocols, across a wide range of diagnostic categories, including healthy samples, post-traumatic stress disorder (PTSD), borderline personality disorder (BDP), sleep disorders, psychotic spectrum disorders, chronic pain, anxiety disorders, depression, and eating disorders. The studies also support hypotheses about the mechanisms underlying the technique: ImRs facilitates the reprocessing of the meaning associated with mental representations and reduces the occurrence of negative intrusive images related to past events. This process alters and rewrites the individual’s negative memories and images. The narrative review supports the effectiveness of ImRs in treating various psychopathological disorders, both trauma-related and non-trauma-related. In addition to highlighting the effectiveness of ImRs when appropriately integrated with other techniques, the review emphasizes the importance of conducting efficacy studies on larger samples to evaluate ImRs as a standalone intervention model. Full article

Effective interpretation of coherent and usable information from aerial images (e.g., satellite imagery or high-altitude drone photography) can greatly reduce human effort in many situations, both natural (e.g., earthquakes, forest fires, tsunamis) and man-made (e.g., highway pile-ups, traffic congestion), particularly in disaster management. This research proposes a novel encoder–decoder framework for captioning of remote sensing images that integrates self-supervised DINOv3 visual features with a hybrid Transformer–LSTM decoder. Unlike existing approaches that rely on supervised CNN-based encoders (e.g., ResNet, VGG), the proposed method leverages DINOv3’s self-supervised learning capabilities to extract dense, semantically rich features from aerial images without requiring domain-specific labeled pretraining. The proposed hybrid decoder combines Transformer layers for global context modeling with LSTM layers for sequential caption generation, producing coherent and context-aware descriptions. Feature extraction is performed using the DINOv3 model, which employs the gram-anchoring technique to stabilize dense feature maps. Captions are generated through a hybrid of Transformer with Long Short-Term Memory (LSTM) layers, which adds contextual meaning to captions through sequential hidden layer modeling with gated memory. The model is first evaluated on two traditional remote sensing image captioning datasets: RSICD and UCM-Captions. Multiple evaluation metrics like Bilingual Evaluation Understudy (BLEU), Consensus-based Image Description Evaluation (CIDEr), Recall-Oriented Understudy for Gisting Evaluation (ROUGE-L), and Metric for Evaluation of Translation with Explicit Ordering (METEOR), are used to quantify the performance and robustness of the proposed DINOv3 hybrid model. The proposed model outperforms conventional Convolutional Neural Network (CNN) and Vision Transformers (ViT)-based models by approximately 9–12% across most evaluation metrics. Attention heatmaps are also employed to qualitatively validate the proposed model when identifying and describing key spatial elements. In addition, the proposed model is evaluated on advanced remote sensing datasets, including RSITMD, DisasterM3, and GeoChat. The results demonstrate that self-supervised vision transformers are robust encoders for multi-modal understanding in remote sensing image analysis and captioning. Full article

Accurate crack segmentation in civil infrastructure imagery remains challenging because of the prevalence of thin, low-contrast, and spatially discontinuous defects that often appear amid textured surfaces, shadows, and acquisition noise. Although Transformer-based models improve global context modeling, many existing solutions incur substantial computational and memory overhead, which limits their use in practical, resource-constrained inspection settings. In this work, we introduce an efficient hybrid segmentation architecture that combines a convolutional encoder for high-fidelity local representation with a lightweight Transformer bottleneck for global dependency modeling, followed by a progressive decoder that restores spatial resolution through multi-level skip-feature fusion. To better accommodate severe foreground sparsity and preserve fine crack structures, the framework is trained with a composite Dice–Binary Cross-Entropy objective and employs a tokenization strategy designed to preserve fine spatial details while enabling efficient global context modeling. We validate the proposed approach on four public benchmarks, demonstrating consistent improvements over representative convolutional, Transformer-based, and hybrid baselines, while ablation studies confirm the contribution of each design component. Finally, runtime profiling shows favorable latency and memory characteristics, supporting real-time or near real-time deployment on embedded and edge inspection platforms. Full article

Accurate river discharge estimation is fundamental for water resource management under increasingly variable hydrological conditions. While conventional in situ techniques remain hydrometric reference standards, their operational deployment is constrained by cost, accessibility, and limited spatial coverage. Advances in remote sensing and artificial intelligence (AI) have introduced non-contact discharge estimation frameworks based on image-derived observations. This systematic review, conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 reporting guidelines, examines the evolution of river discharge measurement methods between 2004 and 2024 through a structured two-stage design. An initial search in Web of Science and Scopus identified 2809 records, of which 249 were retained for first-stage synthesis. A focused second-stage screening isolated seven studies that directly integrate image-based data with machine learning or deep learning architectures for discharge estimation. The analysis reveals a methodological transition from instrument-based hydrometry toward computationally assisted, image-driven approaches. The retained studies employ close-range and satellite imagery combined with Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), and related models. Although reported validation metrics indicate strong predictive capability under specific conditions, performance remains dependent on site-specific calibration and reference discharge records. Broader operational deployment requires improved transferability, uncertainty integration, and cross-basin validation. Full article

Reliable fault detection along transmission corridors is essential for preventing small defects from developing into long outages and costly emergency operations. This study aims to improve the field reliability of an open vocabulary vision language backbone without retraining the large model in an end-to-end manner. The work focuses on four operational fault classes in multi-region corridor imagery collected during routine inspections and uses a Florence-2 vision language model as the base recognizer. On top of this backbone, three domain-specific components are introduced. A subclass-aware fusion scheme keeps probability mass within the active parent concept so that insulator icing and conductor icing produce stable, action-oriented decisions. A Power-Line Focus Then Crop normalization uses an attention-guided corridor window together with isotropic resizing so that thin conductors and small fittings remain visible in the processed image. A corridor geo prior reduces scores as the distance from the mapped centerline increases and in this way suppresses detections that lie outside the corridor. All methods are evaluated under a shared preprocessing and scoring pipeline in training-free and parameter-efficient tuning modes. Experiments on unseen regions show higher accuracy for skinny and low-contrast faults, fewer false alarms outside the right-of-way, and improved score calibration in the confidence range used for triage, while keeping throughput and memory usage suitable for unmanned aerial vehicles and substation edge devices. Full article

Soil salinization is a primary bottleneck for the sustainable development of agriculture in the Yellow River Delta (YRD). Conventional remote sensing monitoring predominantly relies on single-phase instantaneous spectral responses, which are highly vulnerable to transient environmental interferences, such as surface moisture fluctuations. This study proposes a novel predictive framework based on legacy vegetation signals. By integrating multi-temporal Sentinel-2 imagery from the 2024 growing season, we quantified the cumulative physiological feedback of crops from the preceding year and developed a spring soil salinity content (SSC) inversion model for 2025 using the LightGBM algorithm. The results demonstrate that the median compositing technique significantly enhances model robustness against outliers. Furthermore, the optimal time window for capturing these legacy signals for spring salinity monitoring was identified as July to September. Compared with traditional immediate monitoring models, the LightGBM model based on previous-season legacy signals achieved superior predictive accuracy (R² = 0.84), effectively mitigating the impact of stochastic noise. This research validates the critical role of long-term vegetation memory in salinity early warning and provides a robust scientific foundation for the precision management of coastal saline-alkali land. Full article

Multi object tracking (MOT) in UAV imagery is challenged by weak feature representation of small objects due to limited resolution, which leads to frequent missed detections. However, enhancing small object features often amplifies background noise and increases false positives. To address this contradiction, we propose the Temporal Aware Small Object Enhancement Network (TASONet), which integrates spatial enhancement and temporal modeling for robust tracking. The Small Object Enhancement (SOE) module combines depthwise separable convolutions with contrast-aware attention mechanisms (SimAM and LCDAttn) to improve local discriminability. It further incorporates the Small Target Enhancement Path (STEP), which uses motion-difference cues and a confidence adaptive suppression strategy to strengthen spatial features while mitigating noise. The Temporal Enhancement Module (TEM), consisting of Temporal Feature Alignment (TFA) and a Target Memory Unit (TMU), aggregates multi-frame information through adaptive inter-frame fusion and memory of high confidence historical features, improving temporal consistency and reducing false positives potentially introduced by SOE. Experiments show that TASONet achieves significant gains over state-of-the-art methods: on UAVDT, MOTA increases from 68.33 to 75.97 and IDF1 from 83.50 to 88.51; on VisDrone-MOT, MOTA rises from 61.15 to 73.52 with an IDF1 of 88.83. These results validate the effectiveness of jointly enhancing spatial features and temporal coherence for UAV small-object MOT. Full article

In practical underwater object detection tasks, imbalanced sample distribution and the scarcity of samples for certain classes often lead to insufficient model training and limited generalization capability. To address these challenges, this paper proposes FS2-DETR (Few-Shot Detection Transformer for Sonar Images), a transformer-based few-shot object detection network tailored for sonar imagery. Considering that sonar images generally contain weak, small, and blurred object features, and that data scarcity in some classes can hinder effective feature learning, the proposed FS2-DETR introduces the following improvements over the baseline DETR model. (1) Feature Enhancement Compensation Mechanism: A decoder-prediction-guided feature resampling module (DPGFRM) is designed to process the multi-scale features and subsequently enhance the memory representations, thereby strengthening the exploitation of key features and improving detection performance for weak and small objects. (2) Visual Prompt Enhancement Mechanism: Discriminative visual prompts are generated to jointly enhance object queries and memory, thereby highlighting distinctive image features and enabling more effective feature capture for few-shot objects. (3) Multi-Stage Training Strategy: Adopting a progressive training strategy to strengthen the learning of class-specific layers, effectively mitigating misclassification in few-shot scenarios and enhancing overall detection accuracy. Extensive experiments conducted on the improved UATD sonar image dataset demonstrate that the proposed FS2-DETR achieves superior detection accuracy and robustness under few-shot conditions, outperforming existing state-of-the-art detection algorithms. Full article

The Damietta–Port Said coast, Nile Delta, has experienced extreme morphological change over the past four decades due to sediment reduction due to Aswan High Dam and continued anthropogenic pressures. Using multi-temporal Landsat (1985–2025) and high-resolution RapidEye and PlanetScope imagery with 50 m-spaced transects, the study documents major shoreline shifts: the Damietta sand spit retreated by >1 km at its proximal apex while its distal tip advanced by ≈3.1 km southeastward under persistent longshore drift. Sectoral analyses reveal typical structure-induced patterns of updrift accretion (+180 to +210 m) and downdrift erosion (−50 to −330 m). To improve predictive capability beyond linear DSAS extrapolation, Nonlinear Autoregressive Exogenous (NARX) and Bidirectional Long Short-Term Memory (BiLSTM) neural networks were applied to forecast the 2050 shoreline. BiLSTM demonstrated superior stability, capturing nonlinear sediment transport patterns where NARX produced unstable over-predictions. Furthermore, coupled wave–flow modeling validates a sustainable management strategy employing successive short groins (45–50 m length, 150 m spacing). Simulations indicate that this configuration reduces longshore current velocities by 40–60% and suppresses rip-current eddies, offering a sediment-compatible alternative to conventional breakwaters and seawalls. This integrated remote sensing, hydrodynamic, and AI-based framework provides a robust scientific basis for adaptive, sediment-compatible shoreline management, supporting the long-term resilience of one of Egypt’s most vulnerable deltaic coasts under accelerating climatic and anthropogenic pressures. Full article

Electroencephalography-based motor imagery (EEG-MI) classification is a cornerstone of Brain–Computer Interface (BCI) systems, enabling the identification of motor intentions by decoding neural patterns within EEG signals. However, conventional methods, predominantly reliant on convolutional neural networks (CNNs), are proficient at extracting local temporal features but struggle to capture long-range dependencies and global contextual information. To address this limitation, we propose a Triple-path Heterogeneous Feature Collaboration Network (TPHFC-Net), which synergistically integrates three distinct temporal modeling pathways: a multi-scale Temporal Convolutional Network (TCN) to capture fine-grained local dynamics, a Transformer branch to model global dependencies via multi-head self-attention, and a Long Short-Term Memory (LSTM) network to track sequential state evolution. These heterogeneous features are subsequently fused adaptively by a dynamic gating mechanism. In addition, the model’s robustness and discriminative power are further augmented by a lightweight front-end denoising diffusion model for enhanced noisy feature representation and a back-end prototype attention mechanism to bolster the inter-class separability of non-stationary EEG features. Extensive experiments on the BCI Competition IV-2a and IV-2b datasets validate the superiority of the proposed model, achieving mean classification accuracies of 82.45% and 89.49%, respectively, on the subject-dependent MI task and significantly outperforming existing mainstream baselines. Full article

Camellia oleifera, a woody oil crop unique to China, plays a crucial role in alleviating the global pressure of edible oil supply and maintaining ecological security. However, it remains challenging to accurately forecast Camellia oleifera yield in complex landscapes using only remote sensing data. The aim of this study is to develop an interpretable deep learning model, namely Shapley Additive Explanations–guided Attention–long short-term memory (SALSTM), for estimating Camellia oleifera yield by integrating an improved spectral bloom index and environmental parameters. The study area is located in Hengyang City in Hunan Province. Sentinel-2 imagery, meteorological observation from 2019 to 2023, and topographic data were collected. First, an improved spectral bloom index (ISBI) was constructed as a proxy for flowering density, while average temperature, precipitation, accumulated temperature, and wind speed were selected to represent environmental regulation variables. Second, a SALSTM model was designed to capture temporal dynamics from multi-source inputs, in which the LSTM module extracts time-dependent information and an attention mechanism assigns time-step-wise weights. Feature-level importance derived from SHAP analysis was incorporated as a guiding prior to inform attention distribution across variable dimensions, thereby enhancing model transparency. Third, model performance was evaluated using root mean square error (RMSE) and coefficient of determination (R²). The result show that the constructed SALSTM model achieved strong predictive performance in predicting Camellia oleifera yield in Hengyang City (RMSE = 0.5738 t/ha, R² = 0.7943). Feature importance analysis results reveal that ISBI weight > 0.26, followed by average temperature and precipitation from flowering to fruit stages, these features are closely associated with C. oleifera yield. Spatially, high-yield zones were mainly concentrated in the central–southern hilly regions throughout 2019–2023, In contrast, low-yield zones were predominantly distributed in the northern and western mountainous areas. Temporally, yield hotspots exhibited a gradual increasing while low-yield zones showed mild fluctuations. This framework provides an effective and transferable approach for remote sensing-based yield estimation of flowering and fruit-bearing crops in complex landscapes. Full article

This study presents the first application of machine learning (ML) to detect and map mowed tidal wetlands in the Chesapeake Bay region of Maryland and Virginia, focusing on emergent estuarine intertidal (E2EM) wetlands. Monitoring human disturbances like mowing is essential because repeated mowing stresses wetland vegetation, reducing habitat quality and diminishing other ecological services wetlands provide, including shoreline stabilization and water filtration. Traditional field-based monitoring is labor-intensive and impractical for large-scale assessments. To address these challenges, this study utilized 2021 and 2022 Sentinel-2 satellite imagery and a time-series analysis of the Normalized Difference Vegetation Index (NDVI) to distinguish between mowed and unmowed (control) wetlands. A bidirectional Long Short-Term Memory (BiLSTM) neural network was created to predict NDVI patterns associated with mowing events, such as rapid decreases followed by slow vegetation regeneration. The training dataset comprised 204 field-verified and desktop-identified samples, accounting for under 0.002% of the research area’s herbaceous E2EM wetlands. The model obtained 97.5% accuracy on an internal test set and was verified at eight separate Chesapeake Bay locations, indicating its promising generality. This work demonstrates the potential of remote sensing and machine learning for scalable, automated monitoring of tidal wetland disturbances to aid in conservation, restoration, and resource management. Full article

Online visual object tracking is a critical component of remote sensing-based aerial vehicle physical tracking, enabling applications such as environmental monitoring, target surveillance, and disaster response. In real-world remote sensing scenarios, the inherent processing delay of tracking algorithms results in the tracker’s output lagging behind the actual state of the observed scene. This latency not only degrades the accuracy of visual tracking in dynamic remote sensing environments but also impairs the reliability of UAV physical tracking control systems. Although predictive trackers have shown promise in mitigating latency impacts by forecasting target future states, existing methods face two key challenges in remote sensing applications: weak correlation between trackers and predictors, where predictions rely solely on motion information without leveraging rich remote sensing visual features; and inadequate modeling of continuous historical memory from discrete remote sensing data, limiting adaptability to complex spatiotemporal changes. To address these issues, we propose TPC-Tracker, a Tracker-Predictor Correlation Framework tailored for latency compensation in remote sensing-based aerial tracking. A Visual Motion Decoder (VMD) is designed to fuse high-dimensional visual features from remote sensing imagery with motion information, strengthening the tracker-predictor connection. Additionally, the Visual Memory Module (VMM) and Motion Memory Module (M3) model discrete historical remote sensing data into continuous spatiotemporal memory, enhancing predictive robustness. Compared with state-of-the-art predictive trackers, TPC-Tracker reduces the Mean Squared Error (MSE) by up to 38.95% in remote sensing-oriented physical tracking simulations. Deployed on VTOL drones, it achieves stable tracking of remote sensing targets at 80 m altitude and 20 m/s speed. Extensive experiments on public UAV remote sensing datasets and real-world remote sensing tasks validate the framework’s superiority in handling latency-induced challenges in aerial remote sensing scenarios. Full article

High-precision short-term photovoltaic (PV) power prediction has become a critical technology in ensuring grid accommodation capacity, optimizing dispatching decisions, and enhancing plant economic benefits. This paper proposes a long short-term memory (LSTM)-based short-term PV power prediction method with the genetic algorithm (GA)-optimized adaptive fusion of space-based cloud imagery and ground-based meteorological data. The effective integration of satellite cloud imagery is conducted in the PV power prediction system, and the proposed method addresses the issues of low accuracy, poor robustness, and inadequate adaptation to complex weather associated with using a single type of meteorological data for PV power prediction. The multi-source heterogeneous data are preprocessed through outlier detection and missing value imputation. Spearman correlation analysis is employed to identify meteorological attributes highly correlated with PV power output. A dedicated dataset compatible with LSTM algorithm-based prediction models is constructed. An LSTM prediction model with a GA algorithm-based adaptive multi-source heterogeneous data fusion method is proposed, and the ability to construct a precise short-term PV power prediction model is demonstrated. Experimental results demonstrate that the proposed method outperforms single-source LSTM, single-source CNN-LSTM, and dual-source CNN-Transformer models in prediction accuracy, achieving an RMSE of 0.807 kWh and an MAPE of 6.74% on a critical test day. The proposed method enables real-time precision forecasting for grid dispatch centers and lightweight edge deployment at PV plants, enhancing renewable energy integration while effectively mitigating grid instability from power fluctuations. Full article