Search Results (6,182)

Background/Objectives: Based on Blind Source Separation and the use of multispectral imaging, the new approach we propose in this paper aims to improve the estimation of the concentrations of the main skin chromophores (melanin, oxyhemoglobin and deoxyhemoglobin), while considering shading as a fully-fledged source. Methods: In this paper, we demonstrate that the use of the Infra-Red spectral band, in addition to the traditional RGB spectral bands of dermatological images, allows us to model the image provided by each spectral band as a mixture of the concentrations of the three chromophores in addition to that of the shading, which are estimated through four steps using Blind Source Separation. Results: We studied the performance of our new method on a database of real multispectral dermatological images of melanoma by proposing a new quantitative performances measurement criterion based on mutual information. We then validated these performances on a database of multispectral dermatological images that we simulated using our own new protocol. Conclusions: All the results obtained demonstrated the effectiveness of our new approach for estimating the concentrations of the skin chromophores from a multispectral dermatological image, compared to traditional approaches that consist of using only the RGB image by neglecting shading. Full article

As urbanization accelerates, characteristics of urban spatial expansion play a significant role in the future utilization of land resources, the protection of the ecological environment, and the coordinated development of population and land. In this study, Yunnan and Guizhou provinces were selected as the study area, and the 2013–2021 National Polar-Orbiting Partnership’s Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) nighttime light (NTL) data were utilized for spatial and temporal change analysis of urban built-up areas. Firstly, the built-up areas in Yunnan and Guizhou provinces were extracted through ENUI (Enhanced Nighttime Lighting Urban Index) indices, and then the urban expansion speed and urban center of gravity migration were constructed and used to explore and analyze the spatial and temporal change and expansion characteristics of built-up areas in Yunnan and Guizhou provinces. The results showed the following. (1) Due to the complementarity between data types, such as NTL, EVI, NDBI, and NDWI, ENUI has better performance in expressing urban characteristics. (2) Influenced by national and local policies, such as “One Belt, One Road”, transportation infrastructure construction, geographic location, the historical background, and other factors, the urban expansion rate of Yunnan and Guizhou provinces in general showed a continuous advancement from 2013 to 2021, and there were three years in which the expansion rate was positive. (3) The center of gravity migration distance of most cities in Guizhou Province shows a trend of increasing and then decreasing, while the center of gravity migration distance in Yunnan Province shows a trend of continuous decrease in general. From the perspective of migration direction, Guizhou Province has the largest number of migrations to the northeast, while Yunnan Province has the largest number of migrations to the southeast. (4) Influenced by policy, economy, population, geography, and other factors, urban compactness in Yunnan and Guizhou provinces continued to grow from 2013 to 2021. The results of this study can help us better understand urbanization in western China, reveal the urban expansion patterns and spatial characteristics of Yunnan and Guizhou provinces, and provide valuable references for development planning and policymaking in Yunnan and Guizhou provinces. Full article

Lung cancer is a major threat to human health and a leading cause of death. Accurate localization of tumors in vivo is crucial for subsequent treatment. In recent years, fluorescent imaging technology has become a focal point in tumor diagnosis and treatment due to its high sensitivity, strong selectivity, non-invasiveness, and multifunctionality. Molecular probes-based fluorescent imaging not only enables real-time in vivo imaging through fluorescence signals but also integrates therapeutic functions, drug screening, and efficacy monitoring to facilitate comprehensive diagnosis and treatment. Among them, near-infrared (NIR) fluorescence imaging is particularly prominent due to its improved in vivo imaging effect. This trend toward multifunctionality is a significant aspect of the future advancement of fluorescent imaging technology. In the past years, great progress has been made in the field of NIR fluorescence imaging for lung cancer management, as well as the emergence of new problems and challenges. This paper generally summarizes the application of NIR fluorescence imaging technology in these areas in the past five years, including the design, detection principles, and clinical applications, with the aim of advancing more efficient NIR fluorescence imaging technologies to enhance the accuracy of tumor diagnosis and treatment. Full article

The preparation of infrared reference images is of great significance for improving the accuracy and precision of infrared imaging guidance. However, collecting infrared data on-site is difficult and time-consuming. Fortunately, the infrared images can be obtained from the corresponding visible-light images to enrich the infrared data. To this end, this present work proposes an image translation algorithm that converts visible-light images to infrared images. This algorithm, named V2IGAN, is founded on the visual state space attention module and multi-scale feature contrastive learning loss. Firstly, we introduce a visual state space attention module designed to sharpen the generative network’s focus on critical regions within visible-light images. This enhancement not only improves feature extraction but also bolsters the generator’s capacity to accurately model features, ultimately enhancing the quality of generated images. Furthermore, the method incorporates a multi-scale feature contrastive learning loss function, which serves to bolster the robustness of the model and refine the detail of the generated images. Experimental results show that the V2IGAN method outperforms existing typical infrared image generation techniques in both subjective visual assessments and objective metric evaluations. This suggests that the V2IGAN method is adept at enhancing the feature representation in images, refining the details of the generated infrared images, and yielding reliable, high-quality results. Full article

The complexity of urban road scenes at night and the inadequacy of visible light imaging in such conditions pose significant challenges. To address the issues of insufficient color information, texture detail, and low spatial resolution in infrared imagery, we propose an enhanced infrared detection model called LFIR-YOLO, which is built upon the YOLOv8 architecture. The primary goal is to improve the accuracy of infrared target detection in nighttime traffic scenarios while meeting practical deployment requirements. First, to address challenges such as limited contrast and occlusion noise in infrared images, the C2f module in the high-level backbone network is augmented with a Dilation-wise Residual (DWR) module, incorporating multi-scale infrared contextual information to enhance feature extraction capabilities. Secondly, at the neck of the network, a Content-guided Attention (CGA) mechanism is applied to fuse features and re-modulate both initial and advanced features, catering to the low signal-to-noise ratio and sparse detail features characteristic of infrared images. Third, a shared convolution strategy is employed in the detection head, replacing the decoupled head strategy and utilizing shared Detail Enhancement Convolution (DEConv) and Group Norm (GN) operations to achieve lightweight yet precise improvements. Finally, loss functions, PIoU v2 and Adaptive Threshold Focal Loss (ATFL), are integrated into the model to better decouple infrared targets from the background and to enhance convergence speed. The experimental results on the FLIR and multispectral datasets show that the proposed LFIR-YOLO model achieves an improvement in detection accuracy of 4.3% and 2.6%, respectively, compared to the YOLOv8 model. Furthermore, the model demonstrates a reduction in parameters and computational complexity by 15.5% and 34%, respectively, enhancing its suitability for real-time deployment on resource-constrained edge devices. Full article

Eucalyptus plantations with fast growth and short rotation play an important role in improving economic conditions for local farmers and governments. It is necessary to map and update eucalyptus distribution in a timely manner, but to date, there is a lack of suitable approaches for quickly mapping its spatial distribution in a large area. This research aims to develop a uniform procedure to map eucalyptus distribution at a regional scale using the Sentinel-2 imagery on the Google Earth Engine (GEE) platform. Different seasonal Senstinel-2 images were first examined, and key vegetation indices from the selected seasonal images were identified using random forest and Pearson correlation analysis. The selected key vegetation indices were then normalized and summed to produce new indices for mapping eucalyptus distribution based on the calculated best cutoff values using the ROC (Receiver Operating Characteristic) curve. The uniform procedure was tested in both experimental and test sites and then applied to the entire Fujian Province. The results indicated that the best season to distinguish eucalyptus forests from other forest types was winter. The composite indices for eucalyptus–coniferous forest separation (${\mathrm{C}\mathrm{I}}_{\mathrm{E}\mathrm{C}}$) and for eucalyptus–broadleaf forest separation (${\mathrm{C}\mathrm{I}}_{\mathrm{E}\mathrm{B}}$), which were synthesized from the enhanced vegetation index (EVI), plant senescing reflectance index (PSRI), shortwave infrared water stress index (SIWSI), and MERIS terrestrial chlorophyll index (MTCI), can effectively differentiate eucalyptus from other forest types. The proposed procedure with the best cutoff values (0.58 for ${\mathrm{C}\mathrm{I}}_{\mathrm{E}\mathrm{C}}$ and 1.29 for ${\mathrm{C}\mathrm{I}}_{\mathrm{E}\mathrm{B}}$) achieved accuracies of above 90% in all study sites. The eucalyptus classification accuracies in Fujian Province, with a producer’s accuracy of 91%, user’s accuracy of 97%, and overall accuracy of 94%, demonstrate the strong robustness and transferability of this proposed procedure. This research provided a new insight into quickly mapping eucalyptus distribution in subtropical regions. However, more research is still needed to explore the robustness and transferability of this proposed method in tropical regions or in other subtropical regions with different environmental conditions. Full article

Four novel Pseudomonas strains with record resistance to copper (Cu²⁺) previously isolated from ecologically diverse samples (P. lactis UKR1, P. panacis UKR2, P. veronii UKR3, and P. veronii UKR4) were tested against sonochemically synthesised copper-oxide (I) (Cu₂O) and copper-oxide (II) (CuO) nanoparticles (NPs). Nanomaterials characterisation by X-ray diffractometry (XRD), X-ray photoelectron spectroscopy (XPS), Fourier transform infrared spectroscopy (FTIR), and High-Resolution Transmission Electron Microscopy (HRTEM) confirmed the synthesis of CuO and Cu₂O NPs. CuO NPs exhibited better performance in inhibiting bacterial growth due to their heightened capacity to induce oxidative stress. The greater stability and geometrical shape of CuO NPs were disclosed as important features associated with bacterial cell toxicity. SEM and TEM images confirmed that both NPs caused membrane disruption, altered cell morphology, and pronounced membrane vesiculation, a distinctive feature of bacteria dealing with stressor factors. Finally, Cu₂O and CuO NPs effectively decreased the biofilm-forming ability of the Cu²⁺-resistant UKR strains as well as degraded pre-established biofilm, matching NPs’ antimicrobial performance. Despite the similarities in the mechanisms of action revealed by both NPs, distinctive behaviours were also detected for the different species of wild-type Pseudomonas analysed. In summary, these findings underscore the efficacy of nanotechnology-driven strategies for combating metal tolerance in bacteria. Full article

The fusion of infrared and visible images together can fully leverage the respective advantages of each, providing a more comprehensive and richer set of information. This is applicable in various fields such as military surveillance, night navigation, environmental monitoring, etc. In this paper, a novel infrared and visible image fusion method based on sparse representation and guided filtering in Laplacian pyramid (LP) domain is introduced. The source images are decomposed into low- and high-frequency bands by the LP, respectively. Sparse representation has achieved significant effectiveness in image fusion, and it is used to process the low-frequency band; the guided filtering has excellent edge-preserving effects and can effectively maintain the spatial continuity of the high-frequency band. Therefore, guided filtering combined with the weighted sum of eight-neighborhood-based modified Laplacian (WSEML) is used to process high-frequency bands. Finally, the inverse LP transform is used to reconstruct the fused image. We conducted simulation experiments on the publicly available TNO dataset to validate the superiority of our proposed algorithm in fusing infrared and visible images. Our algorithm preserves both the thermal radiation characteristics of the infrared image and the detailed features of the visible image. Full article

Plant nitrogen concentration (PNC) is a key indicator reflecting the growth and development status of plants. The timely and accurate monitoring of plant PNC is of great significance for the refined management of crop nutrition in the field. The rapidly developing sensor technology provides a powerful means for monitoring crop PNC. Although RGB images have rich spatial information, they lack the spectral information of the red edge and near infrared bands, which are more sensitive to vegetation. Conversely, multispectral images offer superior spectral resolution but typically lag in spatial detail compared to RGB images. Therefore, the purpose of this study is to improve the accuracy and efficiency of crop PNC monitoring by combining the advantages of RGB images and multispectral images through image-fusion technology. This study was based on the booting, heading, and early-filling stages of winter wheat, synchronously acquiring UAV RGB and MS data, using Gram–Schmidt (GS) and principal component (PC) image-fusion methods to generate fused images and evaluate them with multiple image-quality indicators. Subsequently, models for predicting wheat PNC were constructed using machine-selection algorithms such as RF, GPR, and XGB. The results show that the RGB_B1 image contains richer image information and more image details compared to other bands. The GS image-fusion method is superior to the PC method, and the performance of fusing high-resolution RGB_B1 band images with MS images using the GS method is optimal. After image fusion, the correlation between vegetation indices (VIs) and wheat PNC has been enhanced to varying degrees in different growth periods, significantly enhancing the response ability of spectral information to wheat PNC. To comprehensively assess the potential of fused images in estimating wheat PNC, this study fully compared the performance of PNC models before and after fusion using machine learning algorithms such as Random Forest (RF), Gaussian Process Regression (GPR), and eXtreme Gradient Boosting (XGB). The results show that the model established by the fusion image has high stability and accuracy in a single growth period, multiple growth periods, different varieties, and different nitrogen treatments, making it significantly better than the MS image. The most significant enhancements were during the booting to early-filling stages, particularly with the RF algorithm, which achieved an 18.8% increase in R², a 26.5% increase in RPD, and a 19.7% decrease in RMSE. This study provides an effective technical means for the dynamic monitoring of crop nutritional status and provides strong technical support for the precise management of crop nutrition. Full article

In this work, a comprehensive characterization of microplastic samples collected from unique geographical locations, including the Mediterranean Sea, Strait of Gibraltar, Western Atlantic Ocean and Bay of Biscay utilizing advanced hyperspectral imaging (HSI) techniques working in the short-wave infrared range (1000–2500 nm) is presented. More in detail, an ad hoc hierarchical classification approach was developed and applied to optimize the identification of polymers. Morphological and morphometrical attributes of microplastic particles were simultaneously measured by digital image processing. Results showed that the collected microplastics are mainly composed, in decreasing order of abundance, by polyethylene (PE), polypropylene (PP), polystyrene (PS) and expanded polystyrene (EPS), in agreement with the literature data related to marine microplastics. The investigated microplastics belong to the fragments (86.8%), lines (9.2%) and films (4.0%) categories. Rigid (thick-walled) fragments were found at all sampling sites, while film-type microplastics and lines were absent in some samples from the Mediterranean Sea and the Western Atlantic Ocean. Rigid fragments and lines are mainly made of PE, whereas PP is the most common polymer for the film category. Average Feret diameter of microplastic fragments decreases from EPS (3–4 mm) to PE (2–3 mm) and PP (1–2 mm). The setup strategies illustrate that the HSI-based approach enables the classification of the polymers constituting microplastic particles and, at the same time, to measure and classify them by shape. Such multiple characterization of microplastic samples at the individual level is proposed as a useful tool to explore the environmental selection of microplastic features (i.e., composition, category, size, shape) and to advance the understanding of the role of weathering, hydrodynamic and other phenomena in their transport and fragmentation. Full article

Ultraviolet solar radiation at the Earth’s surface significantly impacts both human health and ecosystems. A biologically effective daily radiant exposure (BEDRE) model is proposed for various biological processes with an analytical formula for its action spectrum. The following processes are considered: erythema formation, previtamin D3 synthesis, psoriasis clearance, and inactivation of SARS-CoV-2 virions. The BEDRE model is constructed by multiplying the synthetic BEDRE value under cloudless conditions by a cloud modification factor (CMF) parameterizing the attenuation of radiation via clouds. The CMF is an empirical function of the solar zenith angle (SZA) at midday and the daily clearness index from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) measurements on board the second-generation Meteosat satellites. Total column ozone, from MERRA-2 reanalysis, is used in calculations of clear-sky BEDRE values. The proposed model was trained and validated using data from several European ground-based spectrophotometers and biometers for the periods 2014–2023 and 2004–2013, respectively. The model provides reliable estimates of BEDRE for all biological processes considered. Under snow-free conditions and SZA < 45° at midday, bias and standard deviation of observation-model differences are approximately ±5% and 15%, respectively. The BEDRE model can be used as an initial validation tool for ground-based UV data. Full article

The Enhanced Wide-field Galaxy Classification Network (EWGC) is a novel architecture designed to classify spiral and elliptical galaxies using Wide-field Infrared Survey Explorer (WISE) images. The EWGC achieves an impressive classification accuracy of 90.02%, significantly outperforming the previously developed WGC network and underscoring its superior performance in galaxy morphology classification. Remarkably, the network demonstrates a consistent accuracy of 90.02% when processing both multi-target and single-target images. Such robustness indicates the EWGC’s versatility and potential for various applications in galaxy classification tasks. Full article

Multifunctional hydrogel dressings remain highly sought after for the promotion of skin wound regeneration. In the present study, multifunctional CHS-DA/HACC (CH) hydrogels with an interpenetrated network were constructed using hydroxypropyl trimethyl ammonium chloride modified chitosan (HACC) and dopamine-modified chondroitin sulfate (CHS-DA), using genipin as crosslinker. The synthesis of HACC and CHS-DA was effectively confirmed using Fourier transform infrared (FT-IR) analysis and ¹H nuclear magnetic resonance (¹H NMR) spectroscopy. The prepared CH hydrogels exhibited a network of interconnected pores within the microstructure. Furthermore, rheological testing demonstrated that CH hydrogels exhibited strong mechanical properties, stability, and injectability. Further characterization investigations showed that the CH hydrogels showed favorable self-healing and self-adhesion properties. It was also shown that increasing HACC concentration ratio was positively correlated with the antibacterial activity of CH hydrogels, as evidenced by their resistance to Escherichia coli and Staphylococcus aureus. Additionally, Cell Counting Kit-8 (CCK-8) tests, fluorescent images, and a cell scratch assay demonstrated that CH hydrogels had good biocompatibility and cell migration ability. The multifunctional interpenetrated network hydrogels were shown to have good antibacterial properties, antioxidant properties, stable storage modulus and loss modulus, injectable properties, self-healing properties, and biocompatibility, highlighting their potential as wound dressings in wound healing applications. Full article

This study aimed to develop predictive models for rock hardness and abrasivity based on hyperspectral imaging data, providing valuable information without interrupting the mining processes. The data collection stage first involved scanning 159 rock samples collected from 6 different blasted rock piles using visible and near-infrared (VNIR) and short-wave infrared (SWIR) sensors. The hardness and abrasivity of the samples were then determined through Leeb rebound hardness (LRH) and Cerchar abrasivity index (CAI) tests, respectively. The data preprocessing involved radiometric correction, background removal, and staking VNIR and SWIR images. An integrated approach based on K-means clustering and the band ratio concept was employed for feature extraction, resulting in 28 band-ratio-based features. Afterward, the random forest regressor (RFR) algorithm was employed to develop predictive models for rock hardness and abrasivity separately. The performance assessment showed that the developed models can estimate rock hardness and abrasivity of unseen data with R² scores of 0.74 and 0.79, respectively, with the most influential features located mainly within the SWIR region. The results indicate that integrated hyperspectral data and RFR technique have strong potential for practical and efficient rock hardness and abrasivity characterization during mining processes. Full article

Fourier-transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and Scanning electron microscopy (SEM) were used to study the effects of heat treatments and water removal by freeze-drying after different time intervals (6, 12, 24, 48, and 72 h) on the molecular structure of potato tubers. SEM images show structural differences between raw (RP), microwaved (MP), and boiled potato (BP). MP showed a cracked structure. BP was able to re-associate into a granule-like structure after 6 h of freeze-dying, whereas RP had dried granules within a porous matrix after 24 h of freeze-drying. These results are consistent with the moisture content and FTIR results for MP and BP, which demonstrated dried spectra after 6 h of freeze-drying and relatively coincided with RP results after 24 h of freeze-drying. Additionally, three types of hydrogen bonds have been characterized between water and starch, and the prevalence of water very weakly bound to starch has also been detected. The relative crystallinity (RC) was increased by thermal treatment, whereby microwaving recorded the highest value. A comparison of the FTIR and XRD results indicated that freeze-drying treatment overcomes heat effects to generate an integral starch molecule. Full article