Search Results (196)

Fluorescence imaging is crucial for providing detailed information in clinical practice. However, traditional first near-infrared (NIR-I) dyes such as indocyanine green (ICG) exhibit limitations such as shallow penetration depth, low contrast, and suboptimal clarity due to light scattering and autofluorescence. To overcome these drawbacks, we utilized a novel amphiphilic second near-infrared (NIR-II) aggregation-induced emission (AIE) probe (TCP) with an emission range beyond 1300 nm (NIR-IIa). Using approximately 200 co-registered NIR-I/NIR-IIa image pairs acquired with TCP, we trained a SwinUnet-based deep learning model to transform low-quality NIR-I ICG images into high-resolution NIR-IIa-like images. Owing to its superior brightness and photostability, TCP enhances in vivo fluorescent angiography, offering clearer vascular details and a higher signal-to-background ratio (SBR) in the NIR-IIa region, 2.6-fold higher than that of ICG in the NIR-I region. The deep learning model successfully converted blurred NIR-I images into high-SBR NIR-IIa-like images, achieving rapid imaging speeds without compromising quality. This work introduces a synergistic “probe-plus-AI” paradigm that substantially improves both the quality and speed of clinical fluorescence imaging, providing a pathway that is immediately translatable to enhanced diagnostics and image-guided surgery. Full article

Microplastics (MPs) are synthetic environmental pollutants increasingly linked to adverse human health effects. To study their biological impact, researchers require access to environmentally relevant MPs that can be accurately tracked in biological systems. However, most ambient MPs are composed of non-conjugated polymers that lack intrinsic fluorescence, limiting their utility in live-cell or in vivo imaging. Addressing this challenge, we present two alternative labeling approaches that enable visualization, tracking, and quantification of MPs. First, we stained nylon and polypropylene MPs with Rhodamine 6G, a fluorescent dye known for its stability and compatibility with in vivo applications. These labeled MPs retained strong fluorescence in murine lung tissue for up to one week, as confirmed by fluorescent microscopy. Second, we conjugated aminated polystyrene microspheres with IRDye-800CW, a near-infrared fluorophore that enables high-resolution imaging with minimal tissue autofluorescence via an In Vivo Imaging System and confocal microscopy. In vivo experiments revealed organ-specific accumulation of IRDye-labeled MPs, with a 2.8-fold increase in the liver and a 5-fold increase in spleen compared to controls, detectable up to 72 h post-injection. These labeling strategies provide researchers with practical tools to visualize and study the biodistribution of MPs in biological systems, advancing efforts to understand their health implications. Full article

Maize, a globally important crop, is highly susceptible to aflatoxin contamination, posing a serious threat. Therefore, accurate detection of aflatoxin levels in maize is of critical importance. In this study, the Multi-Scale Feature Network with Efficient Channel Attention (MSFNet-ECA) model, based on near-infrared hyperspectral imaging combined with deep learning techniques was developed to analyze the content of aflatoxin B₁ (AFB₁) in maize. Three data augmentation methods—multiplicative random scaling, bootstrap resampling, and Wasserstein generative adversarial networks (WGAN)—were compared with various preprocessing strategies to assess their impact on model performance. Multiplicative random scaling combined with second derivative (D2) preprocessing yielded the best predictive performance for the MSFNet-ECA model. Using this augmentation, the MSFNet-ECA model outperformed four conventional models (partial least squares regression (PLSR), support vector regression (SVR), extreme learning machine (ELM), and one-dimensional convolutional neural network (1D-CNN)), achieving a root mean square error of prediction (RMSEP) of 2.3 μg·kg⁻¹, coefficient of determination for prediction ($R_p^2$) of 0.99, and the residual predictive deviation (RPD) of 9, with accuracy improvements of 86.4%, 79.1%, 71.3%, and 42.5%, respectively. This finding demonstrates that applying data augmentation methods substantially improves the predictive performance of hyperspectral chemometric models driven by deep learning. Moreover, when combined with data augmentation techniques, the proposed MSFNet-ECA model can accurately predict AFB₁ content in maize, offering an efficient and reliable tool for hyperspectral applications in food quality and safety monitoring. Full article

Intermediate-mass black holes (IMBHs; $M_{\mathrm{BH}}\approx {10}^{3–5} {\mathrm{M}}_{\odot }$) play a critical role in understanding the formation of supermassive black holes in the early universe. In this study, we expand on Nguyen et al.’s simulated measurements of IMBH masses using stellar kinematics, which will be observed with the High Angular Resolution Monolithic Optical and Near-infrared Integral (HARMONI) field spectrograph on the Extremely Large Telescope (ELT) up to a distance of 20 Mpc. Our sample focuses on both the Virgo Cluster in the northern sky and the Fornax Cluster in the southern sky. We begin by identifying dwarf galaxies hosting nuclear star clusters, which are thought to be nurseries for IMBHs in the local universe. As a case study, we conduct simulations for FCC 119, the second faintest dwarf galaxy in the Fornax Cluster at 20 Mpc, which is also fainter than most of the Virgo Cluster members. We use the galaxy’s surface brightness profile from Hubble Space Telescope (HST) imaging, combined with an assumed synthetic spectrum, to create mock observations with the HSIM simulator and Jeans Anisotropic Models (JAMs). These mock HARMONI data cubes are analyzed as if they were real observations, employing JAMs within a Bayesian framework to infer IMBH masses and their associated uncertainties. We find that ELT/HARMONI can detect the stellar kinematic signature of an IMBH and accurately measure its mass for $M_{\mathrm{BH}}\gtrsim {10}^5{\mathrm{M}}_{\odot }$ out to distances of ∼20 Mpc. Full article

Climate change is accelerating, and the monitoring of plant phenology is becoming increasingly important. In response to this need, many vegetation indices (VIs) and analytical methods have been developed. However, many VIs are vulnerable to uncertainties caused by snowmelt, making them potentially unsuitable for monitoring spring phenology in forested regions where leaf flush (start of season, SOS) begins simultaneously with snowmelt. Although several VIs for snowy regions have been proposed, most of them were designed for tundra vegetation, such as grasslands. Currently, no VI or analytical method specifically suited for snowy forested regions has been firmly established. Similarly, there is still no well-established method for continuously monitoring autumn coloration. In this study, we propose the use of hue, one of the components of the HSV model, for remote sensing of plant phenology. Hue quantifies differences in object color and is expected to facilitate clearer distinction of snow influence. It may also enable accurate detection of canopy color transitions, such as autumn coloration. We evaluate the applicability of hue to remote sensing using in situ spectroradiometer observations collected from five sites of the Phenological Eyes Network (PEN), which represent a range of ecosystems—including forests, grasslands, and paddy fields—as well as the relative spectral response (RSR) of the Second-generation Global Imager (SGLI) onboard the GCOM-C satellite operated by JAXA (Japan Aerospace Exploration Agency). The results suggest that hue is more robust to snow-related uncertainties than traditional VIs (NDVI, EVI, CCI, NDGI) and may also be effective for quantifying autumn coloration. Hue is calculated solely from blue, green, and red reflectance, without relying on near-infrared (NIR) or shortwave infrared (SWIR) channels. Since blue, green and red channels are available on almost all optical satellite sensors, hue may offer broader applicability than many traditional VIs. Full article

Pomegranate blackheart disease, as an internal disease affecting the global pomegranate industry, is difficult to identify externally and urgently requires non-destructive detection methods for rapid diagnosis. This study established discriminative models for blackheart disease severity in pomegranates by using near-infrared (NIR) spectroscopy and soft X-ray imaging techniques. The results showed that the optimal NIR-based discriminative model, constructed with a Random Forest (RF) algorithm based on spectra preprocessed by the second-derivative (D2) denoising and a Competitive Adaptive Reweighted Sampling (CARS) algorithm, achieved a prediction set accuracy of 86.00%; the optimal soft X-ray imaging-based discriminative model, built with an RF algorithm using textural features extracted from images preprocessed by median filtering and a Contrast-Limited Adaptive Histogram Equalization (CLAHE) algorithm combined with gray-level co-occurrence matrix (GLCM) and gray-gradient co-occurrence matrix (GGCM) algorithms, reached a prediction set accuracy of 93.10%. In terms of model performance, the model based on soft X-ray imaging exhibited superior performance. Both techniques possess distinct advantages and limitations yet enable non-destructive detection of pomegranate blackheart disease. Further technical optimizations in the future could provide enhanced support for the healthy development of the pomegranate industry. Full article

The Flexible Combined Imager (FCI), developed as the next-generation imager for the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Meteosat Third Generation (MTG) satellite series, represents a significant advancement over its predecessor, SEVIRI, on the Meteosat Second Generation (MSG) satellites. FCI offers more spectral bands, higher spatial resolution, and faster imaging capabilities, supporting a wide range of applications in weather forecasting, climate monitoring, and environmental analysis. On 13 January 2024, the FCI onboard MTG-I1 (renamed Meteosat-12 in December 2024) experienced a critical anomaly involving the failure of its onboard Calibration and Obturation Mechanism (COM). As a result, the use of the COM was discontinued to preserve operational safety, leaving the instrument dependent on alternative calibration methods. This loss of onboard calibration presents immediate challenges, particularly for the infrared channels, including image artifacts (e.g., striping), reduced radiometric accuracy, and diminished stability. To address these issues, EUMETSAT implemented an external calibration approach leveraging algorithms from the Global Space-based Inter-Calibration System (GSICS). The inter-calibration algorithm transfers stable and accurate calibration from the Infrared Atmospheric Sounding Interferometer (IASI) hyperspectral instrument aboard Metop-B and Metop-C satellites to FCI’s infrared channels daily, ensuring continued data quality. Comparisons with Cross-track Infrared Sounder (CrIS) data from NOAA-20 and NOAA-21 satellites using a similar algorithm is then used to validate the radiometric performance of the calibration. This confirms that the external calibration method effectively compensates for the absence of onboard blackbody calibration for the infrared channels. For the visible and near-infrared channels, slower degradation rates and pre-anomaly calibration ensure continued accuracy, with vicarious calibration expected to become the primary source. This adaptive calibration strategy introduces a novel paradigm for in-flight calibration of geostationary instruments and offers valuable insights for satellite missions lacking onboard calibration devices. This paper details the COM anomaly, the external calibration process, and the broader implications for future geostationary satellite missions. Full article

The reconstruction of high-resolution (HR) remote sensing images (RSIs) from low-resolution (LR) counterparts is a critical task in remote sensing image super-resolution (RSISR). Recent advancements in convolutional neural networks (CNNs) and Transformers have significantly improved RSISR performance due to their capabilities in local feature extraction and global modeling. However, several limitations remain, including the underutilization of multi-scale features in RSIs, the limited receptive field of Swin Transformer’s window self-attention (WSA), and the computational complexity of existing methods. To address these issues, this paper introduces the NGSTGAN model, which employs an N-Gram Swin Transformer as the generator and a multi-attention U-Net as the discriminator. The discriminator enhances attention to multi-scale key features through the addition of channel, spatial, and pixel attention (CSPA) modules, while the generator utilizes an improved shallow feature extraction (ISFE) module to extract multi-scale and multi-directional features, enhancing the capture of complex textures and details. The N-Gram concept is introduced to expand the receptive field of Swin Transformer, and sliding window self-attention (S-WSA) is employed to facilitate interaction between neighboring windows. Additionally, channel-reducing group convolution (CRGC) is used to reduce the number of parameters and computational complexity. A cross-sensor multispectral dataset combining Landsat-8 (L8) and Sentinel-2 (S2) is constructed for the resolution enhancement of L8’s blue (B), green (G), red (R), and near-infrared (NIR) bands from 30 m to 10 m. Experiments show that NGSTGAN outperforms the state-of-the-art (SOTA) method, achieving improvements of 0.5180 dB in the peak signal-to-noise ratio (PSNR) and 0.0153 in the structural similarity index measure (SSIM) over the second best method, offering a more effective solution to the task. Full article

Cannabis sativa L. is cultivated for therapeutic and recreational use. Delta-9 tetrahydrocannabinol (THC) and cannabidiol (CBD) are primarily responsible for its psychoactive and medicinal effects. As the global cannabis industry continues to expand, constant review and optimization of horticultural practices are needed to ensure a reliable harvest and improved crop quality. There is currently uncertainty about the optimal harvest time of C. sativa, i.e., when cannabinoid concentrations are at their highest during inflorescence maturation. At present, growers observe the color transition of stigmas from white to amber as an indicator of harvest time. This research investigates the relationship between stigma color and cannabinoid concentration using liquid chromatography–mass spectrometry (LCMS) and digital image analysis. Additionally, early screening prediction models have also been developed for six cannabinoids using near-infrared (NIR) spectroscopy and LCMS to assist in early cannabinoid determination. Among the genotypes grown, 22 of 25 showed cannabinoid concentration peaks between the third (mostly amber) and fourth (fully amber) stages; however, some genotypes peaked within the first (no amber) and second (some amber) stages. We have determined that the current ‘rule of thumb’ of harvesting when a cannabis plant is mostly amber is still a useful approximation in most cases; however, studies on individual genotypes should be performed to determine their individual optimal harvest time based on the desired cannabinoid profile or total cannabinoid concentration. Full article

The multi-band atmospheric correction algorithms, now referred to as remote sensing reflectance (R_rs) algorithms, have been implemented on a NASA computing facility for global remote sensing of ocean color and atmospheric aerosol parameters from data acquired with several satellite instruments, including the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi spacecraft platform. These algorithms are based on the 2-band version of the SeaWiFS (Sea-Viewing Wide Field-of-View Sensor) algorithm. The bands centered near 0.75 and 0.865 μm are used for atmospheric corrections. In order to obtain high-quality R_rs values over Case 1 waters (deep clear ocean waters), strict masking criteria are implemented inside these algorithms to mask out thin clouds and very turbid water pixels. As a result, R_rs values are often not retrieved over bright Case 2 waters. Through our analysis of VIIRS data, we have found that spatial features of bright Case 2 waters are observed in VIIRS visible band images contaminated by thin cirrus clouds. In this article, we describe methods of combining cirrus and aerosol corrections to improve spatial coverage in R_rs retrievals over Case 2 waters. One method is to remove cirrus cloud effects using our previously developed operational VIIRS cirrus reflectance algorithm and then to perform atmospheric corrections with our updated version of the spectrum-matching algorithm, which uses shortwave IR (SWIR) bands above 1 μm for retrieving atmospheric aerosol parameters and extrapolates the aerosol parameters to the visible region to retrieve water-leaving reflectances of VIIRS visible bands. Another method is to remove the cirrus effect first and then make empirical atmospheric and sun glint corrections for water-leaving reflectance retrievals. The two methods produce comparable retrieved results, but the second method is about 20 times faster than the spectrum-matching method. We compare our retrieved results with those obtained from the NASA VIIRS R_rs algorithm. We will show that the assumption of zero water-leaving reflectance for the VIIRS band centered at 0.75 μm (M6) over Case 2 waters with the NASA R_rs algorithm can sometimes result in slight underestimates of water-leaving reflectances of visible bands over Case 2 waters, where the M6 band water-leaving reflectances are actually not equal to zero. We will also show conclusively that the assumption of thin cirrus clouds as ‘white’ aerosols during atmospheric correction processes results in overestimates of aerosol optical thicknesses and underestimates of aerosol Ångström coefficients. Full article

The accurate detection of citrus surface defects is essential for automated citrus sorting to enhance the commercialization of the citrus industry. However, previous studies have only focused on single-modal defect detection using visible light images (RGB) or near-infrared light images (NIR), without considering the feature fusion between these two modalities. This study proposed an RGB-NIR multimodal fusion method to extract and integrate key features from both modalities to enhance defect detection performance. First, an RGB-NIR multimodal dataset containing four types of citrus surface defects (cankers, pests, melanoses, and cracks) was constructed. Second, a Multimodal Compound Domain Attention Fusion (MCDAF) module was developed for multimodal channel fusion. Finally, MCDAF was integrated into the feature extraction network of Real-Time DEtection TRansformer (RT-DETR). The experimental results demonstrated that RT-DETR-MCDAF achieved Precision, Recall, mAP@0.5, and mAP@0.5:0.95 values of 0.914, 0.919, 0.90, and 0.937, respectively, with an average detection performance of 0.598. Compared with the model RT-DETR-RGB&NIR, which used simple channel concatenation fusion, RT-DETR-MCDAF improved the performance by 1.3%, 1.7%, 1%, 1.5%, and 1.7%, respectively. Overall, the proposed model outperformed traditional channel fusion methods and state-of-the-art single-modal models, providing innovative insights for commercial citrus sorting. Full article

Every year about fifty volcanoes erupt on average, posing a serious threat for populations living in the neighboring areas. To mitigate the volcanic risk, many satellite monitoring systems have been developed. Information from the medium infrared (MIR) and thermal infrared (TIR) bands of sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Visible Infrared Imaging Radiometer Suite (VIIRS) is commonly exploited for this purpose. However, the potential of daytime shortwave infrared (SWIR) observations from the Sea and Land Surface Temperature Radiometer (SLSTR) aboard Sentinel-3 satellites in supporting the near-real-time monitoring of thermal volcanic activity has not been fully evaluated so far. In this work, we assess this potential by exploring the contribution of a normalized hotspot index (NHI) in the monitoring of the recent Home Reef (Tonga Islands) eruption. By analyzing the time series of the maximum NHI_SWIR value, computed over the Home Reef area, we inferred information about the waxing/waning phases of lava effusion during four distinct subaerial eruptions. The results indicate that the first eruption phase (September–October 2022) was more intense than the second one (September–November 2023) and comparable with the fourth eruptive phase (June–August 2024) in terms of intensity level; the third eruption phase (January 2024) was more difficult to investigate because of cloudy conditions. Moreover, by adapting the NHI algorithm to daytime SLSTR SWIR data, we found that the detected thermal anomalies complemented those in night-time conditions identified and quantified by the operational Level 2 SLSTR fire radiative power (FRP) product. This study demonstrates that NHI-based algorithms may contribute to investigating active volcanoes located even in remote areas through SWIR data at 500 m spatial resolution, encouraging the development of an automated processing chain for the near-real-time monitoring of thermal volcanic activity by means of night-time/daytime Sentinel-3 SLSTR data. Full article

The total organic carbon (TOC) content in lake sediments is an effective archive indicating past climate changes. However, the resolution of the TOC record has generally been limited by factors such as subsampling intervals, hampering further comprehension of past climate change. Recently, hyperspectral imaging technology has been increasingly employed to scan lake sediment cores, presenting new opportunities to reconstruct high-resolution sequences, but the reconstruction of long-term high-resolution TOC records using hyperspectral imaging and the climate implications have not been well studied. In this study, we scanned sedimentary cores from Wudalianchi Crater Lake in northeast China with a spatial resolution of 400 × 400 μm, utilizing visible and near-infrared (VNIR) hyperspectral imaging technology. Then, a partial least-squares regression (PLSR) model was constructed by comparing eight different preprocessing methods and optimally selecting the best spectral subset combined with a genetic algorithm (GA). Our analysis demonstrates that the PLSR model, constructed using 62 relevant bands selected by the Savitzky–Golay second derivative (D2) preprocessing method and GA, was the most reliable, with the validation set’s R-value reaching a high of 0.91 and RMSE as low as 1.18%. Notably, the spectral range of 656–669 nm showed a strong positive correlation with measured TOC, indicating its sensitivity for TOC estimation. Given this advantage, we reconstructed the TOC records of sediments from the Wudalianchi Crater Lake during the 38–13 ka BP period, which exhibited significant millennial-scale fluctuation events. These corresponded well with the millennial-scale events in pollen and TOC from Lake Sihailongwan, δ¹⁸O records of Greenland ice cores, and δ¹⁸O records from Asian stalagmites. Thus, the combination of hyperspectral imaging and the PLSR model is effective in reconstructing high-resolution TOC changes in lake sediments, which is essential for understanding climate change as well as carbon burial in lakes. Full article

We demonstrate high-resolution single-pixel imaging (SPI) in the visible and near-infrared wavelength ranges using an SPI framework that incorporates a novel, dedicated sampling scheme and a reconstruction algorithm optimized for the rapid imaging of highly sparse scenes at the native digital micromirror device (DMD) resolution of 1024 × 768. The reconstruction algorithm consists of two stages. In the first stage, the vector of SPI measurements is multiplied by the generalized inverse of the measurement matrix. In the second stage, we compare two reconstruction approaches: one based on an iterative algorithm and the other on a trained neural network. The neural network outperforms the iterative method when the object resembles the training set, though it lacks the generality of the iterative approach. For images captured at a compression of 0.41 percent, corresponding to a measurement rate of 6.8 Hz with a DMD operating at 22 kHz, the typical reconstruction time on a desktop with a medium-performance GPU is comparable to the image acquisition rate. This allows the proposed SPI method to support high-resolution dynamic SPI in a variety of applications, using a standard SPI architecture with a DMD modulator operating at its native resolution and bandwidth, and enabling the real-time processing of the measured data with no additional delay on a standard desktop PC. Full article

Anxi Tieguanyin belongs to the oolong tea category and is one of the top ten most famous teas in China. In this study, hyperspectral imaging (HSI) technology was combined with chemometric methods to achieve the rapid determination of free amino acid and tea polyphenol contents in Tieguanyin tea. Here, the spectral data of Tieguanyin tea samples of four quality grades were obtained via visible near-infrared hyperspectroscopy in the range of 400–1000 nm, and the free amino acid and tea polyphenol contents of the samples were detected. First derivative (1D), normalization (Nor), and Savitzky–Golay (SG) smoothing were utilized to preprocess the original spectrum. The characteristic wavelengths were extracted via principal component analysis (PCA), competitive adaptive reweighted sampling (CARS), and the successive projection algorithm (SPA). The contents of free amino acid and tea polyphenol in Tieguanyin tea were predicted by the back propagation (BP) neural network, partial least squares regression (PLSR), random forest (RF), and support vector machine (SVM). The results revealed that the free amino acid content of the clear-flavoured Tieguanyin was greater than that of the strong-flavoured type, that the tea polyphenol content of the strong-flavoured Tieguanyin was greater than that of the clear-flavoured type, and that the content of the first-grade product was greater than that of the second-grade product. The 1D preprocessing improved the resolution and sensitivity of the spectra. When using CARS, the number of wavelengths for free amino acids and tea polyphenols was reduced to 50 and 70, respectively. The combination of 1D and CARS is conducive to improving the accuracy of late modelling. The 1D-CARS-RF model had the highest accuracy in predicting the free amino acid (R_P² = 0.940, RMSEP = 0.032, and RPD = 4.446) and tea polyphenol contents (R_P² = 0.938, RMSEP = 0.334, and RPD = 4.474). The use of hyperspectral imaging combined with multiple algorithms can be used to achieve the fast and non-destructive prediction of free amino acid and tea polyphenol contents in Tieguanyin tea. Full article