Search Results (1,053)

Reconstructing building models from urban SAR tomography (TomoSAR) point clouds is often constrained by limited resolution, low positioning accuracy in elevation, as well as data incompleteness and artifacts caused by microwave imaging mechanisms. These challenges seriously restrict the extraction of high-accuracy building models with structural details from TomoSAR point clouds. This paper proposes a refined urban building modeling method that effectively utilizes structural priors, including directionality, orthogonality, and potential symmetry. First, a piecewise fitting strategy integrated with density-based segmentation is employed to iteratively estimate the main directions of the buildings and capture finer geometric variations of complex façade footprints than simple-plane approximations. Second, a roof extraction algorithm combining an adaptive Doug-las–Peucker approach with symmetry evaluation and constraints is developed to regularize roof outlines and repair data defects. Crucially, to handle extreme cases where roof data are entirely missing, a novel building width estimation method based on building shadow analysis is proposed. Experiments conducted on the SARMV3D-1.0 and SARMV3D-3.0 point cloud datasets demonstrate that the proposed method significantly enhances reconstruction accuracy and geometric fidelity in urban regions compared to state-of-the-art approaches. Full article

Fringe projection profilometry (FPP) is widely used for 3D reconstruction, but conventional single-view FPP systems suffer from inherent occlusions and shadow regions, leading to incomplete surface recovery. In this study, we propose CVA-Net, an end-to-end deep learning framework with cross-view attention (CVA) that directly reconstructs dense depth maps from multi-view fringe patterns. CVA-Net simultaneously processes four fringe images acquired from orthogonal projection directions and leverages a CVA module to explicitly model inter-view dependencies, enabling adaptive fusion of complementary information. A 3D U-Net backbone with attention gates, atrous spatial pyramid pooling (ASPP), and an auxiliary parameter estimation branch further enhances reconstruction accuracy and structural consistency via multitask learning. To support Sim2Real network training, we build a Blender-based digital twin of a multi-view FPP system and generate a large-scale synthetic dataset with perfect ground truth. Extensive experiments on both synthetic and real-world objects demonstrate that CVA-Net significantly outperforms state-of-the-art single-view methods. With a symmetric four-view configuration and fringe period of 8, CVA-Net achieves an MAE of 0.0359 mm, an MSE of 0.0379 mm² and an RMSE of 0.1947 mm, reducing the MAE, MSE, and RMSE by 32.8%, 54.1%, and 32.2%, respectively, compared to the best single-view competitor. Ablation studies validate the contribution of each architectural component, while real-system experiments demonstrate the feasibility of transferring a network trained purely on synthetic data to practical FPP measurements without domain adaptation. Although further improvements are required to enhance reconstruction accuracy under real imaging conditions, the proposed framework provides an effective initial step toward bridging the gap between digital-twin-based training and real-world multi-view FPP applications. CVA-Net provides a robust, occlusion-aware solution for multi-view FPP reconstruction. Full article

The Event Horizon Telescope (EHT) imaging of M87* and Sgr A* provides a unique opportunity to test spacetime geometries in the strong-field regime. Motivated by this, we systematically investigate the optical characteristics for three types of nonsingular black holes (BHs) with a Minkowski core and constrain the quantum gravity effect parameter $\alpha$ and the regularization parameter n using EHT observational data. Utilizing the observed shadow sizes of M87* and Sgr A*, we conduct a detailed comparison of the constraints on $\alpha$ for the three BH types. Our analysis reveals significant differences among them: Type I BHs exhibit the largest upper limit, whereas Type III BHs show the smallest upper limit. Furthermore, the constraints derived from M87* observations are tighter than those from Sgr A*, reflecting the close dependence of these limits on current observational precision. Subsequently, we simulate BH images at the current EHT resolution using a Gaussian filter. Although the photon ring and lensed ring features cannot be resolved, variations in shadow size and brightness distribution are clearly detectable. Within the parameter space allowed by EHT observations, the shadow size and total intensity exhibit a distinct monotonic hierarchy: Type I BHs display the largest shadow and highest total intensity, while Type III BHs show the opposite trend. Finally, we find that increasing $\alpha$ leads to shadow contraction and dimming, whereas increasing n causes the shadow to expand while making the optical characteristics of the three BH types increasingly indistinguishable. Consequently, the three BH types become more readily distinguishable only when n is small or $\alpha$ is large. Full article

We applied conversational robots in language learning, building on the previously developed Japanese repeating (shadowing) system for beginners in an applied foreign languages program at a northern Taiwan university. The previous system, designed to support after-class practice, served as the foundation for the present project. In this study, the system is extended to create an image-based vocabulary learning tool for Japanese, Mandarin Chinese, and Vietnamese. The design concepts, integration of visual prompts, and the potential of conversational agents in this study enhance multilingual vocabulary acquisition. To evaluate the system’s effectiveness, a group of student participants tested and validated the prototype, providing feedback on usability, learning support, and overall performance. Full article

Smartphone-based proximal sensing has emerged as a promising low-cost approach for pasture monitoring. A critical component of this methodology is accurate leaf segmentation, as it directly affects the reliability of subsequent image-based analyses. Despite advances in computer vision, the role of preprocessing strategies in segmentation performance remains insufficiently explored, particularly under resource-constrained conditions. This study presents a systematic comparative evaluation of three preprocessing pipelines based on HSV and CIELab color spaces for the segmentation of Urochloa grass leaves (Urochloa hybrid Mavuno and Urochloa decumbens) using smartphone imagery acquired field conditions. The pipelines were assessed using a multi-criteria framework, including the Fisher Discriminant Ratio (FDR), Intersection over Union (IoU), Overlap Error (OE), Structural Similarity Index (SSIM), and Edge Preservation Index (EPI), complemented by discordance map analysis. The results demonstrate that preprocessing design significantly influences segmentation stability, boundary preservation, and robustness to illumination variability. Pipelines based on HSV channels showed high sensitivity to shadows and non-uniform lighting, leading to reduced segmentation consistency. In contrast, the CIELab-based pipeline relying on the a* channel achieved superior performance, with higher discriminative capacity, improved edge preservation, and lower computational cost. These findings highlight that carefully designed classical preprocessing strategies remain highly effective for low-cost, real-time applications, even in the absence of computationally intensive models. This work establishes a robust segmentation foundation for future integration with advanced analytical methods, including machine learning approaches, and supports the development of scalable smartphone-based tools for pasture monitoring. Full article

High dynamic range (HDR) imaging has received sustained interest for its ability to capture a wider scene radiance range than conventional imaging by fusing multi-exposure images. However, increasing the number of exposures aggravates ghosting artifacts during fusion, prompting modern devices to use only one or two shots. This leads to the fact that a single exposure is unable to simultaneously preserve details in both dark and bright regions, and even dual-exposure settings are insufficient to capture the full scene radiance. Under limited exposure conditions, distinct challenges arise for both physics-driven and data-driven models, with the former struggling to model unobserved irradiance distributions and the latter having difficulty capturing diverse exposure variations, leading to unrealistic brightness artifacts in highlights and shadows. To address this problem, we propose a mixed causal–acausal assisted compensation model that integrates physics-driven and data-driven modules to generate interpolated and extrapolated pseudo-exposure images for recovering missing brightness information. The proposed model decomposes pseudo-exposure image generation into a hybrid representation consisting of a causal part reflecting radiance variation and an acausal part capturing the residual scene structure. The causal representation is derived by estimating the physics-driven intensity mapping function from adjacent exposure images while the acausal one is obtained through a data-driven attention-augmented hybrid network. Both theoretical analysis and experimental results demonstrate that the pseudo-exposure images perform well in both objective and subjective evaluations. In addition, it is validated that incorporating interpolated or extrapolated images into raw images can indeed mitigate brightness artifacts in dual-exposure fusion as well as in single-exposure enhancement. Full article

Waterbody extraction is of great significance for water resource investigation and monitoring. In addition to RGB bands, most common satellite images have a near-infrared (NIR) band. By combining these RGB-NIR bands, certain water, vegetation, and shadow indices can be calculated. The near-infrared band and these indices are very similar to the X modality in RGB+X data (common examples include RGB-D and RGB-Thermal). However, at present, no studies have thoroughly examined multimodal feature fusion from the RGB+X perspective in order to extract waterbodies with high precision. As a result, existing algorithms do not fully utilize satellite image information and have limited generalization ability. To overcome this limitation, we propose a dual-complexity backbone for waterbody extraction from the perspective of RGB+X data semantic segmentation. Its complex Transformer branch is used to extract RGB modality features, while its simple CNN branch is used to extract X modality features. This network structure can effectively capture multimodal, global, and local features in remote sensing images. It can also fully leverage the fact that the scale of RGB image datasets in computer vision is significantly larger than that of remote sensing waterbody extraction datasets. If a large pretrained model is used in the RGB branch, it is unnecessary to freeze the weights. Instead, both branches can be trained jointly, allowing the RGB branch to better adapt to the remote sensing waterbody extraction task without raising concerns that fine-tuning might undermine the pretrained model’s strong representation capability. We also propose two X modality configurations with strong generalization performance. To fully fuse multimodal features, we design a hybrid fusion module combining a CNN and a cross-attention mechanism. To integrate the multi-scale features, we employ a multi-scale Transformer structure in the RGB branch and design a multi-scale decoder. Our algorithm achieves state-of-the-art performance on the GID-5 dataset and competitive performance on the S1S2-Water dataset. Furthermore, it significantly outperforms existing methods in cross-dataset zero-shot transfer between the two datasets, with IoU/F1-score gains of 26.08%/27.33% on GID-5 and 38.74%/31.37% on S1S2-Water over previous SOTA methods. Our processing paradigm of modeling RGB-NIR remote sensing images as RGB+X data shows potential for generalization to other multi-modal remote sensing tasks. The dual-complexity backbone we design also has potential to be extended to other tasks that transfer large pretrained RGB models to remote sensing imagery with RGB-NIR four bands or even more spectral bands. We have open-sourced the code and trained models used in this research. Full article

High-precision identification of acoustic convergence zones (CZs) and acoustic shadow zones (SZs) is a core prerequisite for deep-sea sonar performance prediction and long-range underwater target detection. However, in data-scarce marine environments, traditional acoustic identification methods suffer from high environmental sensitivity and significant computational costs, while pure data-driven deep learning methods face dilemmas such as a lack of physical consistency and poor generalization on small samples. To address these issues, a three-level cascaded recognition framework based on physics-prior-augmented deep learning is proposed in this paper, enabling accurate segmentation of CZs and intelligent classification of sound field types under data-scarce scenarios. In this framework, physical acoustic principles are incorporated exclusively as priors through a training dataset generated by a Gaussian beam acoustic propagation code (Bellhop) and through hand-crafted geometric features derived post hoc from the initial segmentation outputs. Taking a typical deep-sea area in the Northwest Pacific Ocean as the research object, a hybrid dataset comprising 5000 simulated transmission loss images and 500 simulated images from a geographically distinct sea area is constructed. The sound field is categorized into four types: strong convergence, usable convergence, weak convergence, and shadow zone. In the first stage, the ResNet-34 backbone is improved by integrating deformable convolution and a global statistical feature module, which, combined with a joint loss function, achieves high-precision pixel-level segmentation of CZs and SZs, with the regional gray contrast reaching 86.9%. In the second stage, a customized dual-channel VGG16 architecture is designed to fuse the extracted geometric priors and visual features, achieving a sound field classification accuracy of 89.91%. In the third stage, a hybrid data augmentation technique combining Mixup and convolutional autoencoder is adopted alongside a transfer learning strategy to mitigate the data scarcity under cross-domain conditions, boosting the small-sample classification accuracy to 84.45%. The experimental results demonstrate that the models in each stage of the proposed framework significantly outperform traditional methods and baseline networks. This study provides a novel methodology and technical support for intelligent sound field identification in data-scarce marine environments. Finally, the core contributions and current limitations are summarized, and future research directions, such as constructing a dynamic hydrological parameter feedback mechanism and identifying three-dimensional complex sound fields, are prospected. Full article

Automatic detection of electric pylons in optical remote sensing imagery is important for large-scale powerline monitoring, but remains challenging due to complex backgrounds, small target appearances, and large variations in pylon-shadow structures. This paper proposes a Refined Deformable-DETR framework with a Spatial Context-aware Query Modulation (SCQM) module to enhance object query representations. SCQM aggregates image-level contextual information from encoder memory and generates channel-wise modulation vectors to recalibrate object queries before deformable cross-attention, thereby providing image-conditioned channel priors for subsequent query-feature interaction. Experiments on the self-constructed Electric Pylon Remote Sensing Dataset (EPRD) show that the proposed method improves AP from 72.7% to 74.1% and APs from 47.2% to 50.9% compared with the baseline Deformable-DETR. Evaluations on the public Electric Pylon Dataset (EPD) further demonstrate its generalization capability. These results indicate that context-aware query modulation is effective for Transformer-based electric pylon detection in complex remote sensing scenarios. Full article

To meet the demand for high-precision target classification in complex scenes, a hyperspectral–polarimetric–LiDAR multimodal image fusion method tailored for few-shot scenarios is proposed. Feature-mapping functions for polarimetric and LiDAR images are constructed, and a multi-scale hierarchical optimization strategy is employed to jointly enhance low- and high-frequency components across modalities. This approach effectively addresses key challenges under limited training data, such as substantial cross-modal dimensional disparities and the difficulty of robust feature extraction and fusion. The proposed algorithm conducts bimodal image fusion on the NWPUSP spectral-polarization dataset and KAIST spectral-depth dataset. Compared with other fusion methods, it achieves average increases of 7.3% and 4.87% in information entropy, 53.18% and 30.35% in standard deviation, 48% and 108.28% in average gradient, as well as 96.25% and 101.13% in spatial frequency, respectively. Moreover, relying on the self-developed integrated hyperspectral-polarization imaging system and commercial LiDAR, we synchronously and efficiently acquire multimodal images including hyperspectral, polarization and LiDAR images of complex ground object scenes. Comparative experiments are implemented against six other mainstream fusion algorithms. The objective evaluation results show that the average improvements reach 7.19% in information entropy, 46.85% in standard deviation, 76.62% in average gradient and 79.74% in spatial frequency, which notably enhances the feature retention capability of fused images. Under few-shot conditions, the target recognition classification accuracy and Kappa coefficient of the fused image are improved by 9.8% and 11.05%, respectively, compared with those of the unimodal hyperspectral image. This effectively highlights targets under shadow occlusion and compensates for LiDAR’s response deficiencies to surface textures, achieving complementary advantages of multimodal images for ground object targets in complex scenes. This research provides a new solution for future optical multimodal remote sensing and image fusion. Full article

Zinc germanium phosphide (ZnGeP₂) is an important nonlinear crystal for mid-infrared conversion, but its performance is limited by residual absorption and intrinsic impurity phases. In this study, polycrystalline ZnGeP₂ was synthesized by a modified two-temperature method, purified by inclined directional recrystallization for up to three cycles, and then grown into single crystals by the vertical Bridgman method. The resulting material was examined by shadow-projection imaging, transmission spectroscopy in the 650–2500 nm range, absorption measurements at 2.097 µm, laser-induced damage threshold (LIDT) testing, and powder X-ray diffraction. Repeated purification improved optical homogeneity and near-infrared transparency, while the absorption coefficient at 2.097 µm decreased from 0.45 to 0.30 cm⁻¹ after three purification cycles. Semi-quantitative PXRD analysis showed progressive suppression of intrinsic impurity phosphides, with phase purity increasing from 86.31% after the first cycle to 95.995% after the second and reaching 100% after the third within the detection limit of the method. However, the LIDT decreased with increasing purification number, indicating a trade-off between lower optical losses and damage resistance. These results demonstrate that inclined directional recrystallization is an effective pre-growth purification route for ZnGeP₂ and that the optimal number of purification cycles should be selected according to the intended application. Full article

Recent advances in 3D Gaussian splatting (3DGS) have enabled efficient image-based scene reconstruction, but existing methods that rely heavily on multi-view photometric consistency remain sensitive to dynamic illumination and weakly constrained regions. This issue is especially evident in tunnel scenes, where limited ambient light and localized active illumination cause strong appearance variation and shadowed regions that appear weakly textured in the captured images. As a result, existing methods often suffer from appearance inconsistency, floating artifacts, and unstable Gaussian distributions. To address these challenges, we present dynamic lighting-aware Gaussian splatting (DLG-GS), a real-time framework designed primarily for tunnel-oriented reconstruction under dynamic lighting. DLG-GS includes two complementary components: a dynamic lighting-adaptive appearance modeling strategy that reduces illumination-induced artifacts while preserving local texture details, and a voxel–depth joint constraint that uses monocular depth priors to regularize the spatial distribution of voxel anchors and neural Gaussians, thereby improving optimization stability and suppressing floating artifacts in shadow-induced weak-texture regions. By jointly optimizing appearance adaptation and depth-guided spatial regularization, DLG-GS improves reconstruction stability and rendering quality while maintaining real-time performance. Experiments on a self-collected tunnel dataset show clear improvements over selected baselines, and additional evaluations on public benchmarks indicate competitive performance beyond the target tunnel setting. Full article

A significant wave height (SWH) estimation method for X-band ocean radar images based on the shadow modulation principle is studied. The conventional shadow method obtains the wave steepness by analyzing the bright and dark patterns in the radar image and then calculates the SWH. The shadow method relies on accurate estimation of wave steepness, which is the most reliable in the upwave area, so it shows a strong azimuth dependence. Under the actual observation conditions, it is usually difficult to obtain an ideal analysis region to effectively mitigate the direction dependence due to the limitations of physical obstacles and platform attitude changes, which affects the inversion accuracy. To solve the problem, this paper proposes a wave steepness correction method based on harmonic fitting. By establishing a harmonic fitting model between wave steepness and wave angle, the method reconstructs the continuous and stable wave steepness distribution with wave angle from $0°$ to $360°$ according to limited data points. Then, the wave steepness independent of azimuth is obtained when the wave angle is $0°$. The effectiveness of the proposed wave steepness correction method is validated using a total of 466 sets of radar data collected from 8 November to 18 November 2014 and 10 January to 20 January 2015. After applying the wave steepness correction method, compared to the conventional shadow method without correction, although the correlation coefficient (CC) increased by only 0.07, the bias (BIAS) decreased by 0.12 m, and the average root mean square error (RMSE) decreased by 0.12 m. Full article

Italy is one of Europe’s largest consumers of wood pellets, while domestic production remains comparatively limited. In parallel, wood chips (WC) represent a strategic biofuel for power generation, where particle size distribution (PSD) affects handling and storage. Conventional PSD assessment relies on time-consuming methodology. This study proposes a patent-pending image-processing approach (Shadow Size Distribution—SSD analysis) for PSD classification of WC under bulk conditions. One hundred samples were characterized via both standard analysis and SSD. PSD data were aggregated into fine and coarse macro-fractions and used to define binary class labels. Multivariate analyses (PERMANOVA, PCA) and Support Vector Classifier (SVC) models were employed to evaluate the discriminative capability of SSD features. PCA revealed coherent relationships between PSD macro-variables and key shadow descriptors, particularly shadow number and area. The best SVC configuration achieved 0.77 test accuracy, with strong recall for coarse samples. Although overall performance was constrained by dataset size and imbalance, the results demonstrate that SSD features retain meaningful granulometric information, supporting further development toward automated, in-line PSD monitoring systems. From a sustainability perspective, the proposed SSD-based approach enables faster and potentially in-line monitoring of biomass quality, supporting more efficient combustion processes, reduced emissions, and improved resource management in bioenergy systems. Full article

X-band marine radar shadow features are widely applied to wave height estimation. Since the shadow fraction rises with the distance from the radar antenna, wave slope estimation is sensitive to the selected analysis region. To resolve this issue, a wave height estimation method is proposed by adopting the optimal shadowed fraction which is unrelated to the boundary selection of the analysis area. Within this paper, the shadow fraction is computed on the basis of the mechanism of radar image shadow imaging. Instead of adopting the widely used Smith fitting function, the wave slope with the non-shadow areas is achieved by using the obtained shadow fraction and the grazing angle. The collected marine radar images, totaling 450 h, are employed to demonstrate the performance of the proposed wave height retrieval method. Compared with fundamental shadow statistical approach, the root mean square error of the proposed method decreases by 0.19 m, and the correlation coefficient increases by 0.10. Meanwhile, the execution time of the presented algorithm has significantly decreased. Full article