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• Remote sensing for structural-hydrothermal alterations mapping in sub-tropical region. • The hydrothermal alteration zones are significantly related to areas of structural complexity. • Gold distribution closely associated with hydrothermal ore assemblage of gold-galena-pyrite-hematite. • The mineralization occurred during D 2 –D 3 as gold-bearing quartz veins controlled by strike-slip tectonics. Multispectral remote sensing data has great promise for mineral prospecting via regional-scale mapping of alteration zones and geological structures, once the method is calibrated using in-situ geological observations. The Borongo-Mborguene area, located in eastern Cameroon at the transition from sub-tropical to arid climates, hosts several alluvial gold deposits. In this investigation, maps developed by processing of Landsat-8 OLI images, ASTER satellite images, and SRTM DEM data were compared to results of a comprehensive field survey to locate zones of hydrothermal alteration and regional structural lineaments, and to verify the association of these features with gold mineralization. Specific Landsat-8 and ASTER band ratios, as well as principal component analysis of the multispectral images, were selected to isolate spectral signals that characterize particular groups of alteration minerals, such as clays, hydrated carbonates, hydrated sulphates, iron oxides, and iron hydroxides. Lineaments and their preferred orientations were extracted by filtering of the digital elevation data. A fuzzy logic approach was subsequently applied to the ensemble of resulting maps to highlight five areas of concentrated hydrothermal alteration and structural complexity as potential gold prospects (Mborguene, Boutila, Nandoungue, Borongo and Ouaden villages). Petrographic study of samples collected from the remotely-identified alteration zones revealed a hydrothermal ore assemblage of gold, galena, pyrite and hematite in mineralized quartz veins. Structural control of the alteration zones in the Borongo-Mborguene gold field was confirmed by field mapping along a shear zone. Hence, our petrographic and field studies indicate that the multispectral remote sensing data and data processing methods developed herein are appropriate for mineral exploration in remote sub-tropical areas of Africa. More generally, the computationally efficient and simple procedure developed here should yield accurate preliminary maps of the spatial distribution of hydrothermal halos and related mineral deposits in challenging mixed-type vegetated terrain around the globe. [ABSTRACT FROM AUTHOR]

3D multi-spectral imaging (MSI) of metal implants necessitates relatively long scan times. We implemented a fast isotropic 3D MSI technique at 3 T and compared its image quality and clinical utility to non-isotropic MSI in the evaluation of hip implants. Two musculoskeletal radiologists scored images from coronal proton density-weighted conventional MAVRIC-SL and an isotropic MAVRIC-SL sequence accelerated with robust-component-analysis on a 3-point scale (3: diagnostic, 2: moderately diagnostic, 1: non-diagnostic) for overall image quality, metal artifact, and visualization around femoral and acetabular components. Grades were compared using a signed Wilcoxon test. Images were evaluated for effusion, synovitis, osteolysis, loosening, pseudotumor, fracture, and gluteal tendon abnormalities. Reformatted axial and sagittal images for both sequences were subsequently generated and compared for image quality with the Wilcoxon test. Whether these reformats increased diagnostic confidence or revealed additional pathology, including findings unrelated to arthroplasty that may contribute to hip pain, was also compared using the McNemar test. Inter-rater agreement was measured by Cohen's kappa. 39 symptomatic patients with a total of 59 hip prostheses were imaged (mean age, 70 years ±9, 14 males, 25 females). Comparison scores between coronal images showed no significant difference in image quality, metal artifact, or visualization of the femur and acetabulum. Except for loosening, reviewers identified more positive cases of pathology on the original coronally-acquired isotropic sequence. In comparison of reformatted axial and sagittal images, the isotropic sequence scored significantly (p < 0.01) higher for overall image quality (3.0 vs 2.0) and produced significantly (p < 0.01) more cases of increased diagnostic confidence (42.4% vs 7.6%) or additional diagnoses (50.8% vs 22.9%). Inter-rater agreement was substantial (k = 0.798) for image quality. Mean scan times were 4.2 mins (isotropic) and 7.1 mins (non-isotropic). Compared to the non-isotropic sequence, isotropic 3D MSI was acquired in less time while maintaining diagnostically acceptable image quality. It identified more pathology, including postoperative complications and potential pain-generating pathology unrelated to arthroplasty. This fast isotropic 3D MSI sequence demonstrates promise for improving diagnostic evaluation of symptomatic hip prostheses at 3 T while simultaneously reducing scan time. • Accelerated isotropic 3D MSI at 3 T is clinically feasible in hip implants. • Accelerated isotropic 3D MSI has good visualization of postoperative complications. • This technique can facilitate formation of fast MRI protocol for hip implant patients. [ABSTRACT FROM AUTHOR]

Hyperspectral (HS) satellites like PRISMA (PRecursore IperSpettrale della Missione Applicativa) offer remarkable capabilities, yet they are constrained by a relatively coarse spatial resolution, curbing their efficacy in those applications that require pinpoint accuracy. Here we propose a fusion process, aimed at the enhancement of PRISMA HS spatial resolution by using the spatial and spectral information of Sentinel-2 multispectral (MS) data (HS-MS fusion process), validated against four airborne HS flights simultaneous to satellite overpasses on different land use distributions. Adopting the PRISMA panchromatic (PAN) image, the proposed solution was also compared with the results of a HS-PAN pansharpening process. A two-steps operational workflow is proposed, based on two state-of-the-art and open-source algorithms. The first step consisted of the geocoding of PRISMA L2 products using Senintel-2 as reference and was accomplished with the phase-based algorithm implemented in AROSICS (Automated and Robust Open-Source Image Co-registration Software). The geometric displacement in L2 data was found to be between 80 m and 250 m, irregularly spatially distributed throughout the same scene and among scenes, and it was corrected by means of thousands of regularly spatially distributed tie points. A second-order polynomial transformation function was integrated in the algorithm. The second step consisted of employing the HySure (HS Super resolution) fusion algorithm to perform both the HS-MS fusion and the HS-PAN pansharpening, returning a PRISMA HS improved dataset with a spatial resolution of 10 m and 5 m, respectively. Four different per-band accuracy metrics were used to evaluate the accuracy of both products against airborne data. Overall, HS-MS data achieved increased accuracy in all validation metrics, i.e. + 28 % (root mean square error, RMSE), +23 % (spectral angle mapper, SAM), +7% (peak signal-to-noise ratio, PSNR) and + 11 % (universal image quality index, UIQI), with respect of HS-PAN data. These outcomes showed that using the spectral information of Sentinel-2 both spectral and spatial patterns were reconstructed more consistently in three different urban and rural scenarios, avoiding the presence of blur and at-edge artefacts as opposed to HS-PAN pansharpening, therefore suggesting an optimal strategy for satellite HS data resolution enhancement. [ABSTRACT FROM AUTHOR]

The complex interplay between hydrogen peroxide (H 2 O 2) and nitric oxide (NO) in endothelial cells presents challenges due to technical limitations in simultaneous measurement, hindering the elucidation of their direct relationship. Previous studies have yielded conflicting findings regarding the impact of H 2 O 2 on NO production. To address this problem, we employed genetically encoded biosensors, HyPer7 for H 2 O 2 and geNOps for NO, allowing simultaneous imaging in single endothelial cells. Optimization strategies were implemented to enhance biosensor performance, including camera binning, temperature regulation, and environmental adjustments to mimic physiological normoxia. Our results demonstrate that under ambient oxygen conditions, H 2 O 2 exhibited no significant influence on NO production. Subsequent exploration under physiological normoxia (5 kPa O 2) revealed distinct oxidative stress levels characterized by reduced basal HyPer7 signals, enhanced H 2 O 2 scavenging kinetics, and altered responses to pharmacological treatment. Investigation of the relationship between H 2 O 2 and NO under varying oxygen conditions revealed a lack of NO response to H 2 O 2 under hyperoxia (18 kPa O 2) but a modest NO response under physiological normoxia (5 kPa O 2). Importantly, the NO response was attenuated by l -NAME, suggesting activation of eNOS by endogenous H 2 O 2 generation upon auranofin treatment. Our study highlights the intricate interplay between H 2 O 2 and NO within the endothelial EA.hy926 cell line, emphasizing the necessity for additional research within physiological contexts due to differential response observed under physiological normoxia (5 kPa O 2). This further investigation is essential for a comprehensive understanding of the H 2 O 2 and NO signaling considering the physiological effects of ambient O 2 levels involved. [Display omitted] • Co-expressing HyPer7 and O-geNOps enable multichromatic imaging of H 2 O 2 and NO in endothelial cells. • Exogenous H 2 O 2 does not affect NO levels under hyperoxic and normoxic conditions. • Endogenous H 2 O 2 generation has minimal NO impact under normoxic conditions. [ABSTRACT FROM AUTHOR]

The new generation of remote sensing satellites improves the coverage of remote sensing imaging by improving the orbital surface and using the rotary scanning imaging technology. In this paper, a new technical scheme of rotating payload platform based on disturbance-free payload technology is proposed to adapt to the rotary scanning imaging satellite. The platform adopts the cubic Stewart configuration and uses the Lorentz force magnetic actuator as the actuator to form the satellite support module and the payload into a noncontact two-stage three-body. First, this paper uses Newton-Euler method to establish a six-degree-of-freedom dynamic model for the new rotating payload platform, and establishes a kinematic model by quaternion method. Then, according to the established kinematics and dynamics model, the control scheme of absolute pointing control of payload platform (upper platform) and relative follow-up control of kinetic energy container (middle platform) is designed. Furthermore, the six-degree-of-freedom decoupling control method of the actuator is designed. Based on this, the absolute pointing controller of the payload module and the relative position and attitude calculate controller of the kinetic energy container are designed. Finally, the research of simulation is carried out. The simulation results show that the new rotating payload platform has higher rotation pointing accuracy and stability than the traditional ultra-quiet platform, and the vibration isolation acting on the support module and the vibration absorption acting on the payload are more effective. [ABSTRACT FROM AUTHOR]

The Indian National Satellite-3DR (INSAT-3DR) provides half-hourly multispectral images over the Indian region from a geostationary position. It observes diurnal variations highly relevant for variety of application areas, including weather forecasting. This work assesses the performance of the cloud mask product of INSAT-3DR new algorithm developed by Indian Space Research Organization (ISRO) by comparison with Moderate Resolution Imaging Spectrometer radiometer (MODIS) cloud mask data for the months from July 2019 to July 2020. The cloud detection algorithm employs nine different tests, considering solar illumination, satellite angle and surface type conditions to get pixel-resolution cloud mask. Our validation results indicate that the probability of detection (POD) of cloudy (clear) sky is 86 % (82 %) with 83 % hit rate for INSAT-3DR cloud mask, using MODIS cloud mask as a reference. When the INSAT-3DR cloud mask algorithm is also implemented on similar channels of MODIS, the POD of cloudy (clear) sky is 76 % (74 %) with 85 % hit rate. Our findings indicate the reliability of the INSAT-3DR cloud mask retrieved product for research and other applications. This is one of the important parameter of satellite derived products, which plays major role in daily operational weather forecasting. [ABSTRACT FROM AUTHOR]

• Colorimetric color reproduction of different multispectral imaging systems applied to cultural heritage studies were examined. • Two methods were devised named Color Transformation Method and Illuminant Method. • The Color Transformation Method is more meticulous and leads to a better result as opposed to the Illuminant Method. • The Illuminant Method is much simpler and still leads to acceptable results. • The two methods have been shown to be applicable to both cameras showing the applicability of the methods developed here to different multispectral imaging systems. Multispectral imaging has long been used in the study of cultural heritage artifacts, particularly manuscripts with low-contrast text. The goal of multispectral imaging systems has largely been to enhance the text by imaging outside the spectral response of the human visual system and by applying various image processing techniques. However, there is a desire to use multispectral images for high quality, true color rendering of the artifacts as well. We use two multispectral imaging systems in this work. One of them is the Multispectral Imaging System for the study of Historical Artifacts (MISHA) which has been demonstrated to have significant utility at a lower cost and better ease of use compared to commercially available systems. This system was specifically designed and built for the purpose of imaging artifacts, particularly manuscripts and works on paper with illegible text due to deterioration and other environmental causes. Given this goal of "discovering" low-contrast text (and other features) on planar objects, the system has not been optimized for color reproduction purposes. The second system is a commercially available MegaVision EVTM Spectral Imaging System. This system is also used for cultural heritage imaging. In this work, we develop an approach to colorimetrically characterize these cameras and illumination systems, adding an option for accurate color reproduction to their capabilities. To do this, we present two different approaches. The first approach we call the Color Transformation Method. Here we use a Macbeth ColorChecker to first linearize the camera photometric response, and then find a linear matrix transformation from the camera response to CIEXYZ tristimulus values. The second approach we call the Illuminant Method. Here we consider each output by the camera as a reflectance factor value at the known wavelength of a narrowband illuminator. We then convert these values to the color of the object in terms of CIEXYZ tristimulus values. We show that the Color Transformation Method using a Macbeth ColorChecker leads to a more accurate result. Meanwhile, the Illuminant Method is much simpler and produces acceptable results, particularly considering accurate color reproduction was not a goal in designing the MISHA camera system. The same methods are applied to both imaging systems to show the robustness and applicability of the approaches developed herein to other imaging systems. Results are shown for both quantitative calibration targets as well as artifacts of interest to the field of cultural heritage studies. It is worth noting that we are not comparing the two imaging systems overall or the color processing software available from MegaVision. Rather, we simply show that the two approaches developed here are applicable to different multispectral imaging systems, from a very cost-effective system, i.e., MISHA to a more expensive one, i.e., MegaVision. [ABSTRACT FROM AUTHOR]

• In-depth scientific study of a rare painted shroud from a female Egyptian mummy. • Mummy dates to the 1st – 2nd century C.E. and was likely traced back to Upper Egypt. • Color palette consists of a few mineral pigments and plant-derived dyes. • Organic materials include animal fat, plant lipid, Pinaceae resin, gum, and beeswax. • Shroud and inner bandages were made of linen, now severely degraded. This article describes a multi-analytical technical study of a rare painted shroud still wrapped around a female Egyptian mummy (MCABo EG 1974) in the collection of the Museo Civico Archeologico of Bologna, Italy. Long stored in the museum's warehouses, these mummified human remains were recently rediscovered within the Bologna Mummy Project (BOmp), an interdisciplinary endeavor promoted by the Museo Civico Archeologico and the Institute for Mummy Studies of Eurac Research, Bolzano, Italy. Dating to the Roman period (1st-2nd century C.E.), as confirmed by radiocarbon dating, this mummy displays a unique series of technical features, including the exceptional finding of a colorful painted shroud still preserved in its original location around the wrapped body, to which it is secured with textile straps and resin. In this context, scientific analysis aimed to deepen our current knowledge of the artistic practices of Roman Egypt through an in-depth study of the painting technique, context of production, and possible provenance of the mummy, while promoting a science-informed, enhanced approach to the preservation of the mummified human remains. This research relied on an integrated analytical protocol based on the combined use of imaging and mapping techniques, non-invasive point analysis, and micro-invasive investigation of minute samples, each carried out at the different partnering institutions. Computed tomography (CT) highlighted varying radio-densities for some of the flesh tones and red decorations. Visible photography and multiband imaging provided information on the nature and distribution of various materials on the surface. Fiber optics reflectance spectroscopy (FORS), X-ray fluorescence (XRF) spectroscopy, and Raman spectroscopy contributed to the characterization of the shroud's color palette along with optical microscopy (OM), scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM/EDS), as well as high-performance liquid chromatography coupled with diode-array detection and mass spectrometry (HPLC/DAD and HPLC/MS). Materials detected include a limited number of mineral pigments and plant-derived dyes such as red lead, red ocher, madder, an unknown yellow dye, Egyptian blue and green, and a carbon-based black. Through access to MOLAB equipment and expertise, macro-XRF (MA-XRF), as well as combined X-ray diffraction (XRD) spot analysis and mapping, enhanced pigment identification. A combination of transmission Fourier-transform infrared (FTIR) spectroscopy with gas chromatography / mass spectrometry (GC/MS) was used to identify the paint binders and any additional organic substances possibly involved in embalming practices and ritual traditions, including animal fat, plant lipid, Pinaceae resin, gum, and beeswax. Mineralogical data gathered by XRD on surface deposits and soil residues collected from within the shroud's inner folds was crucial to put forward hypotheses, in support of the stylistic study of the shroud, concerning a possible provenance from Upper Egypt, most likely West Thebes. Both the shroud and inner bandages were found to be made with linen using OM. After undergoing scientific analysis and conservation treatment, the mummy was displayed in the "Mummies. Unwrapping the past" exhibition and was featured at the 10th World Congress on Mummy Studies, both held in Bolzano in the fall of 2022. [ABSTRACT FROM AUTHOR]

Multi-spectral satellite images of the Earth's surface are used in various applications, from water quality assessment and urban planning to climate monitoring, disaster response, infrastructure oversight, and agricultural surveillance. Many of these applications would benefit from higher spatial and temporal resolution of observations, which could be achieved by combining observations from several sources. This study introduces a deep learning-based pipeline to harmonize the spectral and spatial discrepancies between the Landsat-8 and Sentinel-2 Earth Observation satellites. Through established image quality metrics, we demonstrate a significant enhancement in the spatial resolution of Landsat-8 images. Field observation experiments show that leveraging unified images from both satellites increases the availability of cloud-free images by 21% annually on average in our study area. Additionally, our pipeline enhances the Normalized Difference Vegetation Index (NDVI) correlation between Landsat-8 and Sentinel-2 observations by about 4.9%, offering significant performance gains in a downstream crop segmentation task. Our 100M parameter model, trained on European data, generalizes to most regions with only minor limitations. Furthermore, we show that the pipeline can provide uncertainty estimates for its outputs, which are valuable for decision-making in downstream applications. Our code base is available online: https://github.com/venkatesh-thiru/Deep-Harmonization. [ABSTRACT FROM AUTHOR]

Airborne hyperspectral images can be used to map the land cover in large urban areas, thanks to their very high spatial and spectral resolutions on a wide spectral domain. While the spectral dimension of hyperspectral images is highly informative of the chemical composition of the land surface, the use of state-of-the-art machine learning algorithms to map the land cover has been dramatically limited by the availability of training data. To cope with the scarcity of annotations, semi-supervised and self-supervised techniques have lately raised a lot of interest in the community. Yet, the publicly available hyperspectral data sets commonly used to benchmark machine learning models are not totally suited to evaluate their generalization performances due to one or several of the following properties: a limited geographical coverage (which does not reflect the spectral diversity in metropolitan areas), a small number of land cover classes and a lack of appropriate standard train/test splits for semi-supervised and self-supervised learning. Therefore, we release in this paper the Toulouse Hyperspectral Data Set that stands out from other data sets in the above-mentioned respects in order to meet key issues in spectral representation learning and classification over large-scale hyperspectral images with very few labeled pixels. Besides, we discuss and experiment self-supervised techniques for spectral representation learning, including the Masked Autoencoder (He et al., 2022), and establish a baseline for pixel-wise classification achieving 85% overall accuracy and 77% F1 score. The Toulouse Hyperspectral Data Set and our code are publicly available at www.toulouse-hyperspectral-data-set.com and www.github.com/Romain3Ch216/tlse-experiments/ , respectively. [ABSTRACT FROM AUTHOR]

This work introduces a new multispectral database and framework to train and evaluate eyeblink detection in RGB and Near-Infrared (NIR). Our contributed dataset (mEBAL2, multimodal EyeBlink and Attention Level estimation, Version 2) is the largest existing eyeblink database, representing a great opportunity to improve data-driven multispectral approaches for blink detection and related applications (e.g., attention level estimation). mEBAL2 includes 21,100 image sequences from 180 different students (more than 2 million labeled images in total) while conducting a number of e-learning tasks of varying difficulty or taking a real course on HTML initiation through the edX MOOC platform. mEBAL2 uses multiple sensors, including two Near-Infrared (NIR) and one RGB camera to capture facial gestures during the execution of the tasks, as well as an Electroencephalogram (EEG) band to get the cognitive activity of the user and blinking events. Furthermore, this work proposes 3 data-driven approaches as benchmarks for blink detection on mEBAL2, where the architecture based on Convolutional Long Short-Term Memory (ConvLSTM) achieved performances of up to 99%. The experiments explored whether combining RGB and NIR spectrum data improves blink detection in training and architectures that merge both types of data. Experiments showed that the NIR spectrum enhances results, even when only RGB images are available during inference. Finally, the generalization capacity of the proposed eyeblink detectors, along with state-of-the-art eyeblink detection implementations, is validated in wilder and more challenging environments like the HUST-LEBW dataset to show the usefulness of mEBAL2 to train a new generation of data-driven approaches for eyeblink detection. • We are presenting mEBAL2, the largest existing eyeblink database public for research. • Eyeblink detection enhanced through the combination of NIR and RGB images. • Competitive eyeblink detection, up to 99% accuracy in e-learning. • Validation in challenging wild environments, achieving state-of-the-art results. [ABSTRACT FROM AUTHOR]

• Successful restoration of two shell paintings from the Warring states period. • Identifying the mineral type of the pigment and obtaining its spectral information. • In-depth explanation of the picture details and discussion of its significance in early art history. The use of shells as a medium for painting is an exceptionally rare discovery in archaeology, representing a art form that has been largely overlooked. In this study, we present two shell paintings of the Warring States period (476–221 BCE) discovered in Zhongshan State, Hebei, China. Employing various techniques, including microscopic observation, computed tomography (CT), X-ray fluorescence (XRF), multispectral imaging, and Raman spectroscopy, we non-destructively unveil the exquisite images obscured by soil and reveal the unparalleled shallow relief technique employed in their creation. The identified pigments for the different paintings include cinnabar, charcoal black, and malachite. These elaborate depictions offer insights into the costume, rituals, life, hierarchy, and art of the Warring States period. The meticulous use of prominent lines emphasizes color patches rather than the brushstrokes relied upon in later stereotypical Chinese painting, significantly reshaping previous notions about the early development of Chinese painting. This unique expression undoubtedly refreshes our understanding of early ancient art. [ABSTRACT FROM AUTHOR]

• Multispectral imaging for study of the Sarajevo Haggadah. • Non-invasive scientific analysis of medieval illuminated Hebrew manuscripts. • Application of image processing techniques to recover erased text. Over the last two decades, multispectral imaging (MSI) has established itself as the most important tool in recovering illegible text in erased or damaged manuscripts, and to a lesser extent as an aid to the codicological study of manuscript supports and media. Rarely, however, have spectral imaging initiatives involved Hebrew illuminated manuscripts. In 2022, an international team of scholars, imaging scientists, and conservators from the National Museum of Bosnia and Herzegovina and the Lazarus Project of the University of Rochester imaged the Sarajevo Haggadah multispectrally. Produced for a Jewish commissioner and housed at the National Museum of Bosnia and Herzegovina, this 14th-century illuminated manuscript contains, inter alia , a partially erased text of a sale contract on folio 106v* that is now newly legible. Codicological details of the manuscript – ink, stains, type of animal used for parchment – have also benefitted from material analysis via MSI. Herein, we describe the MSI process of the Sarajevo Haggadah and the application of image processing algorithms to recover the erased text on folio 106v*. A complete text of a sale contract is revealed for the first time, providing significant information about the ownership and provenance of the Sarajevo Haggadah at the beginning of the 16th century. [ABSTRACT FROM AUTHOR]

The tradeoff among spatial, temporal, and spectral resolution of remote sensing (RS) images due to sensor properties limits the development of RS applications. Most image enhancement studies tend to focus on either spatio-temporal fusion or spatio-spectral fusion. As a more comprehensive solution, spatial–temporal-spectral fusion (STSF) is complicated but its potential is worth to be further explored. In this study, we propose a novel STSF method from the perspective of multi-temporal Pansharpening. Canny edge extraction is applied to Panchromatic (PAN) images to identify edges while avoiding the disruption of multi-temporal land cover changes. We then build a TemPanSharpening net (TPSnet) which only uses one high-spatio-low-spectra-temporal PAN and one low-spatio-high-spectra-temporal multispectral image as input. TPSnet follows a super-resolution structure and embeds two basic modules: residual-in-residual dense blocks (RRDB) and convolutional block attention module (CBAM). A series of interior ablation experiments were conducted on TPSnet and we also compared it with some representative spatio-temporal fusion, Pansharpening, and STSF algorithms. TPSnet presented satisfactory performance on complicated meter-level ground surfaces according to the quantitative evaluation result, and it demonstrated excellent robustness to land cover change. [ABSTRACT FROM AUTHOR]

The empirical approach of satellite-derived bathymetry provides a straightforward, easily-implemented, and very effective way of estimating shallow-water depths from high-spatial-resolution satellite images. However, this approach has a great challenge that it requires high-quality in-situ depth data, which often do not exist or are not available due to financial and/or technical reasons, as the depth calibration data. To address this problem, this article assesses the feasibility of replacing high-quality in-situ depth data with four kinds of possible sourced depth data in satellite-derived empirical bathymetry. These four kinds of depth data are satellite altimeter depth data from laser altimeter systems on some earth observing satellites; satellite photogrammetric depth data, which are derived from satellite stereo-images with the photogrammetric method; nautical chart depth data, which come from existing electronic or paper charts; and radiative transfer model-based depth data, which are simulated in accordance with a bio-optical radiative transfer model (RTM). Correspondingly, four kinds of empirical bathymetry's model calibration data are referred to as altimeter data, photogrammetric data, chart data, and RTM data, respectively. The objective of this article is to assess the similarities, differences, limitations, and applicability of these four kinds of possible model calibration data, in order to know which of them is most suitable for a certain empirical bathymetry task. WorldView-2 and Sentinel-2A multi-spectral images covering four typical shallow water areas in Hawaii and Xisha Islands and Stumpf's logarithmic band ratio model were used to compare and contrast the bathymetric performance of these four kinds of possible model calibration data. The result showed that all four kinds of possible model calibration data can be used for satellite-derived empirical bathymetry when the high-quality in-situ depth data are not provided, but they will provide different bathymetric accuracy values. According to five evaluation indexes (i.e., bathymetric performance, data availability, data quality, complexity of data generation, and data adaptability), the altimeter data perform best, the chart data perform second best, the RTM data follow with the third performance, and the photogrammetric data perform markedly worse than other three kinds of model calibration data. Such factors as the in-situ property, spatial coverage, co-registration and independence of model calibration data must be taken into consideration as well during a model calibration data selection. [ABSTRACT FROM AUTHOR]

Despite significant progress has been made towards crop yield prediction with remote sensing, there exist knowledge gaps on (1) the impacts of temporal resolution of imaging frequencies on yield prediction, (2) transferability of the models among different genotypes and test sites, and (3) translation of these research developments to crop breeding that benefit farmers. Existing research predominantly provides an on-site perspective, frequently missing the complexities of real-world applications. The objectives of this paper are to investigate the transferability and generalization capabilities of yield prediction models for crop breeding across test sites located in North and South Americas. Toward that goal, we tested different machine learning techniques including Random Forest Regressor (RF), Gradient Boosting Regression (GB), and Deep Neural Networks (DNN) for soybean yield prediction with experiments conducted in different climate and growth conditions. A novel transfer learning approach was proposed for genotype selection and categorizing soybean yield for screening high-yield varieties. Furthermore, we studied the effect of temporal resolution on yield prediction, focusing on the critical development stages and optimal aerial survey frequencies for precise yield prediction using large 31,404 sample data. Results demonstrated that the combined dataset of Argentina and United States representing different climate regimes provided the highest performance with an R2 of 0.76 using RF and GB algorithms. The classification approach was proven to be most useful for crop breeding as demonstrated by accurately identifying the high-yielding genotypes. Increasing temporal sampling of key phenological stages significantly improved yield prediction. Although transfer learning yielded promising outcomes across trials within Argentina the efficacy of transferring models from Argentina to the United States was limited, attributed to significant seasonal and climate variations. This study pioneered the use of transfer learning for model adaptability in real-world breeding scenarios, training and transferring models within the South and North Americas, providing actionable insights and strategies for the breeding community, aiming to facilitate improved decision-making for agricultural productivity. [ABSTRACT FROM AUTHOR]

The recent launch of Landsat-9 satellite enriches the opportunities to work with dense time series of multispectral medium-resolution images. The integration of Landsat-9 in a multi-constellation series with Landsat-8 and Sentinel-2 requires a harmonization of the surface reflectance values that can be obtained from the official Level-2 products. This paper proposes the coefficients of the optimal linear transformations for the European continent, which allow to integrate Landsat-9 with the similar operating missions. These coefficients are based on a regression over 30 independent random extractions of 240,000 samples from images of the same areas but acquired by different sensors within two days. The coefficients were validated on an independent dataset. Furthermore, the effects of the proposed harmonization were tested on four popular vegetation indices, by evaluating the distributions of the differences in values obtained from each sensor pair. Finally, a test on a local scale was carried out with a spectroradiometer survey on 16 locations to collect some reference spectra to be compared with the reflectance values provided by the images. The results demonstrate the interoperability of Landsat and Sentinel-2 missions, since reflectance differences are in most cases within the accuracy specifications of the sensors. However, some discrepancies are observed in the blue and SWIR bands, probably due to inconsistencies in the atmospheric correction processes. [ABSTRACT FROM AUTHOR]

• Comprehensive study of an alleged forgery of a tempera painting on tile with apparently Botticelli features. • Pigments identification – i.e., iron oxide pigments, cinnabar, carbon black, and calcite) - by combined FORS and Raman spectroscopy. • Pigments mapping by SCM algorithm application to the multispectral image cube. • Colorimetric analysis to quantitatively evaluate the cleaning method and provide support for an informed restoration process. In this work, a tempera painting of uncertain attribution from the Uffizi Galleries was studied. The painting, which portraits a man with apparently Botticellian features, was probably created by one of the most skilled forgers of the early 20th century, Umberto Giunti. He used a flat tile as a support, covered with preparatory layers emulating a wall painting. To shed light on the authorship of the painting, it was crucial to investigate its composing materials, as well as the execution technique and the state of conservation. To this end, non-invasive spectroscopic and imaging optical techniques were applied. Specifically, VIS-NIR multispectral reflectography was used to reveal the presence of possible preparatory drawings, pentimenti and overpaintings. Raman and Fibre Optics Reflectance Spectroscopies allowed identifying the pigments used by the artist. Finally, colour variations due to the cleaning procedure were documented in order to provide a support for an informed restoration process. [ABSTRACT FROM AUTHOR]

• Optical scanner microprofilometry for surface analysis of book heritage. • Integration of microprofilometry and multispectral imaging for materials-surface analysis of manuscripts. • Spatial registration of microsurface and multispectral images combining height-intensity datasets. • Multispectral image-guided microsurface analysis for segmentation and investigation of degraded book heritage. A novel workflow is proposed to combine the use of two powerful techniques in the study of ancient manuscripts: multispectral imaging and optical microprofilometry. Multispectral imaging is routinely used and allows to examine each individual folium as a superposition of layers that give different responses in the UV-Vis-NIR bands. It enables the analysis of the conservation state of an object, the mapping of previous restorations or the detection of writings no longer visible. The downside of this technique is the lack of quantitative data on surface morphology. On the other hand, surface microprofilometry on book heritage is unexplored. The optical scanning microprofilometer used in this work employs single-point, interferometric depth-sensors that enable to measure the surface topography of the manuscript (deformation and roughness) in full-field (areas of tens of centimeters) at micrometer scale. The crucial task of spatial referencing the surface topography at micrometer scale to the visible features (e.g., written text) is performed with a novel procedure that solves the problem of the lack of reference points in the microprofilometer height data. We exploit the raw intensity signal collected by the laser depth sensor to fuse the interferometric measurements with the multispectral image stack. The full-field integration of quantitative microsurface measurements and in-band imaging responses enables a more comprehensive exploration of ancient manuscripts, by integrating materials-surface analysis, advancing the diagnostic protocol. [ABSTRACT FROM AUTHOR]

Regional carbon sink estimation and local forest management require spatially explicit maps of bamboo distribution. However, accurate bamboo mapping is challenging due to the similarity of bamboo's optical spectral with those of other vegetation. To gain a high-precision bamboo forest distribution map circa year 2020, we developed a novel classification framework that integrated in-situ measurements, GEDI, and time-series multispectral Sentinel-2 images in a whale-optimized dual-channel Dense Convolutional Network (DenseNet) classification algorithm. The new method shows that vegetation structure features from GEDI can improve the performance of bamboo mapping by more than 5 percent compared with results obtained using only spectral information. Additionally, ablation studies show our proposed dual-channel DenseNet with whale-optimized DropBlock regularization outperforms traditional deep learning algorithms. The proposed method demonstrates a high level of accuracy in identifying bamboo, with precision, recall (producer's and user's accuracies), and F1-score reaching 90.81%, 91.86%, and 91.33%, respectively, which can provide more detailed and accurate results than previous studies, especially in regions where the terrain is complex or bamboo is intermixed with other vegetation. [ABSTRACT FROM AUTHOR]

Object detection is one of the essential tasks in a variety of real-world applications such as autonomous driving and robotics. In a real-world scenario, unfortunately, there are numerous challenges such as illumination changes, adverse weather conditions, and geographical changes, to name a few. To tackle the problem, we propose a novel multi-modal object detection model that is built upon a hierarchical transformer and cross-guidance between different modalities. The proposed hierarchical transformer consists of domain-specific feature extraction networks where intermediate features are connected by the proposed Cross-Guided Attention Module (CGAM) to enrich their representational power. Specifically, in the CGAM, one domain is regarded as a guide and the other is assigned to a base. After that, the cross-modal attention from the guide to the base is applied to the base feature. The CGAM works bidirectionally in parallel by exchanging roles between modalities to refine multi-modal features simultaneously. Experimental results on FLIR-aligned, LLVIP, and KAIST multispectral pedestrian datasets demonstrate that the proposed method is superior to previous multi-modal detection algorithms quantitatively and qualitatively. [Display omitted] • Cross-guided attention mechanism by complementary interactions between modalities. • Multi-scale attention maps generated by the proposed hierarchical transformer. • State-of-the-art performance in FLIR-aligned, LLVIP, and KAIST multispectral datasets. [ABSTRACT FROM AUTHOR]

Hyperspectral image (HSI) classification is an active research problem in computer vision and multimedia field. Contrary to traditional image data, HSIs contain rich spectral, spatial and semantic information. Thus, how to extract the discriminative features for HSIs by integrating spectral , spatial and semantic cues together is the core issue to address HSI classification task. Existing works mainly focus on exploring spectral and spatial information which usually fail to fully explore the rich semantic information in HSIs. To address this issue, in this paper, we first propose a novel semantic Transformer scheme, named SemanticFormer, which aims to learn discriminative visual representations for semantics by exploiting the interaction among different semantic tokens. Using the proposed SemanticFormer, we then propose a novel heterogeneous network that contains both spectral–spatial convolution network branch and SemanticFormer branch to extract spectral–spatial and semantic features simultaneously for HSIs. Experiments on two widely used datasets demonstrate the effectiveness of our SemanticFormer and HSI classification network method. Our codes will be available in https://github.com/SissiW/SemanticFormer. • We propose a novel SemanticFormer module for semantic representation. • We integrate SemanticFormer into HSI classification framework. • SemanticFormer is a general scheme in CV. • Extensive experimental results prove the effectiveness of SemanticFormer. [ABSTRACT FROM AUTHOR]

The license plate recognition system currently in use is susceptible to interference from the external environment and performs poorly in low-light conditions. This paper presents a solution for license plate recognition under a low-light environment. We adopted URetinex-Net methods that unfold an optimization issue into a learnable network to decompose a low illumination image into reflectance and illumination layers. We also adopted TRBA, an end-to-end recognition method involving no character segmentation. The experimental results show that the accuracy of the night environment of the proposed method is 80.11% increased by 5.11% compared to without the low light image enhancement method. [ABSTRACT FROM AUTHOR]

Exposing a healthy wound bed for skin grafting is an important step during burn surgery to ensure graft take and maintain good functional outcomes. Currently, the removal of non-viable tissue in the burn wound bed during excision is determined by expert clinician judgment. Using a porcine model of tangential burn excision, we investigated the effectiveness of an intraoperative multispectral imaging device combined with artificial intelligence to aid clinician judgment for the excision of non-viable tissue. Multispectral imaging data was obtained from serial tangential excisions of thermal burn injuries and used to train a deep learning algorithm to identify the presence and location of non-viable tissue in the wound bed. Following algorithm development, we studied the ability of two surgeons to estimate wound bed viability, both unaided and aided by the imaging device. The deep learning algorithm was 87% accurate in identifying the viability of a burn wound bed. When paired with the surgeons, this device significantly improved their abilities to determine the viability of the wound bed by 25% (p = 0.03). Each time a surgeon changed their decision after seeing the AI model output, it was always a change from an incorrect decision to excise more tissue to a correct decision to stop excision. This study provides insight into the feasibility of image-guided burn excision, its effect on surgeon decision making, and suggests further investigation of a real-time imaging system for burn surgery could reduce over-excision of burn wounds. • Study demonstrates usefulness of an imaging device in providing surgical guidance to aid clinicians in burn excision. • Multispectral imaging and artificial intelligence correctly identified the viable and non-viable wound bed. • Multispectral imaging and artificial intelligence helped surgeons stop at the correct depth during burn excision surgery. • Multispectral imaging and artificial intelligence could eventually provide reliable guidance during burn surgery. [ABSTRACT FROM AUTHOR]

Recently, ensemble multiple deep learning (DL) classifiers has been reported to be an effective method for improving remote sensing classification accuracy. Although these approaches still follow the conventional pattern of inputting instance features and outputting corresponding classes, they often overlook the intrinsic relationships between pixels beyond their spatial features. As a result, the diversity in the ensemble classification results primarily relies on different DL models. However, training the DL models consumes a significant amount of time, and training multiple networks not only incurs additional time costs but also affects the overall efficiency. To address this, a new approach has been proposed in this paper, which takes advantage of the relationships between pixels and their combinations to generate diverse classification results. It's a novel ensemble classification framework, termed as the Doublet-Based Ensemble Classification Framework (DBECF), which eliminates the need for multiple classifiers. The DBECF starts by utilizing the training set to combine different samples to generate doublets. Then, features are assigned to these doublets through an exponentiation operation, resulting in a doublet training set. Using both the original training set and the derived doublet datasets, the DBECF is trained. For each input pixel, the DBECF produces multiple classification results, which are then integrated to obtain a more accurate output. To validate the proposed approach, experiments were conducted on three datasets, including multispectral images, hyperspectral images, and time series images. The maximum accuracies achieved by DBECF on the three datasets are 87.80 %, 97.71 %, and 83.51 %, respectively. In comparison to the contrastive methods, the incremental improvements in accuracy are 3.73 %, 7.66 %, and 9.16 %, respectively. The experimental results indicate that no matter using DL or non-deep learning for training, our proposed framework achieves progress on accuracy improvement outperforming classifications using comparative approach that based on single instance. This research provides a new perspective on the combination of DL and ensemble learning, highlighting its important implications and practical value in enhancing classification accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Although single-band emission upconversion fluorescence, which is particularly important in sensors, biomedical and imaging applications, has been extensively investigated in nanoparticles using energy transfer engineering, it is still difficult to be achieved in transparent ceramics. Herein, the MgAlON: Er3+/Yb3+ transparent ceramics with functionalized grain boundaries were successfully fabricated. Lanthanide ions were investigated to highly enrich at the MgAlON grain boundaries, resulting in novel fluorescent properties. Upon 980 nm laser excitation, the sample emitted temperature and excitation power-independent single-band red upconverted fluorescence with a red-to-green fluorescence intensity ratio of ∼25. The new mechanism of Yb3+-Yb3+ cooperative fluorescence-assisted energy transfer, which is responsible for the single-band feature, was elucidated. Moreover, the upconverted fluorescence chromaticity can be modified by the excitation wavelength, relying on the existence of the cooperative fluorescence level and its related energy transfer mechanism. This work reveals the potential of grain boundary functionalization of transparent ceramics in energy transfer engineering. [ABSTRACT FROM AUTHOR]

In recent years, various self-knowledge distillation approaches have been proposed to reduce the cost of training teacher networks. However, these methods often overlook the significance of deep features. To address this limitation and strengthen the capability of deep features while preserving the ability of shallow features, we propose performing Self-Knowledge Distillation via Pyramid Feature Refinement (PR-SKD). Inspired by the representation learning characteristics of deep neural networks, PR-SKD builds a cohort of sub-networks with a pyramid architecture to hierarchically transfer refined information to the target network. According to the different contributions and functions between deep and shallow feature maps, our PR-SKD fully utilizes feature information to improve deep feature representation ability without compromising the capability of shallow feature maps. Extensive experiments on various image classification datasets demonstrate the superiority of our proposed method over widely used state-of-the-art knowledge distillation methods. The code is available at: https://github.com/wo16pao/PR-SKD. • The method recognizes the variation contribution of different feature maps in self-distillation. • A new hierarchical refinement distillation technique via pyramid architecture. • The approach improves the deep feature representative capability of the target network. • The proposed technique achieves highly competitive performance on recent benchmarks by focusing on deep feature representation. [ABSTRACT FROM AUTHOR]

The two-phase Airborne Visible InfraRed Imaging Spectrometer – Next Generation (AVIRIS-NG) campaign over India has generated datasets of numerous natural and artificial surroundings with high signal-to-noise ratios and higher spatial and spectral consistencies. However, the first phase AVIRIS-NG datasets of urban areas have 8.1 m spatial resolution, which is insufficient for producing level-4 urban material and natural cover maps required to model Earth's processes at the micro-scale or constantly monitor urban processes. The current work addresses this drawback by demonstrating a part of the proposed proof-of-concept approach on the AVIRIS-NG dataset of Ahmedabad, India, wherein super-resolution (SR) is proposed to enhance the hyperspectral image's spatial resolution and preserve its rich spectral content at the targeted spatial scale before utilizing it for producing detailed urban land cover maps. A step-wise account of the SR process has been provided herein to aid the reader in reproducing these experiments. Super-resolved products generated using four SR algorithms have been compared and validated using a composite framework based on processing time, qualitative visual investigation of selected image patches and spectral profiles, re-interpretation of Wald's reduced resolution protocol and full-reference image quality metrics. Results suggest the AVIRIS-NG dataset's successful spectral and spatial fidelity at a larger spatial scale, albeit varying in each super-resolved output. Sparse regression and natural prior (SRP), and anchored neighbourhood regression (ANR) produce the best and poorest super-resolved outputs among the tested SR algorithms. Although gradient profile prior (GPP) generated super-resolved product is the second best, it is also the most computationally expensive algorithm. An additional yardstick in the form of a spectral similarity analysis of randomly selected unknown spectral signatures on super-resolved output against a reference spectral library has been proposed to ascertain spectral content preservation further before utilizing the super-resolved product for urban information extraction. This exercise's findings reveal the presence of the target urban land cover feature at the unknown pixels with matching scores of greater than 0.5 in both best and worst super-resolved products, thereby advocating the extension of the proposed proof-of-concept to recently launched spaceborne hyperspectral missions' datasets. [ABSTRACT FROM AUTHOR]

The present study evaluates the potential of airborne hyperspectral and space-borne multispectral satellite datasets to determine alpha (α) and beta (β) diversity combining in-situ floristic composition in tropical deciduous forests (TDFs) of Mudumalai Tiger Reserve (MTR), India. 43 forest plots (each of 0.04 ha) were sampled to record the species diversity in MTR. The very high-resolution AVIRIS-NG hyperspectral images and temporal Sentinel-2 multispectral images were used to extract spectral variables by associating each field plot with corresponding pixels of the satellite images. A high positive correlation of field-based α-diversity (Shannon-Wiener diversity index (H′)) with AVIRIS-NG based α-diversity (R2: 0.83 (2016); R2: 0.82 (2018)) was observed compared to Sentinel-2 based α-diversity (R2: 0.50 to 0.63) with high correlation during post-monsoon (R2: 0.63). Principal coordinate analysis of AVIRIS-NG based β-diversity signifies a low diversity (H′: <1.00) in the eastern parts (0.13% of MTR) in contrast to moderate (H′: 1.00 to 2.00; 13.39% of MTR) to high diversity (H′ >2.00; 86.49% of MTR) in the remaining parts of MTR. The study highlights the application of high spectral-spatial AVIRIS-NG images for precise tree diversity mapping and the high temporal-spatial Sentinel-2 satellite images for assessing the phenological variations of the forest species. The integration of high spatial-hyperspectral and temporal multispectral satellite datasets with field-based observations for fine-scale tree species diversity mapping at geographical and temporal scales is the novelty of the study that may effectively contribute in devising effective forest management plans for biodiversity conservation. [ABSTRACT FROM AUTHOR]

• High fulvic acid (FA) content impedes NO 2 −-N production in partial nitritation (PN). • EEM-PARAFAC and UV/V is an effective method to characterize the evolution of FA. • The PN process has a high utilization of small molecules with 1–5 kDa. • The removal mechanism of FA was varied under different influent FA concentrations. • Changes of Comamonas, OLB12 and Thauera might be critical for FA removal. This study employed multispectral techniques to evaluate fulvic acid (FA) compositional characteristic and elucidate its biodegradation mechanisms during partial nitritation (PN) process. Results showed that FA removal efficiency (FRE) decreased from 90.22 to 23.11% when FA concentrations in the reactor were increased from 0 to 162.30 mg/L, and that molecular size, degree of aromatization and humification of the effluent FA macromolecules all increased after treatment. Microbial population analysis indicated that the proliferation of the Comamona s, OLB12 and Thauera exhibit high FA utilization capacity in lower concentrations (<50.59 mg/L), promoting the degradation and removal of macromolecular FA. In addition, the sustained increase in external FA may decrease the abundance of above functional microorganisms, resulting in a rapid drop in FRE. Furthermore, from the genetic perspective, the elevated FA levels restricted carbohydrate (ko00620, ko00010 and ko00020) and nitrogen (HAO, AMO, NIR and NOR) metabolism-related pathways, thereby impeding FA removal and total nitrogen loss associated with N 2 O emissions. [Display omitted] [ABSTRACT FROM AUTHOR]

Childhood demyelinative diseases classification (DDC) with brain magnetic resonance imaging (MRI) is crucial to clinical diagnosis. But few attentions have been paid to DDC in the past. How to accurately differentiate pediatric-onset neuromyelitis optica spectrum disorder (NMOSD) from acute disseminated encephalomyelitis (ADEM) based on MRI is challenging in DDC. In this paper, a novel architecture M-DDC based on joint U-Net segmentation network and deep convolutional network is developed. The U-Net segmentation can provide pixel-level structure information, that helps the lesion areas location and size estimation. The classification branch in DDC can detect the regions of interest inside MRIs, including the white matter regions where lesions appear. The performance of the proposed method is evaluated on MRIs of 201 subjects recorded from the Children's Hospital of Zhejiang University School of Medicine. The comparisons show that the proposed DDC achieves the highest accuracy of 99.19% and dice of 71.1% for ADEM and NMOSD classification and segmentation, respectively. [ABSTRACT FROM AUTHOR]

• A Suaeda Salsa spectral index (SSSI) is proposed. • SSSI can be used for both S. salsa mapping and fractional cover (FC) estimation. • S. salsa with FC as low as 20% can be accurately detected. • SSSI performs better than NDVI and SAVI for S. salsa FC estimation. • SSSI alleviates the impact of heterogeneous soil backgrounds in intertidal wetlands. Suaeda Salsa (S. salsa), with short and red-purplish plants, is a typical pioneer saltmarsh species in the intertidal wetlands of temperate East Asia. It has important ecological, economic, and recreational values. In the past few decades, S. salsa has severely degraded in coastal China, which has brought widespread attention from regional and local governments. As a result, extensive S. salsa restoration projects have been initiated in recent years. It is urgently needed to develop satellite-based methods for both S. salsa mapping and fractional cover (FC) estimation because degradation and recovery of S. salsa are manifested by changes in both area and plant cover. However, accurate mapping and FC estimation of S. salsa are challenging because (1) S. salsa in intertidal areas have low FC and (2) heterogeneous soil backgrounds in wetlands greatly impact the spectral reflectance observed by satellites. To address these issues, this study proposed a new Suaeda Salsa Spectral Index (SSSI) to support accurate detection and FC estimation of S. salsa. The SSSI was designed based on the laboratory spectral measurements by considering variations in wetland soil moisture and by taking advantage of the reddish color of S. salsa. It consists of two components, one of which utilized blue, green and red bands to separate S. salsa from green vegetation, and the other component utilized a modification of the Soil Adjusted Vegetation Index (SAVI) to reduce the impact of soil background and maintain a linear relationship with S. salsa FC. SSSI was then applied on Sentinel-2/GF-1 images over the Yellow River Delta (YRD) and Liao River Delta (LRD), China. Based on SSSI, a simple thresholding approach was used to identify S. salsa, and a linear regression model was used to estimate FC. With reference datasets provided from field investigations, Unmanned Aerial Vehicle multispectral images and high-spatial resolution satellite images, our results show that the SSSI was able to detect low-coverage S. salsa (FC > 20 % in YRD and FC > 10 % in LRD), and the S. salsa maps had an overall accuracy over 94%. The SSSI-FC models achieved good estimation accuracies (R2 = 0.77 ∼ 0.86, RMSE = 7.55 % ∼ 9.79 %). Compared to the Normalized Difference Vegetation Index (NDVI) and SAVI, SSSI alleviated the impacts from soil backgrounds and provided better S. salsa FC estimations, particularly for low-coverage S. salsa. SSSI has great potential in supporting continuous monitoring of S. salsa dynamics and evaluating the effectiveness of S. salsa restoration projects. [ABSTRACT FROM AUTHOR]

The article explores the possibilities of applying immersive technologies for the preservation of cultural heritage, using their implementation in various museums as an example. A classification of images using machine learning methods was carried out on a small number of exhibits from the A. Kasteev Museum (Almaty, Kazakhstan), achieving high accuracy. Different aspects of implementing a mobile application for the museum with an integrated exhibit recognition system were examined. Directions for further research were identified. [ABSTRACT FROM AUTHOR]

Agricultural landscapes are constantly at risk due to erratic weather phenomena. Monitoring these landscapes, especially through crop-type maps produced from remote sensing imagery, becomes imperative. These maps serve as pivotal tools in forecasting and estimating crop yields, where accuracy is crucial. In this research, we conducted a thorough investigation using a series of multispectral images taken by an unmanned aerial vehicle (UAV). The focus was on three specific crops: soybean, barley, and wheat. The primary objective was to determine the comparative efficiency of crop classification models, either trained using Gray Level Co-occurrence Matrix (GLCM) features or those leveraging the UAV multispectral images directly. The results emerging from this study were as follows: the fusion of GLCM features derived from a time series of images and the ExtraTreesClassifier emerged as the standout performer, achieving an accuracy, precision, and recall of 0.87, 0.88, and 0.87, respectively. Following closely was the combination using the same GLCM features, but with the Random Forest classifier. The outcomes of this research highlight the primacy of feature selection for accurate crop classification tasks, which often is more important than the choice of classification algorithm itself. Effective agricultural planning depends on accurate crop classification. It makes it possible for decision-makers to allocate resources wisely, modify the supply chain, and adopt adaptive farming techniques. Additionally, it provides a path for sustainable policies that consider market demands and climate conditions and subsequently contribute to the field of agricultural strategy and governance. [ABSTRACT FROM AUTHOR]

Severe cloud storms that produce hail (hailstorms) are common across Europe and pose major episodes of meteorological risk. The study presents all the methodological steps that were followed to develop a Deep machine learning Neural Network (DNN) model for hail detection (in terms of hail probability of occurrence), using the Meteosat multispectral infrared (IR) imagery, exclusively. The DNN model was trained using numerous cases of hail events as they were recorded from the European Severe Weather Database (ESWD). In each pixel with a recorded hail event, a set of parameters was calculated which were used to train the DNN model. Among them, a new remote sensing index named Hail Potential Index (HPI) was used to achieve optimal accuracy of the proposed DNN model in hail detection. The accuracy assessment of the DNN model was found satisfactory, with a Mean Absolute Error (MAE) of 1.16%. Also, the DNN model was applied in two different case studies (hail episodes) with satisfactory efficiency. The accuracy achieved allows its operational mode for hail detection using multispectral IR information from modern meteorological satellites worldwide. [ABSTRACT FROM AUTHOR]

In March 2022, a COVID-19 associated lockdown at an intravenous (IV) contrast production facility resulted in global shortages. We report our experience as a comprehensive cancer center navigating the IV contrast shortage. A triage prioritization system was developed to serve as a guideline for ordering clinicians to reduce contrast use. The triage team reviewed all requests and made final determination based on patient history, treatment plan, prior imaging, possible alternative modalities, and competing requests. Our institution performed a median of 194 CT studies per day. Contrast utilization as a percentage of all CTs ordered was approximately 80% prior to the shortage, nadired at 9% during peak shortage, and has since returned to pre-shortage levels. Over the study period, 132 requests were reviewed. Fifty studies (38%) were approved by the team for contrast administration, 56 (42%) were recommended to be performed without contrast, 15 (11%) for a change in modality, and 11 (8%) were felt suitable for delay. There was overall general concordance between the recommendations of the triage team and studies conducted without significant distributional differences (χ2 = 4.004, two-tailed p = 0.2610). The concept of resilience involves the development of system-based practices that allow for sustained operations during periods of sudden change, or loss of critical supplies. The effort to optimally allocate limited supply of contrast was an extensive effort across the organization including from senior leadership, IT, radiology, nursing, physicians, and APPs. Concepts from heuristics and behavioral science can aid the conservation of a scarce resource. Decisions made by the team appeared to be sound without any known patient harm associated with a lack of contrast. • IA COVID-19 associated lockdown resulted in shortages of (IV) contrast. • Concepts from heuristics and behavioral science can aid the conservation of a scarce resource. • As shortages of critical elements can occur at any time, it is important to have a response plan to navigate such shortages. • We found that the development of a triage team was effective in making determinations about the optimal use of IV contrast. • Decisions appeared to be sound without any known evidence of patient harm. [ABSTRACT FROM AUTHOR]

Pan-sharpening is a very productive technique to enhance the spatial details of multispectral images with the aid of panchromatic images. Nowadays, deep learning-based pan-sharpening has scored tremendous achievements. However, strict requirement for training image pairs and low generalization hamper the development of supervised pan-sharpening with limited samples. Unsupervised image fusion is an effective technique to fuse images without adequate ground truth as training samples. Existing unsupervised pan-sharpening methods are commonly based on the image generator model, suffering from unsatisfactory spatial details. In this study, we proposed a self-supervised pan-sharpening method with the aid of spectral super-resolution named sSRPNet. Coarsening-scale self-learning exploits the internal information in multispectral images at a coarsening scale and trains the initial fusion model without other labels. Spectral super-resolution injection explores the missing spatial details in the initial fused images and recovers it. Degradation self-learning introduces strong spectral constraints with original multispectral images. Both reduced-resolution and full-resolution experiments on three datasets have proved the superiority of sSRPNet. Furthermore, the proposed spectral super-resolution injection can be implemented on any existing pan-sharpening algorithms, improving their performance. [ABSTRACT FROM AUTHOR]

The major hurdle in building a visible and infrared fusion model is the necessity of large-scale pixel-aligned and time-synchronized image pairs. In this paper, we propose an easy-to-learn visible and infrared image fusion framework that does not require pairs for training. In addition to easy training using unpaired sets, our framework provides fusion images with more textures and meaningful scene information compared to previous works. In particular, to mix features from each spectrum, we newly present a feature line-up module to identify important information in each source. Additionally, in order to provide a new option for benchmark evaluation, we construct a new sequence-based visible and infrared paired dataset that is aligned and synchronized. Finally, we perform extensive experiments to verify the performance of the proposed method by both public and proposed datasets. [Display omitted] • The proposed image fusion framework can be learned with an unpaired set. • Aligning images and preparing ground truth is not needed at all. • A feature line-up method is designed to mix features from each spectrum. • Our loss function targets both rich textures and well-represented scene information. • A new sequence-based dataset for validation of need-to-fuse situations is introduced. [ABSTRACT FROM AUTHOR]

• The painting was dated 1492 (terminus post quem) and the wood came from Baltic forests connected to the port of Klaipeda as their outlet to the sea. • IR-reflectography and false-colour infrared made it possible to identify the original compositional revisions and subsequent restoration work. • Our results suggest the attribution of the painting to the Master of Frankfurt, an artist active in the city of Antwerp between 1480 and 1520. The panel painting depicting the Virgin Enthroned with Child underwent dendrochronological, multi-spectral, and stylistic/documental analyses. Dendrochronology facilitated the determination of the terminus post quem , which was 1492. Robust correlations with the Baltic1 reference chronology and its recent update, 2021BLT1, indicated a likely origin of the timber in the central-eastern Baltic area. Multi-spectral analyses, such as IR-reflectography and false-colour infrared analyses, aided in identifying preparatory drawings, compositional revisions, and later additions resulting from restoration efforts. By comparing the analysis results with the painting's recent history reconstruction, doubts regarding its possible attribution were effectively addressed. Based on the findings, it can be concluded that the painting was executed by the so-called Frankfurt Master around the year 1508. [ABSTRACT FROM AUTHOR]

The use of artificial intelligence has led to an increase in road extraction projects from satellite images through deep learning. However, multi-spectral images (MSI) have been largely overlooked in road extraction algorithms due to their lower resolution compared to panchromatic or fused images. Additionally, deep learning faces the challenge of image content reasoning from distant contexts in data rule mining. To address these issues, we propose a new method for road extraction called the MSI-guided Segmentation Network, which utilizes all the data from the GF2 satellite to achieve optimal results. This study highlights the advantages of using MSI with low-resolution for obtaining deeper semantic information in a faster manner, while high-resolution fused images are better suited for extracting precise characteristics. The proposed method includes two sections: (1) a local symmetry feature fusion to enhance the network's local context-awareness for shallow details, and (2) a global asymmetric semantic fusion to improve the network's capability to comprehend the whole scene for deep semantic information. Moreover, to evaluate the robustness and generalization of this method, we have provided a GF2 Full-band China Road Dataset. The codes and datasets will be made public on https://github.com/Dudujia160918/MSNet. [ABSTRACT FROM AUTHOR]

Multispectral imaging (MSI) is a unique layer-by-layer imaging technique that allows the visualization of a wide array of retinal and choroidal pathologies including retinovascular disorders, retinal pigment epithelial changes, and choroidal lesions. Herein, we summarize the basic imaging principles and current applications of MSI together with recent technology advances in the field. MSI detects reflectance signal from both normal chorioretinal tissue and pathological lesions. Either hyperreflectance or hyporeflectance reveals the absorption activity of pigments such as hemoglobin and melanin and the reflection from interfaces such as the posterior hyaloid. Advances in MSI technique include creation of a retinal and choroidal oxy-deoxy map that could provide a better understanding of blood oxygen saturation within lesions as well as better interpretation of reflectance phenomenon of MSI images such as the different reflectance from the Sattler and Haller layers described in this review. [ABSTRACT FROM AUTHOR]

Today, with the non-stop expansion of urbanization, mapping urban areas and monitoring their dynamic changes have become challenges for governments and also a hot topic for researchers. Remote sensing imageries play a key role in urban studies, the extraction of urban built-up areas, and monitoring their changes. A variety of studies have proposed methods for the extraction of regional, national, and global built-up areas. However, the majority of them used limited features and applied a manual sample selection strategy for classification, leading to time-consuming and low-efficient algorithms. This paper proposes a fully automatic procedure to real-time extract built-up areas by integrating the Luojia 1–01 nighttime lights (NTL) images, Sentinel-2 multispectral data, Sentinel-1 Radar images, and SRTM elevation data in cloud-computing Google Earth Engine. Firstly, potential built-up areas (PBA) and non-built-up areas (NBA) are obtained by applying Otsu and multi-level thresholding to some of the extracted spectral-textural-spatial (STS) features and by applying logical rules. Secondly, built-up and non-built-up samples are automatically selected and are used to train a Support Vector Machine (SVM) supervised classifier and to classify the hybrid feature set so that a preliminary classified map (PCM) can be obtained. Thirdly, the PCMs are automatically corrected using the non-built-up area, and morphological operations in the so-called post-classification to provide a refined classified map (RCM) and final built-up map. Four study areas in Northern America, Europe (Scandinavia), the Middle East, and Eastern Asia were selected to test the proposed method. Also, five state-of-the-art built-up products, accompanied by Google Earth images, were used as the reference data. The results indicate that the proposed method can accurately and automatically select samples and map built-up areas with a spatial resolution of 10 m. Its performance is validated with an average overall accuracy of 94.4% and an average Kappa coefficient of 0.89 and by visual comparison of our method results with other reference data. The proposed method has significant potential to be used in real-time extracting built-up areas and in monitoring their dynamic changes on national and global scales. [ABSTRACT FROM AUTHOR]

Nearshore sediment distribution is a significant component of fundamental geographic data, but the inherent optical properties of the water column make the large-scale observation of sediment particularly difficult. The development of satellite-based imaging sensors has enabled earth observation to capture more types and finer granularities of ground information, making it possible to obtain the target sediment distribution on a large scale. We use the proposed wavelet clustering algorithm to separate noise points and signal photons from ICESat-2 and then combine multispectral images and a quasi-analytical algorithm to calculate substrate reflectance to reduce the influence of the inherent properties of the water body. Furthermore, a new deep learning model, TSDANet, is being built to combine substrate reflectance and multispectral image data from SuperDove for nearshore sediment identification. We use field data to validate the method through a large number of experiments. The experiments demonstrate that the wavelet clustering algorithm has a coefficient of determination (R 2) of 0.90 and 0.95 for signal point extraction over a large scale of high and low photon densities compared with a digital elevation model, respectively. Compared to single-source data, the TSDANet sediment classification kappa improves by 6.44% on average and 8.28% at its maximum. The results indicate that the identification and classification of sediment using ICESat-2 and SuperDove data is a new and highly feasible method. [ABSTRACT FROM AUTHOR]

[Display omitted] Aboveground biomass (AGB) is an essential factor in rice ecological research. Optical variables (e.g. reflectivity and vegetation index (VI)) are widely adopted in monitoring AGB while spatial information (e.g. texture) is less introduced into research. Limited research is available regarding the impact on the monitoring accuracy caused by the utility of VI and texture in AGB estimation at different combinations of growth stages. To compare the improvements caused by the involvement of texture at different combinations of growth stages, a hyperspectral camera was mounted on an unmanned aerial vehicle (UAV) to obtain images of rice field during the early growth stages (including the tillering, jointing and booting stage) under five nitrogen levels over two years. Spectral and spatial information derived from images was utilized to compute VIs and textures at different combinations of growth stages. After analyzing their correlations with AGB, multiple stepwise regression (MSR) and multiple linear regression (MLR) techniques were employed to establish rice AGB models using vegetation index (VI), vegetation index combined with the corresponding-band texture (VI-CBT) and vegetation index combined with the full-band texture (VI-FBT). It was found that the models using VI and texture enhanced the capability of the conventional VI to estimate AGB at different combinations of growth stages. The combination of VI and FBT achieved the most accurate estimation, followed by the VI-CBT. Different combinations of growth stages had diverse responses to textures. Overall, the tillering stage had the maximum response to the involvement of textures, followed by the booting stage. Models using VI-CBT and VI-FBT significantly improved the AGB estimation at the booting and tillering stage, respectively. The monitoring accuracy of the jointing stage showed a slight response to the involvement of texture. At the multiple growth stages, the monitoring effect of the tillering-booting stages was significantly improved, followed by the jointing-booting stages either using the model based on VI-CBT or VI-FBT. Models using VI and texture tended to yield larger improvements on the error values at the extreme AGB levels. Near-infrared and red-edge bands were the sensitive bands to estimate rice AGB, and MEA, COR were favorable textures to monitor the AGB. This study quantified the model improvements of using VI and texture in rice AGB estimation at different combinations of growth stages, delivering guidance for timely and accurate rice management in the field. [ABSTRACT FROM AUTHOR]

Fusing hyperspectral images (HSIs) and multispectral images (MSIs) is an economic and feasible way to obtain images with both high spectral resolution and spatial resolution. Due to the limited receptive field of convolution kernels, fusion methods based on convolutional neural networks (CNNs) fail to take advantage of the global relationship in a feature map. In this paper, to exploit the powerful capability of Transformer to extract global information from the whole feature map for fusion, we propose a novel Multiscale Spatial–spectral Transformer Network (MSST-Net). The proposed network is a two-branch network that integrates the self-attention mechanism of the Transformer to extract spectral features from HSI and spatial features from MSI, respectively. Before feature extraction, cross-modality concatenations are performed to achieve cross-modality information interaction between the two branches. Then, we propose a spectral Transformer (SpeT) to extract spectral features and introduce multiscale band/patch embeddings to obtain multiscale features through SpeTs and spatial Transformers (SpaTs). To further improve the network's performance and generalization, we proposed a self-supervised pre-training strategy, in which a masked bands autoencoder (MBAE) and a masked patches autoencoder (MPAE) are specially designed for self-supervised pre-training of the SpeTs and SpaTs. Extensive experiments on simulated and real datasets illustrate that the proposed network can achieve better performance when compared to other state-of-the-art fusion methods. The code of MSST-Net will be available at http://www.jiasen.tech/papers/ for the sake of reproducibility. [Display omitted] • A multiscale spatial–spectral Transformer network is proposed. • Spectral multi-head self-attention is designed to extract spectral features. • Multiscale band/patch embeddings are introduced to extract Multiscale features. • A self-supervised pre-training strategy is developed. [ABSTRACT FROM AUTHOR]

• Imaging spectroscopy discloses the secrets of Picasso's painting "La Copa Blava". • Reflectance hyperspectral imaging enabled visualisation of earlier versions of the painting. • New-insights on the Picasso Blue period are gained with imaging spectroscopy. • Reflectance hyperspectral imaging has a potential for museums educational tasks. Reflectance hyperspectral imaging (HSI) in the Visible and Near-IR (VNIR) to the Short Wave IR (SWIR) ranges is acknowledged as one of the most effective techniques for non-invasive examination of paintings. Recently, in some important museums worldwide a new trend has emerged towards a more systematic use of reflectance HSI, which is applied as advanced imaging technique to attain at once analysis and documentation of the painted surfaces and inner layers. As an example of this trend, in the framework of the interdisciplinary program "Picasso: Blue Project" recently carried out at the Museu Picasso in Barcelona, a selection of paintings of the Blue Period (1901–1904) collection were revisited through the eye of cutting-edge analytical techniques, including reflectance HSI. The present paper focuses on the painting "The Blue Glass" (La Copa Blava, 1903), which was examined by using a high-performance reflectance HSI instrumentation. Under its visible surface, this iconic oil on canvas featured a stratification of different compositions that perfectly witnessed the Picasso's creative process. The HSI data enabled an unprecedented visualisation of the artistic metamorphosis of this painting. The well-known Picasso's creative practice of repainting on his own canvas was brought to the light by means of HSI images, which offered unique materials for visualising the hidden layers of the painting, by disclosing subsequent and unknown earlier versions and the artistic evolution of the painting. The outcomes of the project were disseminated in an exhibition, that addressed the ambitious objective of sharing with the wide public scholarly advanced knowledge hitherto gained on the Picasso Blue Period. Within this context, reflectance HSI proved to be exceptionally effective, not only for addressing analytical tasks, but also for educational purposes. [ABSTRACT FROM AUTHOR]

• Thermal Quasi-Reflectography (TQR) in the MWIR for book heritage. • Integrated approach of TQR and standard UV–vis–NIR imaging on manuscripts. • Joint use of TQR and UV-based imaging for surface investigation. • Proof of concept on Vergilius Veronensis palimpsest of Biblioteca Capitolare of Verona. Multispectral imaging techniques applied to book heritage are useful to scholars to obtain information on the materials under investigation. The novel introduction of Thermal Quasi-Reflectography (TQR) technique, operating in the medium wave infrared (MWIR) spectral range, can flank conventional diagnostic techniques such as UV-based methods and IR imaging techniques in the 850–1700 nm range on manuscript acquisition. As proof of concept, this work investigates the application of TQR on the Ms. XL (38) palimpsest, known as Vergilius Veronensis , of the Biblioteca Capitolare of Verona. While investigating the manuscript with full-field imaging technique, this work focuses its application to improve the mapping of conservation materials. The manuscript contains several traces of reagents and conservation treatments applied during its conservation history. The study compared the manuscript materials response obtained with TQR with the response collected using UV-induced visible fluorescence (UVL), UV reflected imaging (UVR), and NIR imaging in the 850–1050 nm and 900–1700 nm bands. Being the TQR technique sensitive to surface materials with MWIR reflectance features, it may especially provide more accurate documentation of the restoration history of the object. TQR imaging discriminated non-homogeneities of products in the surface layer thanks to an enhanced MWIR contrast. The TQR image response on manuscript materials should be further investigated and compared to reflectance spectroscopic data. The results of this study demonstrate the added value of TQR imaging for manuscript diagnostics, for which integration of the TQR technique in the standard protocol is desirable. [ABSTRACT FROM AUTHOR]

This paper follows the idea of divide and conquer to propose a multi-exposure fusion network for the unsupervised generation of high dynamic range-like images. We develop a new intrinsic image decomposition (IID) network based on our modified IID model to produce the reflectance, shading, and color components from source images, which respectively represent the texture structure, lighting condition, and visible color distribution of the imaging scene. Three fusion sub-networks are then designed to process these different components, producing pleasing images with rich structures, reasonable lighting, and suitable color. Specifically, we first develop a reflectance fusion network to integrate the reflectance components, in which an adaptive pixel-scale selection strategy is adopted to dynamically guide the network to preserve rich scene textures. Then, a shading fusion network with the adaptive global image-scale weighting strategy is designed to fulfill lighting adjustment, which can generate suitable illumination by defining the lighting adjustment as a game between the illumination of source images. Finally, we propose a color fusion network that embeds both pixel-scale and image-scale strategies, which can guarantee a pleasing scene color distribution with a specifically designed color fidelity loss. Extensive experiments demonstrate the superiority of our method over state-of-the-art methods in terms of texture integration, illumination adjustment, and color fidelity. Moreover, our method can be applied to fulfill tasks of the single low-light image enhancement and the single overexposed image correction with promising performance. The code is publicly available at https://github.com/HaoZhang1018/IID-MEF. • An MEF network based on intrinsic image decomposition is proposed to generate HDR images. • We design a decomposition network to produce reflectance, shading, and color. • We propose three sub-networks to integrate texture, adjust illumination, and preserve color. • Extensive experiments are conducted to verify the superiority of our method. [ABSTRACT FROM AUTHOR]

Although extensive researches have carried out on visible and infrared images fusion, quality assessment of the fused image is still challenging due to the absence of the reference image. In this paper, a subjective benchmark dataset and a semi-reference objective assessment method based on a Transformer encoder–decoder framework named Semantic-Relation Transformer (SRT), are developed for Visible and Infrared Fused Image Quality Assessment (VIF-IQA). Different with existing Transformers, SRT decoder can extract multi-level source image features and adopts a Multi-Head Self-Evaluation (MHSE) block which is constructed to mine latent relation knowledge between the fused image and source images. The relation knowledge is then injected into 3D-token with deep semantic embedding of receptive regions. Finally, the objective assessment score is obtained from-token through linear mapping of local to global. Moreover, we meticulously select 4,000 fused images from 200 scenes in TNO, MSRS, M3FD and Road Scene datasets and create a Visible and Infrared fuSed qualiTy Assessment (VISTA) dataset, which is guided by Subjective VIF-IQA Specification rigorously. VISTA dataset is utilized to comprehensively validate the proposed SRT. The experimental results demonstrated that the output of SRT is more in keeping with subjective feelings. Moreover, SRT has state-of-the-art performance on quantitative metrics when compared with 12 popular methods and the output of SRT is more in keeping with subjective feelings. VISTA dataset is available at https://github.com/ChangeZH/VISTA-Dataset. • A subjective benchmark dataset called Visible and Infrared fuSed qualiTy Assessment is built. • The subjective fused image quality assessment specification is formulated. • A new Semantic Relation Transformer is proposed for fused image quality assessment. • Fusion quality is assessed in SRT through semantic embedding and relation knowledge. • MHSE blocks are advanced to explore the correlation between source and fused images. [ABSTRACT FROM AUTHOR]

The brain–computer interface (BCI) provides a direct communication pathway between the human brain and external devices. However, the models trained for existing subjects perform poorly on new subjects, which is termed the subject calibration problem. In this paper, we propose a semi-supervised meta learning (SSML) method for subject-transfer calibration. The proposed SSML learns a model-agnostic meta learner with existing subjects and then fine-tunes the meta learner in a semi-supervised learning manner, i.e. using a few labelled samples and many unlabelled samples of the target subject for calibration. It is significant for BCI applications in which labelled data are scarce or expensive while unlabelled data are readily available. Three different BCI paradigms are tested: event-related potential detection, emotion recognition and sleep staging. The SSML achieved classification accuracies of 0.95, 0.89 and 0.83 in the benchmark datasets of three paradigms. The runtime complexity of SSML grows linearly as the number of samples of target subject increases so that is possible to apply it in real-time systems. This study is the first attempt to apply semi-supervised model-agnostic meta learning methodology for subject calibration. The experimental results demonstrated the effectiveness and potential of the SSML method for subject-transfer BCI applications. [ABSTRACT FROM AUTHOR]