Search Results (199)

Accurate water body segmentation from multispectral remote sensing imagery is critical for hydrological monitoring and environmental management. However, leveraging transfer learning with pre-trained models remains challenging due to the dimensional mismatch between three-channel RGB-based architectures and multi-band spectral data. To address this, this study proposes a novel segmentation network, termed Intelligent Gated Fusion Network (IGF-Net), built upon a dual-branch feature encoder module and a core Intelligent Gated Fusion Module (IGFM). The IGFM achieves adaptive fusion of visual and spectral features through a cascaded mechanism integrating differences-and-commonalities parallel modeling, channel-context priors, and adaptive temperature control. We evaluate IGF-Net on the newly constructed Tiangong-2 remote sensing image water body semantic segmentation dataset, which comprises 3776 meticulously annotated multispectral image patches. Comprehensive experiments demonstrate that IGF-Net achieves strong and consistent performance on this dataset, with an Intersection over Union of 0.8742 and a Dice coefficient of 0.9239, consistently outperforming the evaluated baseline methods, such as FCN, U-Net, and DeepLabv3+. It also exhibits strong cross-dataset generalization capabilities on an independent Sentinel-2 water segmentation dataset. Ablation studies and visualization analyses confirm that the proposed fusion strategy significantly enhances segmentation accuracy and stability, particularly in complex scenarios. placeholder Full article

Egyptian papyri are commonly documented using high-resolution two-dimensional imaging, which enhances legibility but does not adequately capture the micrometric surface morphology required for material and conservation studies. To address this limitation, we developed and validated an integrated, fully non-contact imaging workflow combining Ultra-Close-Range Multiband Photogrammetry with Reflectance Transformation Imaging (RTI) and normal map integration. The protocol was tested on six papyrus fragments from the Museo Egizio di Torino (XXI Dynasty–Byzantine period) exhibiting different conservation conditions. Multiband photogrammetry in the visible and visible-induced infrared luminescence bands achieved a Ground Sample Distance of 17 µm/px and a point cloud density of approximately 170 points/mm², enabling detailed analysis of fiber morphology, surface deformation, and the spatial distribution of Egyptian blue. RTI-based normal map integration provided complementary high-frequency surface information with reduced acquisition and processing times. To overcome RTI low-frequency distortions, a revised normal integration strategy was implemented using surface planarization and frequency-domain fusion with photogrammetric data based on Power Spectral Density analysis. The resulting hybrid models combine metric reliability with enhanced surface detail, providing a scalable and non-invasive approach for papyrological documentation and conservation research. Full article

The digital reconstruction of damaged archeological wall paintings is a challenging task due to severe material degradation, high fragmentation, and the lack of reference images. A crucial preliminary step is the separation and grouping of fragments originating from different wall paintings, which are often found mixed together at archeological sites. To address this issue, we explored the potential of multispectral imaging (MSI) for unsupervised fragment clustering, aiming to assess whether integrating multiple spectral bands can enhance fragment discrimination compared to using the visible band alone. As a test set, we examined five groups of wall painting fragments from a Roman domus (1st c. BC–1st c. AD) provided by the Archaeological Museum of Cremona (Italy). Images were acquired using the Hypercolorimetric Multispectral Imaging (HMI) system developed by Profilocolore^® Srl (Rome, Italy). Specifically, we considered visible reflectance (VIS), infrared reflectance (IR), infrared false color (IRFC), and Ultraviolet-induced Fluorescence (UVF) images. Through a systematic benchmarking study, we compared several state-of-the-art feature extraction and clustering methods across single- and multi-band configurations. Results show that combining MSI data can substantially enhance the system’s ability to correctly separate and group fragments, indicating a promising direction for future research. Full article

Satellite imagery is increasingly being scrutinized through deep learning methodologies for remote sensing applications, particularly focusing on the detection of water bodies. Identification and analysis of rivers, lakes, and reservoirs through segmentation have become feasible, enabling the exploration of their statistical information. During crises such as floods and changes in river pathways, real-time detection of water bodies via remote sensing proves to be highly advantageous. Nevertheless, achieving precise segmentation of water bodies presents a notable challenge, mainly due to the necessity of high-resolution multi-channel satellite images. Existing literature predominantly relies on satellite data from multi-band satellites for water-body extraction. Conversely, the current research emphasizes the segmentation of water-body regions using relatively lower-resolution RGB images without the incorporation of extra multi-spectral channels. To tackle this challenge, a unique methodology is suggested, involving a customized U-Net model integrated with a time-frequency attention network for segmentation. To assess the comprehensive performance of the proposed model, it is evaluated against a publicly available Sentinel-2 satellite dataset, and the outcomes are compared against standard benchmark metrics. The proposed TFA-U-Net model demonstrates superior performance compared to several recent state-of-the-art water-body segmentation models. Experimental results show that the proposed model achieves a precision of 0.94, sensitivity of 0.96, Dice score of 0.93, accuracy of 0.97, and mean IoU of 0.85, indicating its effectiveness for accurate water-body segmentation using low-resolution satellite images. Full article

In this work, we aimed to develop and evaluate multi-band outer volume suppression pulses for increased acceleration rates in simultaneous multi-slice accelerated cardiac MRI. MB-OVS pulses were constructed from a multi-band combination of two slab-selective saturation pulses and tested for various pulse shapes using Bloch simulation and phantom experiment. The MB-OVS pulses were interleaved between imaging pulses to ensure homogeneous suppression throughout the cardiac cycle/imaging window in vivo. Simultaneous multi-slice (SMS) CINE and first-pass myocardial perfusion scans with and without the proposed MB-OVS pulses were compared in terms of residual artifacts at high acceleration rates. Among the tested pulses, both Bloch simulation and phantom experiments showed that amplitude-optimized sinc pulses provided the best trade-off in suppression efficiency, the required $B_1^{+}$, SAR, and slab profile. CINE imaging with 5-fold SMS-OVS acceleration significantly outperformed imaging without MB-OVS, maintaining leakage-free image quality, even when adding 2-fold in-plane acceleration. SMS-OVS also enabled perfusion imaging in 9 slices with 1.7 × 1.7 mm² resolution, achieving a 16-fold spatial-only acceleration while ensuring accurate contrast dynamics without leakage artifacts. Interleaved MB-OVS modules enabled thorough leakage artifact suppression in cardiac SMS-accelerated CINE and perfusion imaging, particularly at high acceleration rates. The proposed approach may be promising for unlocking further acceleration potential of SMS in cardiac imaging. Full article

Satellite-derived bathymetry holds significant value for acquiring nearshore bathymetric data. However, in coastal waters, bathymetry is affected by in-water particle scattering and seafloor substrate variability, leading to spatial inconsistency between the logarithmic green band profile derived from multispectral satellite imagery and the actual water depth profile. According to the position information of interpolated points and the inverse distance square relationship with the surrounding 16 points from low-reference bathymetric data (such as the bathymetric map from GEBCO, NOAA Electronic Navigational Charts), this model adopts a third-order inverse distance square bicubic convolution interpolation method to resample a high-resolution bathymetric map with the size of the satellite image. Normalized underwater topography trend data (derived from the low-resolution reference bathymetric map) were combined with normalized green band data to compute an averaged dataset. In this way, a linear bathymetric model was constructed. We invert this model’s parameters and calculate the water depth by using the average data and reference points from reference bathymetric data. Validation tests were conducted across three test areas using independent validation bathymetric data: Weizhou Island, China (Case II waters); Saipan, Northern Mariana Islands, USA (Case I waters); and Molokai Island, Hawaii, USA (Case I waters). Each test area was studied using five error analysis methods (i.e., scatterplot, error histogram, regional bathymetric error, three check lines, and seven check points). Compared to four classic bathymetric models (i.e., single-band model, log-ratio model, ratio-log model, and multi-band model), the proposed model achieved lower root mean square errors (RMSE) of 2.08 m, 1.40 m, and 2.01 m in the three test areas, representing reductions of 35%, 43%, 45%, and 20% and overall averages of 48%, 62%, 64%, and 43%, respectively. Its goodness of fit (R²) reached 0.87, 0.97, and 0.97, showing improvements of at least 5%, 5%, 9%, and 9% and overall averages of 17%, 77%, 84%, and 12%, respectively. The results demonstrate that the proposed model significantly improves bathymetry accuracy while maintaining algorithmic simplicity, providing a new model for acquiring nearshore foundational bathymetric maps. Full article

So-called ‘war rugs’ started being produced in Afghanistan after the Soviet invasion in 1979. These textiles have sparked debate as symbols of resilience and political commentary but also as controversial commodification of human suffering. However, their manufacture and materiality have not been studied so far. In the framework of the British Museum exhibition “War rugs: Afghanistan’s knotted history”, a scientific investigation was conducted on nine rugs from the collection. Approximately 65 samples were analysed by optical microscopy (OM), scanning electron microscopy coupled to energy dispersive X-ray spectroscopy (SEM-EDX) and high-pressure liquid chromatography coupled to diode array detector and tandem mass spectrometry (HPLC-DAD-MS/MS) to study the fibres, mordants and dyes used in the production of the rugs. Scanning X-ray fluorescence (MA-XRF) and multiband imaging (MBI) were also used directly on the rugs to map the distribution of specific mordants and dyes, respectively. The results revealed the intentional use of white or dark wool as the substrate for dyeing, to obtain specific colour shades. A wide range of synthetic dyes was detected, including Acid Orange 7, Acid Red 88, Basic Green 4, Acid Blue 92, Acid Black 1 and Direct Black 38 in the earlier rugs, whereas Direct Yellow 1, Direct Brown 1, Direct Yellow 12, Acid Green 25, Acid Blue 113 and Direct Blue 15 were identified in the later rugs. Some synthetic dyes remained unidentified. Additionally, natural dyes were used in three rugs. An emodin-based colourant, possibly obtained from dock or sorrel (Rumex spp.), was detected in two light brown areas. A berberine-based colourant consistent with barberry (Berberis spp.) was detected in a yellow area. These results represent the first scientific study of these objects and enable preliminary insights into the details of this complex craft that has evolved from centuries of carpet making in this area. Full article

Multi-channel common-aperture optical systems, which excel at simultaneous multi-spectral information acquisition, are widely used for image fusion. However, complex systems for long-distance multi-band detection suffer from difficulties in assembly and adjustment and light vignetting. To resolve this, the paper proposes a modular design method that splits the optical path into independent modules: the common-aperture optical path adopts an off-axis reflective beam-shrinking structure to extend the focal length and ensure 100% light input, compared with coaxial multi-channel common-aperture systems. The relay optical path of each spectral channel uses a continuous zoom design for smooth detection–recognition switching. Based on the method, a three-channel common-aperture system is developed integrating visible light (VIS), short-wave infrared (SWIR), and mid-wave infrared (MWIR). The modulation transfer function (MTF) and wavefront distribution of the common-aperture optical path approach the diffraction limit. After integration with the relay optical paths, the system, without global optimization, can achieve the following performance: the root mean square (RMS) across the full field of view (FOV) at different focal lengths for each channel is smaller than the detector pixel size (3.45 μm for VIS, 15 μm for SWIR/MWIR); the MTF exceeds 0.2 at the cutoff frequency. Subsequently, the results of the tolerance analysis verify the feasibility of the design for each module and the advantage of the modular layout in the assembly and adjustment of the system. Finally, the paper discusses the influence of parallel plates on the wavefront distortion of the system and proposes optimization thinking using freeform surfaces. The design results of the study validate the feasibility of the modular layout in simplifying the design and assembly of multi-channel common-aperture optical systems. Full article

Multiband imaging (MBI) is a non-invasive, portable digital technique that has become increasingly widespread in the technical study and condition assessment of paintings, owing to its affordability and ease of use. This paper presents an experimental study aimed at optimising MBI at the microscopic scale—referred to as micro-multiband imaging (µMBI)—with the particular aim of expanding its diagnostic capabilities. A range of µMBI techniques was used on custom-made mock-ups made up of pigments selected for their spectral responses, and representative of traditional artistic materials. The techniques used included microphotography of polarised and unpolarised visible light (µVIS), raking light microphotography (µRL), transmitted light microphotography (µTL), ultraviolet-induced visible luminescence microphotography (µUVL), near-infrared microphotography (µIR), near-infrared micro-trans-irradiation (µIRT), and near-infrared false-colour microphotography (µIRFC). The results obtained through µMBI were compared with those from standard MBI methods, allowing for a critical discussion of the strengths and limitations of this emerging approach. Results evidence that µMBI provides high-resolution, spatially specific insights into materials and painting techniques, offering a more detailed understanding at the microscale of how a painting was executed. It also enables the assessment of deterioration processes (e.g., cracking, delamination, and metal soap formation), contributing to a deeper comprehension of the origin and progression of failure phenomena and supporting the development of more informed, preventive conservation strategies. Full article

This study aims to elucidate the deterministic correlation between the microscopic fracture mechanisms and the multi-domain characteristics of acoustic emission in reinforced concrete beams under cyclic loading. Cyclic incremental tests were designed and conducted, with synchronized application of digital image correlation and AE techniques to capture the entire damage evolution process and corresponding signal responses throughout. The findings reveal that the damage stage division based on mechanical responses is consistent with that based on AE responses. Damage accumulation and irreversible processes can be clearly characterized by AE activity, and the systematic decrease in the Felicity ratio quantitatively verifies the irreversible accumulation of damage. Under cyclic loading, different microscopic fracture mechanisms generate AE frequency-domain signatures with statistically significant differences. A damage identification model integrating the Felicity ratio and multi-band energy features was developed, achieving an accuracy of 88.89% in identifying macroscopic damage stages. This research quantitatively confirms the effectiveness of AE characteristics as reliable identifiers of microscopic fracture mechanisms, providing a new basis for advancing structural health monitoring technologies grounded in fracture mechanism recognition. Full article

Multispectral infrared detection plays a crucial role in advanced applications spanning environmental monitoring, military surveillance, and biomedical diagnostics, offering superior target identification accuracy compared to single-band imaging techniques. In this work, we synthesized four distinct bands of colloidal quantum dots (CQDs)—specifically, a cut-off of 1.3 µm with PbS CQDs and 1.8 µm, 2.6 µm, and 3.5 µm with HgTe CQDs—and employed them to construct planar multiband infrared photodetectors. The device exhibited a clear photoresponse at room temperature from 0.8 µm to 3.5 µm, with responsivity of 5.39 A/W and specific detectivity of 2.01 × 10¹¹ Jones at 1.8 µm. This materials–device co-design strategy integrates wavelength-selective CQD synthesis with planar pixel-level patterning, providing a versatile pathway for developing low-cost, solution-processed, multiband infrared photodetectors. Full article

Precise lagoon bathymetry remains scarcely available for most tropical islands despite its importance for navigation, resource assessment, spatial planning, and numerical hydrodynamic modeling. Hydrodynamic models are increasingly used for instance to understand the ecological connectivity between marine populations of interest. Island remoteness and shallow waters complicate in situ bathymetric surveys, which are substantially costly. A multisource strategy using historical point sounding, multibeam surveys and well calibrated satellite-derived bathymetry (SDB) can offer the possibility to map entirely extensive and geomorphologically complex lagoons. The process is illustrated here for the rugose complex lagoon of Gambier Islands in French Polynesia. The targeted bathymetry product was designed to be used in priority for numerical larval dispersal modeling at 100 m spatial resolution. Spatial gaps in in situ data were filed with Sentinel-2 satellite images processed with the Iterative Multi-Band Ratio method that provided an accurate bathymetric model (1.42 m Mean Absolute Error in the 0–15 m depth range). Processing was optimized here, considering the specifications and the constraints related to the targeted hydrodynamic modeling application. In the near future, a similar product, possibly at higher spatial resolution, could improve spatial planning zoning scenarios and resource-restocking programs. For tropical island countries and for French Polynesia, in particular, the needs for lagoon hydrodynamic models remain high and solutions could benefit from such multisource coverage to fill the bathymetry gaps. Full article

This study proposes a data-efficient fine-tuning strategy for multi-band antenna synthesis using a Wasserstein Auxiliary-Guided Progressive Growing GAN (WAG-PGGAN). Starting from a pretrained 512 × 512 dual-band PIFA-like generator trained on 4180 samples at 2.45/5.2 GHz, we introduce three 3.5-GHz wideband seeds augmented to 836 images (new:legacy ≈ 1:5) and fine-tune only the highest-resolution stage on the combined 5016-image corpus. A Hough-transform-based edge-enhancement module with an edge-aware loss preserves conductor boundaries and strengthens frequency–geometry correlation. Across n = 8 fabricated prototypes, all achieve |S11| < −10 dB and collectively span 1.86–5.83 GHz; measured total efficiencies are 52–87% (e.g., 73.6% @ 2.68 GHz, 66.7% @ 3.56 GHz, 69.0% @ 5.83 GHz), with radiation patterns consistent with simulation. The method retains prior 2.45/5.2 GHz performance while adding 3.5-GHz wideband behavior using ≤ 17% new data (836/5016), demonstrating effective transfer from small datasets. On an RTX 3060 Ti, inference is ≈ 3 s/design after ~192 h of training. Simulation–measurement agreement confirms that fine-tuned WAG-PGGAN yields high-resolution, physically valid multi-band antennas with reduced data and computational cost. Full article

3D reconstruction technology generates three-dimensional representations of real-world objects, scenes, or environments using sensor data such as 2D images, with extensive applications in robotics, autonomous vehicles, and virtual reality systems. Traditional 3D reconstruction techniques based on 2D images typically rely on RGB spectral information. With advances in sensor technology, additional spectral bands beyond RGB have been increasingly incorporated into 3D reconstruction workflows. Existing methods that integrate these expanded spectral data often suffer from expensive scheme prices, low accuracy, and poor geometric features. Three-dimensional reconstruction based on NeRF can effectively address the various issues in current multispectral 3D reconstruction methods, producing high-precision and high-quality reconstruction results. However, currently, NeRF and some improved models such as NeRFacto are trained on three-band data and cannot take into account the multi-band information. To address this problem, we propose Multispectral-NeRF—an enhanced neural architecture derived from NeRF that can effectively integrate multispectral information. Our technical contributions comprise threefold modifications: Expanding hidden layer dimensionality to accommodate 6-band spectral inputs; redesigning residual functions to optimize spectral discrepancy calculations between reconstructed and reference images; and adapting data compression modules to address the increased bit-depth requirements of multispectral imagery. Experimental results confirm that Multispectral-NeRF successfully processes multi-band spectral features while accurately preserving the original scenes’ spectral characteristics. Full article

Classifying multiband images acquired by advanced sensors, including those mounted on satellites, is a central task in remote sensing and environmental monitoring. These sensors generate high-dimensional outputs rich in spectral and spatial information, enabling detailed analyses of Earth’s surface. However, the complexity of such data presents substantial challenges to achieving both accuracy and efficiency. To address these challenges, we tested the ensemble learning framework based on ResNet50, MobileNetV2, and DenseNet201, each trained on distinct three-channel representations of the input to capture complementary features. Training is conducted on the LCZ42 dataset of 400,673 paired Sentinel-1 SAR and Sentinel-2 multispectral image patches annotated with Local Climate Zone (LCZ) labels. Experiments show that our best ensemble surpasses several recent state-of-the-art methods on the LCZ42 benchmark. Full article