Search

The applications of hyperspectral imaging across disciplines such as healthcare, automobiles, forensics, and astronomy are constrained by the requirement for intricate filters and dispersion lenses. By utilization of devices with engineered spectral responses and advanced signal processing techniques, the spectral imaging process can be made more approachable across various fields. We propose a spectral response design method employing photon-trapping surface textures (PTSTs), which eliminates the necessity for external diffraction optics and facilitates system miniaturization. We have developed an analytical model to calculate electromagnetic wave coupling using the effective refractive index of silicon in the presence of PTST. We have extensively validated the model against simulations and experimental data, ensuring the accuracy of our predictions. We observe a strong linear relationship between the peak coupling wavelength and the PTST period along with a moderate proportional relation to the PTST diameters. Additionally, we identify a significant correlation between inter-PTST spacing and wave propagation modes. The experimental validation of the model is conducted using PTST-equipped photodiodes fabricated through complementary metal-oxide-semiconductor-compatible processes. Further, we demonstrate the electrical and optical performance of these PTST-equipped photodiodes to show high speed (response time: 27 ps), high gain (multiplication gain, M: 90), and a low operating voltage (breakdown voltage: ∼ 8.0 V). Last, we utilize the distinctive response of the fabricated PTST-equipped photodiode to simulate hyperspectral imaging, providing a proof of principle. These findings are crucial for the progression of on-chip integration of high-performance spectrometers, guaranteeing real-time data manipulation, and cost-effective production of hyperspectral imaging systems.

Image denoising is a widely concerned problem, which has been successfully applied in remote sensing, medicine and other fields. A typical idea of image denoising is to exploit some prior information existing in real-world data, such as low-rank prior and local smoothness prior. Some researchers devote themselves to combining both priors, however, the current methods cannot capture both properties simultaneously and adequately. Motivated by a new regularizer named three-dimensional correlated total variation (3DCTV) for robust principal component analysis problem, in this paper, we propose a new image denoising model via the double-weighted correlated total variation regularization. Specifically, we perform weighting operations on the 3DCTV regularization term and the sparse term separately, which can make fuller use of the low-rank prior, the local smoothness prior and the sparse prior of images. In addition, we add the Frobenius norm term to this model for modeling strong Gaussian noise in some real-world scenarios. Then, we develop an efficient algorithm to solve the resulting optimization problem by using the well-known alternating direction method of multipliers. Finally, we conduct extensive experiments on hyperspectral images, multispectral images and medical images under various noise situations, and the experimental results show that the proposed method outperforms the existing state-of-the-art denoising methods. Especially when the test image is polluted by low-intensity sparse noise, the MPSNR index of our method is about 5 points higher than that of the 3DCTV method. [ABSTRACT FROM AUTHOR]

Filter array multispectral cameras are influenced by imaging mechanism and process characteristics, spliced images have edge interference fringes and greyscale differences. Aiming at the problems of inconsistent greyscale of filter array multispectral camera, a new method of greyscale correction algorithm is proposed in this paper. First, the mechanism and adjustment principle of edge interference streaks and stripe greyscale difference are thoroughly analyzed; Second, the greyscale of interference area is adjusted by using greyscale of adjacent image non-interference area; Third, the greyscale of whole image is adjusted by using proportional relationship between the adjacent overlap area greyscale; Finally, sequence images are spliced to obtain single-band image with same greyscale. Theoretical analysis and experimental results show that this method can not only effectively solve the problem of inconsistent greyscale due to the influence of imaging mechanism and process characteristics, but also can maximally preserve spectral information characteristics in different wavelength bands. [ABSTRACT FROM AUTHOR]

Automatic road feature extraction from the remote sensing (RS) imagery has a significant role in various applications such as urban planning, transportation management, and environmental monitoring. In this paper, propose a method based on the U-Net model to extract the road features from the LANDSAT-8 operational land imager (OLI) images. This method aims to extract road features in OLI images that appear as curvilinear features and roads with widths greater than 25 meters, which are mostly covered within a single pixel of the OLI resolution of multi-spectral images. The U-Net architecture is well-known for its effectiveness in image segmentation tasks. However, to optimize the complexity in the U-Net model, simplified the architecture while retaining its key components and principles. The proposed model by decreasing the convolution layers and the parameters which are involved to optimize the model called as simplified U-Net model. To train this model, the label images were generated for LANDSAT-8 OLI images, by using the saturation based adaptive thresholding and morphology (SATM) method. This reduces the efforts to draw the labels in the vector format labels and convert to raster images. The model is able to effectively generate weights, which are able to extract the road features. This model weights applied on the OLI images which covers the urban and rural areas of India, producing the satisfactory results. The experimental results with the quantitative analysis presented in the paper. [ABSTRACT FROM AUTHOR]

In remote sensing, the information present in hyperspectral images (HSI) and multispectral images (MSI) often contrasts with each other. HSI has a higher spectral resolution than spatial resolution, while MSI is rich in spatial details. Image fusion aims to integrate crucial information from these source images into a single fused image that enhances both spatial and spectral features. In this work, we introduce a deep interpolating convolutional neural network (DICNN) model that utilizes anisotropic-dependent principal component analysis (PCA) for HSI-MSI fusion. Initially, our goal is to enhance the HSI resolution by training the DICNN on numerous examples. This training aligns the spatial resolutions between HSI and MSI data. Subsequently, we replace the highest variance HSI principal components with their corresponding MSI counterparts obtained after band selection. Additionally, we preserve significant edge information through the incorporation of anisotropic filtering. The proposed methodology demonstrates superior results compared to other existing state-of-the-art methods, both qualitatively and quantitatively. [ABSTRACT FROM AUTHOR]

Due to the complex environments of burned areas and limitations of hardware devices, the collected multi-spectral image (MSI) is sometimes with many mixed pixels to hinder the accurate mapping of burned areas. To solve this problem, sub-pixel mapping (SPM) technology has been applied to handle with these mixed pixels to map burned areas. However, the spatial–spectral information of burned areas used by SPM is usually constructed in a specified rectangular local window, and the number of spectral bands utilized by SPM is also little, affecting the mapping accuracy of burned areas. To improve the mapping accuracy of burned areas, we propose burned-area SPM based on spatial–spectral information at super-pixel scale for multi-spectral image (SSIASC). In SSIASC, super-pixels representing the burned areas with irregular distribution are obtained by interpolation and then segmentation of the original coarse MSI. The extended random walker algorithm is then used to calculate the spatial correlation in super-pixels to obtain spatial term, and at the same time the normalized model is constructed to calculate all the spectral bands in super-pixels to yield spectral term. Next, the two terms are integrated to produce the objective term with spatial–spectral information at super-pixel scale. Finally, particle swarm optimization is employed to optimize the objective term to derive the burned-area mapping result. Experimental results on the two burned-area MSIs show that the proposed SSIASC produces the better results than the state-of-the-art SPM methods. [ABSTRACT FROM AUTHOR]

Waterlogging is a significant concern in urban areas and can result in severe disruptions and damage and it's an urban problem. This study is conducted in Thoothukudi and Tamil Nadu, which are particularly sensitive to waterlogging because of their geographical and meteorological circumstances. Using synthetic aperture radar (SAR) images from 2015 to 2022, topographical analysis, land use/land cover (LULC) data, and geological insights, this research intends to identify and assess areas prone to water logging. The data source for this study comprises rainfall records from the Indian Meteorological Department (IMD), Sentinel-1 SAR imagery, Sentinel-2 multispectral images from the European Space Agency (ESA), and the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM).Terrain analysis was undertaken using DEM to generate elevation, slope, and aspect maps, while SAR data were processed to extract water pixels, which included the extraction of water pixels from SAR data for each year and overlaying them. The overlaid image was correlated with topographic maps to identify the high-risk regions. Key places such as Muthayapuram, Milavittan, Bryant Nagar, and Thalamuthunagar were constantly highlighted as prone to floods. Additionally, the saltpan regions, defined by low-lying water table levels, endure continuous flooding, demonstrating the usefulness of combining SAR imaging with topographic analysis for urban water management. These findings provide useful insights for urban planners and policymakers, underlining the need for deliberate steps to reduce waterlogging, maintain public health, and minimize infrastructure damage, thus enabling sustainable development in Thoothukudi. [ABSTRACT FROM AUTHOR]

Bacterial Leaf Blight (BLB) usually attacks rice in the flowering stage and can cause yield losses of up to 50% in severely infected fields. The resulting yield losses severely impact farmers, necessitating compensation from the regulatory authorities. This study introduces a new pipeline specifically designed for detecting BLB in rice fields using unmanned aerial vehicle (UAV) imagery. Employing the U-Net architecture with a ResNet-101 backbone, we explore three band combinations—multispectral, multispectral+NDVI, and multispectral+NDRE—to achieve superior segmentation accuracy. Due to the lack of suitable UAV-based datasets for rice disease, we generate our own dataset through disease inoculation techniques in experimental paddy fields. The dataset is increased using data augmentation and patch extraction methods to improve training robustness. Our findings demonstrate that the U-Net model incorporating ResNet-101 backbone trained with multispectral+NDVI data significantly outperforms other band combinations, achieving high accuracy metrics, including mean Intersection over Union (mIoU) of up to 97.20%, mean accuracy of up to 99.42%, mean F1-score of up to 98.56%, mean Precision of 97.97%, and mean Recall of 99.16%. Additionally, this approach efficiently segments healthy rice from other classes, minimizing misclassification and improving disease severity assessment. Therefore, the experiment concludes that the accurate mapping of the disease extent and severity level in the field is reliable to accurately allocating the compensation. The developed methodology has the potential for broader application in diagnosing other rice diseases, such as Blast, Bacterial Panicle Blight, and Sheath Blight, and could significantly enhance agricultural management through accurate damage mapping and yield loss estimation. [ABSTRACT FROM AUTHOR]

Background/Objectives: Performing hematoxylin and eosin (H&E) staining and immunohistochemistry (IHC) on the same specimen slide provides advantages that include specimen conservation and the ability to combine the H&E context with biomarker expression at the individual cell level. We previously used invisible deposited chromogens and dual-camera imaging, including monochrome and color cameras, to implement simultaneous H&E and IHC. Using this approach, conventional H&E staining could be simultaneously viewed in color on a computer monitor alongside a monochrome video of the invisible IHC staining, while manually scanning the specimen. Methods: We have now simplified the microscope system to a single camera and increased the IHC multiplexing to four biomarkers using translational assays. The color camera used in this approach also enabled multispectral imaging, similar to monochrome cameras. Results: Application is made to several clinically relevant specimens, including breast cancer (HER2, ER, and PR), prostate cancer (PSMA, P504S, basal cell, and CD8), Hodgkin's lymphoma (CD15 and CD30), and melanoma (LAG3). Additionally, invisible chromogenic IHC was combined with conventional DAB IHC to present a multiplex IHC assay with unobscured DAB staining, suitable for visual interrogation. Conclusions: Simultaneous staining and detection, as described here, provides the pathologist a means to evaluate complex multiplexed assays, while seated at the microscope, with the added multispectral imaging capability to support digital pathology and artificial intelligence workflows of the future. [ABSTRACT FROM AUTHOR]

A good quantitative knowledge of regional sources and sinks of atmospheric carbon dioxide (CO2) is essential for understanding the global carbon cycle. It is also a key prerequisite for elaborating cost-effective national strategies to achieve the goals of the Paris Agreement. However, available estimates of CO2 fluxes for many regions of the world remain uncertain, despite significant recent progress in the remote sensing of terrestrial vegetation and atmospheric CO2. In this study, we investigate the feasibility of inferring reliable regional estimates of the net ecosystem exchange (NEE) using column-averaged dry-air mole fractions of CO2 (XCO2) retrieved from Orbiting Carbon Observatory-2 (OCO-2) observations as constraints on parameters of the widely used Vegetation Photosynthesis and Respiration model (VPRM), which predicts ecosystem fluxes based on vegetation indices derived from multispectral satellite imagery. We developed a regional-scale inverse modeling system that applies a Bayesian variational optimization algorithm to optimize parameters of VPRM coupled to the CHIMERE chemistry transport model and which involves a preliminary transformation of the input XCO2 data that reduces the impact of the CHIMERE boundary conditions on inversion results. We investigated the potential of our inversion system by applying it to a European region (that includes, in particular, the EU countries and the UK) for the warm season (May–September) of 2021. The inversion of the OCO-2 observations resulted in a major (more than threefold) reduction of the prior uncertainty in the regional NEE estimate. The posterior NEE estimate agrees with independent estimates provided by the CarbonTracker Europe High-Resolution (CTE-HR) system and the ensemble of the v10 OCO-2 model intercomparison (MIP) global inversions. We also found that the inversion improves the agreement of our simulations of XCO2 with retrievals from the Total Carbon Column Observing Network (TCCON). Our sensitivity test experiments using synthetic XCO2 data indicate that the posterior NEE estimate would remain reliable even if the actual regional CO2 fluxes drastically differed from their prior values. Furthermore, the posterior NEE estimate is found to be robust to strong biases and random uncertainties in the CHIMERE boundary conditions. Overall, this study suggests that our approach offers a reliable and relatively simple way to derive robust estimates of CO2 ecosystem fluxes from satellite XCO2 observations while enhancing the applicability of VPRM in regions where eddy covariance measurements of CO2 fluxes are scarce. [ABSTRACT FROM AUTHOR]

This study presents a method for aligning the geometric parameters of images in multi-channel imaging systems based on the application of pre-processing methods, machine learning algorithms, and a calibration setup using an array of orderly markers at the nodes of an imaginary grid. According to the proposed method, one channel of the system is used as a reference. The images from the calibration setup in each channel determine the coordinates of the markers, and the displacements of the marker centers in the system's channels relative to the coordinates of the centers in the reference channel are then determined. Correction models are obtained as multiple polynomial regression models based on these displacements. These correction models align the geometric parameters of the images in the system channels before they are used in the calculations. The models are derived once, allowing for geometric calibration of the imaging system. The developed method is applied to align the images in the channels of a module of a multispectral imaging polarimeter. As a result, the standard image alignment error in the polarimeter channels is reduced from 4.8 to 0.5 pixels. [ABSTRACT FROM AUTHOR]

In this study, we used desert soil from Gansu, China, as a sample to propose a method for designing hyperspectral stealth coatings against desert soil backgrounds within the spectral range of 400–2500 nm, and the corresponding coating was prepared. Firstly, the correlation between the composition and typical spectral detected characteristics of the desert soil was systematically analyzed. It was found that the color and the spectrum of the desert soil in the range of 400–1000 nm were influenced by different types of iron oxides. The main spectral characteristic and reflection intensity at 1000–2500 nm were impacted by quartz and montmorillonite. Subsequently, the design method for hyperspectral stealth coatings was developed by analyzing the differences in spectral and structural characteristics between the coatings and the soil. The prepared coating exhibited similar color and spectral shape to the soil in the range of 400–1000 nm, with comparable spectral features around 1414 nm, 1915 nm, 2212 nm, 2250 nm, and 2346 nm. The correlation coefficient and the spectral cosine angle between the reflectance spectra of the coating and the soil within the 400–2500 nm wavelength were calculated to be 0.989 and only 0.05 radians, respectively. The effectiveness of the coating in achieving excellent camouflage against the desert soil background was confirmed through the analysis of multispectral images and thermal infrared temperature. This study holds significant importance for the application of hyperspectral stealth techniques in desert soil scenarios. [ABSTRACT FROM AUTHOR]

AbstractThis review focused on mass spectrometry imaging (MSI), a powerful tool in food analysis, covering its ion source schemes and procedures and their applications in food quality, safety, and nutrition to provide detailed insights into these aspects. The review presented a detailed introduction to both commonly used and emerging ionization sources, including nanoparticle laser desorption/ionization (NPs-LDI), air flow-assisted ionization (AFAI), desorption ionization with through-hole alumina membrane (DIUTHAME), plasma-assisted laser desorption ionization (PALDI), and low-temperature plasma (LTP). In the MSI process, particular emphasis was placed on quantitative MSI (QMSI) and super-resolution algorithms. These two aspects synergistically enhanced MSI’s analytical capabilities: QMSI enabled accurate relative and absolute quantification, providing reliable data for composition analysis, while super-resolution algorithms improved molecular spatial imaging resolution, facilitating biomarker and trace substance detection. MSI outperformed conventional methods in comprehensively exploring food functional factors, biomarker discovery, and monitoring processing/storage effects by discerning molecular species and their spatial distributions. However, challenges such as immature techniques, complex data processing, non-standardized instruments, and high costs existed. Future trends in instrument enhancement, multispectral integration, and data analysis improvement were expected to deepen our understanding of food chemistry and safety, highlighting MSI’s revolutionary potential in food analysis and research. [ABSTRACT FROM AUTHOR]

Recent studies have demonstrated a rising trend in the utilization of multispectral and hyperspectral image de-noising, which is crucial for improving the visual quality of images and facilitating more efficient image processing tasks. Despite significant advancements in image denoising research, removing noise without distorting the image remains a challenging task. This paper addresses these limitations of multispectral satellite images and performs denoising of multispectral images by integrating noise estimates with a hybrid denoising framework that combines the benefits of two distinct approaches to image processing. Integrating filtering with Wavelet decomposition yields a denoising solution that is more robust and accurate than either technique could achieve on its own. Experiments on multispectral images from Sentinel-2 demonstrate the applicability and generalization of the proposed method in real-life scenarios. The comparative analysis indicates that this method significantly outperforms existing techniques, thereby demonstrating its superior performance. [ABSTRACT FROM AUTHOR]

In this study, we explore an enhancement to the U-Net architecture by integrating SK-ResNeXt as the encoder for Land Cover Classification (LCC) tasks using Multispectral Imaging (MSI). SK-ResNeXt introduces cardinality and adaptive kernel sizes, allowing U-Net to better capture multi-scale features and adjust more effectively to variations in spatial resolution, thereby enhancing the model's ability to segment complex land cover types. We evaluate this approach using the Five-Billion-Pixels dataset, composed of 150 large-scale RGB-NIR images and over 5 billion labeled pixels across 24 categories. The approach achieves notable improvements over the baseline U-Net, with gains of 5.312% in Overall Accuracy (OA) and 8.906% in mean Intersection over Union (mIoU) when using the RGB configuration. With the RG-NIR configuration, these improvements increase to 6.928% in OA and 6.938% in mIoU, while the RGB-NIR configuration yields gains of 5.854% in OA and 7.794% in mIoU. Furthermore, the approach not only outperforms other well-established models such as DeepLabV3, DeepLabV3+, Ma-Net, SegFormer, and PSPNet, particularly with the RGB-NIR configuration, but also surpasses recent state-of-the-art methods. Visual tests confirmed this superiority, showing that the studied approach achieves notable improvements in certain classes, such as lakes, rivers, industrial areas, residential areas, and vegetation, where the other architectures struggled to achieve accurate segmentation. These results demonstrate the potential and capability of the explored approach to effectively handle MSI and enhance LCC results. [ABSTRACT FROM AUTHOR]

In modern imaging systems, the application of multispectral imaging technologies is pervasive, furnishing an enhanced spectrum of information. Multispectral methods typically employ arrays of filters to selectively exclude light from undesired spectral bands, thus facilitating the capture of discrete narrowband data. However, the inherent multi-channel filtering process limits their energy utilization efficiency, a constraint that is magnified by the current trend of miniaturization in imaging devices. In this work, we have developed a pixel-level, metal-based, mid-infrared router by employing an inverse design method. This design achieved peak spectral efficiencies of 58.61% and 67.35% within the operational bands of 3.5–4.2 and 4.4–5 μm, respectively, and an average energy utilization efficiency across the entire operational range of 3.5–5 μm was elevated to 72%, which is 1.44 times higher than that of conventional filter-based systems. The designed routers were realized by standard nanofabrication processes that transfer the designed patterns into a gold film on a ZnS substrate. The spectral measurements show that the fabricated routers have a routing performance close to the simulation results. [ABSTRACT FROM AUTHOR]

With rapid advancement in ISR (intelligence, surveillance, and reconnaissance), the demand for multispectral stealth technology has become urgent. In the field of radar stealth, 3D metamaterial absorbers have garnered significant attention due to their ultra-wideband microwave absorption. However, they face the challenge of restricted multispectral-compatible stealth capabilities, elevating the risk of being detected under ISR technology. In this study, we propose a solution by covering the absorber with a structural topology-based pixelated color-changing layer (STPCL), providing environmental camouflage and enhancing absorption intensity. Multiwall carbon nanotubes/spherical carbonyl iron/silicone rubber composites and thermochromic capsules/polydimethylsiloxane composites are used to fabricate the absorber and the STPCL, respectively. The STPCL not only provides adaptive camouflage in grassland and desert environments but also increases the characteristic dimensions to tune the absorption peaks and incorporates a grading circuit with stepped impedance to enhance impedance matching. As a result, the absorption bandwidth is slightly extended from 3.28–40 to 2.87–40 GHz, while the average reflection loss is improved from −13.55 to −16.83 dB. This approach demonstrates the potential to enhance the functionality and adaptability of metamaterial microwave absorbers in diverse operational environments. [ABSTRACT FROM AUTHOR]

AbstractPurposeMethodsResultsConclusionThis study explores the potential interaction of brolucizumab with platelets and its effects on platelet activation and reactivity, crucial in retinal vasculitis and retinal vascular occlusion. Safety concerns remain of interest, although brolucizumab showed superior retinal efficacy and reduced injection frequency compared to other licensed anti-VEGF agents.Resting and activated platelets of healthy volunteers were pretreated with brolucizumab at the following concentrations 0.6 µg/mL, 3 µg/mL, 6 µg/mL, 300 µg/mL, and 3000 µ/mL or its solvent or PBS. The surface expression of platelet activation markers GPIIb/IIIa and P-selectin was determined by multispectral imaging flow cytometry, which combines flow cytometry and fluorescence microscopy. Two different methods were used to examine the interaction of brolucizumab with platelets: 1) A cross-pretreatment experiment was performed with FITC-labeled brolucizumab and bevacizumab; 2) Resting and activated platelets were pretreated with brolucizumab or its solvent or PBS, followed by anti-brolucizumab antibody generated by rabbit immunization.Brolucizumab did not significantly affect platelet activation compared to solvent or PBS, across a range of concentrations. No significant upregulation of CD62P and no activation of the fibrinogen receptor (GPIIb/IIa) were observed in resting and TRAP-activated platelets. After pretreatment with PBS, the level of brolucizumab-FITC was significantly lower in comparison to bevacizumab-FITC (normalized MFI = 3.32, CI = 3.16–3.48 vs. normalized MFI = 7.19, CI = 7.04–7.35; p < 0.001). Both brolucizumab- and bevacizumab-FITC were downregulated after pretreatment with brolucizumab or bevacizumab compared to pretreatment with PBS. Antibodies against brolucizumab did not show any significant difference between pretreatment with brolucizumab and its solvent in resting and TRAP-activated platelets.Brolucizumab does not appear to directly affect platelet activation or reactivity to thrombin receptor agonists. No platelet interaction was observed after increasing brolucizumab concentrations or anti-brolucizumab antibodies in resting and activated platelets. However, brolucizumab might be taken up in platelets. [ABSTRACT FROM AUTHOR]

Nonlinear optics, a critical branch of modern optics, presents unique potential in the study of two-dimensional (2D) magnetic materials. These materials, characterized by their ultra-thin geometry, long-range magnetic order, and diverse electronic properties, serve as an exceptional platform for exploring nonlinear optical effects. Under strong light fields, 2D magnetic materials exhibit significant nonlinear optical responses, enabling advancements in novel optoelectronic devices. This paper outlines the principles of nonlinear optics and the magnetic structures of 2D materials, reviews recent progress in nonlinear optical studies, including magnetic structure detection and nonlinear optical imaging, and highlights their role in probing magnetic properties by combining second harmonic generation (SHG) and multispectral integration. Finally, we discuss the prospects and challenges for applying nonlinear optics to 2D magnetic materials, emphasizing their potential in next-generation photonic and spintronic devices. [ABSTRACT FROM AUTHOR]

Pansharpening is a traditional image fusion problem where the reference image (or ground truth) is not accessible. Machine-learning-based algorithms designed for this task require an extensive optimization phase of network parameters, which must be performed using unsupervised learning techniques. The learning phase can either rely on a companion problem where ground truth is available, such as by reproducing the task at a lower scale or using a pretext task, or it can use a reference-free cost function. This study focuses on the latter approach, where performance depends not only on the accuracy of the quality measure but also on the mathematical properties of these measures, which may introduce challenges related to computational complexity and optimization. The evaluation of the most recognized no-reference image quality measures led to the proposal of a novel criterion, the Regression-based QNR (RQNR), which has not been previously used. To mitigate computational challenges, an approximate version of the relevant indices was employed, simplifying the optimization of the cost functions. The effectiveness of the proposed cost functions was validated through the reduced-resolution assessment protocol applied to a public dataset (PairMax) containing images of diverse regions of the Earth's surface. [ABSTRACT FROM AUTHOR]

Multispectral imaging (MSI) enables non-invasive tissue differentiation based on spectral characteristics and has shown great potential as a tool for surgical guidance. However, adapting MSI to open surgeries is challenging. Systems that rely on light sources present in the operating room experience limitations due to frequent lighting changes, which distort the spectral data and require countermeasures such as disruptive recalibrations. On the other hand, MSI systems that rely on dedicated lighting require external light sources, such as surgical lights, to be turned off during open surgery settings. This disrupts the surgical workflow and extends operation times. To this end, we present an approach that addresses these issues by combining active illumination with smart background suppression. By alternately capturing images with and without a modulated light source at a desired wavelength, we isolate the target signal, enabling artifact-free spectral scanning. We demonstrate the performance of our approach using a smart pixel camera, emphasizing its signal-to-noise ratio (SNR) advantage over a conventional high-speed camera. Our results show that accurate reflectance measurements can be achieved in clinical settings with high background illumination. Medical application is demonstrated through the estimation of blood oxygenation, and its suitability for open surgeries is discussed. [ABSTRACT FROM AUTHOR]

Land cover information is becoming more important in urban planning, change detection, and management. The fusion of point clouds and images increases the accuracy of land use classification by utilising the advantages of both modalities. Similar structures such as buildings and roads, low and high vegetation, and impervious and bare regions are not too much discriminative. Models fail to discriminate these classes leading to misclassifications, false detections, and unreliable land cover maps. Therefore, this research proposes the fusion of dense point clouds and multi-spectral images based on a dual-stream deep convolutional model by adding vegetation and elevation information to spectral information. To fuse both modalities' features, a dual-stream deep neural network based on Deeplabv3+ architecture is implemented. In addition, the Xception (Extreme Inception) model is considered as a backbone and feature extractor. The model performance is evaluated with F1-Score and Overall Accuracy. 93.4% Overall Accuracy and F1-Score are achieved after adding height and vegetation information to the model. Results indicate improvements in all indexes, meaning that data fusion with the proposed model outperforms the existing state-of-the-art models. [ABSTRACT FROM AUTHOR]

Pansharpening is a crucial technique in remote sensing for enhancing spatial resolution by fusing low spatial resolution multispectral (LRMS) images with high spatial panchromatic (PAN) images. Existing deep convolutional networks often face challenges in capturing fine details due to the homogeneous operation of convolutional kernels. In this paper, we propose a novel predictive filtering approach for pansharpening to mitigate spectral distortions and spatial degradations. By obtaining predictive filters through the fusion of LRMS and PAN and conducting filtering operations using unique kernels assigned to each pixel, our method reduces information loss significantly. To learn more effective kernels, we propose an effective fine-grained fusion method for LRMS and PAN features, namely element-wise feature mixing. Specifically, features of LRMS and PAN will be exchanged under the guidance of a learned mask. The value of the mask signifies the extent to which the element will be mixed. Extensive experimental results demonstrate that the proposed method achieves better performances than the state-of-the-art models with fewer parameters and lower computations. Visual comparisons indicate that our model pays more attention to details, which further confirms the effectiveness of the proposed fine-grained fusion method. Codes are available at https://github.com/yc-cui/PreMix. [ABSTRACT FROM AUTHOR]

In this study, we present a method for detecting and analysing the velocities of moving objects in Earth observation satellite images, specifically using data from Planet Labs' push broom scanning satellites. By exploiting the sequential acquisition of multi-spectral images, we estimate the relative differences in acquisition times between spectral bands. This allows us to determine the velocities of moving objects, such as aircraft, even without precise timestamp information from the image archive. We validate our method by comparing the velocities of aircraft observed in satellite images with those reported by onboard ADS-B transponders and find an accuracy of $ \sim 4$ ∼ 4 %. The results demonstrate the potential, despite challenges posed by the limitations of proprietary data, of a new application of commercial satellite data originally intended as an ongoing, once-daily survey of single images covering the entire land-area of the Earth. Our approach extends the applicability of satellite survey imagery for dynamic object tracking and contributes to the broader use of commercial satellite data in scientific research. [ABSTRACT FROM AUTHOR]

Olive leaf spot (OLS), caused by Fusicladium oleagineum, is a significant disease affecting olive orchards, leading to reduced yields and compromising olive tree health. Early and accurate detection of this disease is critical for effective management. This study presents a comprehensive assessment of OLS disease progression in olive orchards by integrating agronomic measurements and multispectral imaging techniques. Key disease parameters—incidence, severity, diseased leaf area, and disease index—were systematically monitored from March to October, revealing peak values of 45% incidence in April and 35% severity in May. Multispectral drone imagery, using sensors for NIR, Red, Green, and Red Edge spectral bands, enabled the calculation of vegetation indices. Indices incorporating Red Edge and near-infrared bands, such as Red Edge and SR705-750, exhibited the strongest correlations with disease severity (correlation coefficients of 0.72 and 0.68, respectively). This combined approach highlights the potential of remote sensing for early disease detection and supports precision agriculture practices by facilitating targeted interventions and optimized orchard management. The findings underscore the effectiveness of integrating a traditional agronomic assessment with advanced spectral analysis to improve OLS disease surveillance and promote sustainable olive cultivation. [ABSTRACT FROM AUTHOR]

Global climatic changes increase areas that are influenced by drought. Remote sensing based on the spectral characteristics of reflected light is widely used to detect the action of stressors (including drought) in plants. The development of methods of improving remote sensing is an important applied task for plant cultivation. Particularly, this improvement can be based on the calculation of reflectance indices and revealing the optimal spectral bandwidths for this calculation. In the current work, we analyzed the sensitivity of broadband-normalized difference reflectance indices and RGB indices to the action of soil drought on pea and wheat plants. Analysis of the heat maps of significant changes in reflectance indices showed that increasing the spectral bandwidths did not decrease this significance in some cases. Particularly, the index RI(659, 553) based on the red and green bandwidths was strongly sensitive to drought action in plants. The normalized red–green index (NRGI), which was the RGB-analog of RI(659, 553) measured by a color camera, was also sensitive to drought. RI(659, 553) and NRGI were strongly related. The results showed that broadband and RGB indices can be used to detect drought action in plants. [ABSTRACT FROM AUTHOR]

In forest ecosystems, the application of hyperspectral (HS) imagery offers unprecedented opportunities for refined identification and classification. The diversity and complexity of forest cover make it challenging for traditional remote-sensing techniques to capture subtle spectral differences. Hyperspectral imagery, however, can reveal the nuanced changes in different tree species, vegetation health status, and soil composition through its nearly continuous spectral information. This detailed spectral information is crucial for the monitoring, management, and conservation of forest resources. While Convolutional Neural Networks (CNNs) have demonstrated excellent local context modeling capabilities in HS image classification, their inherent network architecture limits the exploration and representation of spectral feature sequence properties. To address this issue, we have rethought HS image classification from a sequential perspective and proposed a hybrid model, the Spectral Weaver, which combines CNNs and Transformers. The Spectral Weaver replaces the traditional Multi-Head Attention mechanism with a Channel Attention mechanism (MCA) and introduces Centre-Differential Convolutional Layers (Conv2d-cd) to enhance spatial feature extraction capabilities. Additionally, we designed a cross-layer skip connection that adaptively learns to fuse "soft" residuals, transferring memory-like components from shallow to deep layers. Notably, the proposed model is a highly flexible backbone network, adaptable to both hyperspectral and multispectral image inputs. In comparison to traditional Visual Transformers (ViT), the Spectral Weaver innovates in several ways: (1) It introduces the MCA mechanism to enhance the mining of spectral feature sequence properties; (2) It employs Centre-Differential Convolutional Layers to strengthen spatial feature extraction; (3) It designs cross-layer skip connections to reduce information loss; (4) It supports both multispectral and hyperspectral inputs, increasing the model's flexibility and applicability. By integrating global and local features, our model significantly improves the performance of HS image classification. We have conducted extensive experiments on the Gaofen dataset, multispectral data, and multiple hyperspectral datasets, validating the superiority of the Spectral Weaver model in forest hyperspectral image classification. The experimental results show that our model achieves 98.59% accuracy on multispectral data, surpassing ViT's 96.30%. On the Jilin-1 dataset, our proposed algorithm achieved an accuracy of 98.95%, which is 2.17% higher than ViT. The model significantly outperforms classic ViT and other state-of-the-art backbone networks in classification performance. Not only does it effectively capture the spectral features of forest vegetation, but it also significantly improves the accuracy and robustness of classification, providing strong technical support for the refined management and conservation of forest resources. [ABSTRACT FROM AUTHOR]

Avocado production faces a substantial global threat in the form of Phytophthora root rot (PRR). When trees succumb to PRR, their canopy health deteriorates, leading to adverse impacts on production. To effectively implement remedial strategies, infected trees need to be identified, evaluated, and located within the field. The current commercially accepted method for determining PRR severity in canopies consists of a visual estimation using the 'Ciba-Geigy' rating scale. This rating scale incorporates a numerical severity ranking system based on a visual approach conducted by trained personnel. However, tracking tree health using visual ratings is a time-consuming process, fraught with practical challenges arising from gradual visual changes, spatial variation, and dimensions of the orchards. To address these limitations, the integration of remote sensor-based methods is proposed as a viable alternative to the visual severity ranking. A field experiment was conducted in two avocado blocks to investigate the effect of spatial resolution, phenological stages, and canopy conditions on the mapping of PRR severity. The results of this study showed that canopy management practices revealed a pronounced influence in the determination of the severity ranking using remote sensing (RS) methods and that these methods can be used as an alternative to visual estimations. Additionally, the spatial resolution of the images emerged as a significant factor, improving the estimation of severity when more detailed spatial data were incorporated into the analysis. In the most favorable scenario, an R2 determination coefficient of 0.80 was achieved. In summary, RS approaches can provide valuable information to mitigate the effect of PRR in avocado production. However, the image characteristics and particular canopy conditions need to be carefully considered in order to deliver a reliable method that can be used for informed decision-making. Nonetheless, the results were promising and could open doors to further investigate RS methods as a subjective and efficient means of PRR severity rankings. [ABSTRACT FROM AUTHOR]

The increasing demand for extracting comprehensive information from light through multispectral and polarization imaging has driven the development of advanced photodetection technologies. In response, a polarization‐sensitive narrowband InGaAs photodetector (PD) operating in the short‐wave infrared (SWIR) range is proposed, capable of capturing wavelength, intensity, and polarization data concurrently without additional optical components. The device is formed by integrating an InGaAs PD onto a silicon grating, utilizing the guided‐mode resonance (GMR) effect to amplify absorption at specific target wavelengths. The intrinsic polarization dependence of the 1D GMR structure allows for distinct absorption peaks for TE and TM polarized light. The detection performance of the device, including spectral rejection ratios greater than 30, peak responsivities of 0.46 A W−1, and polarization extinction ratios of up to 41.3 is demonstrated. Precise design of the period and arrangement of the grating enables fabrication of pixel arrays with diverse detection wavelengths and polarization directions, in a single process eliminating the process complexity. This is the only capability of this study among previously reported devices. [ABSTRACT FROM AUTHOR]

Background: Senescence is a complex developmental process that is regulated by a multitude of environmental, genetic, and physiological factors. Optimizing the timing and dynamics of this process has the potential to significantly impact crop adaptation to future climates and for maintaining grain yield and quality, particularly under terminal stress. Accurately capturing the dynamics of senescence and isolating the genetic variance component requires frequent assessment as well as intense field testing. Here, we evaluated and compared the potential of temporally dense drone-based RGB- and multispectral image sequences for this purpose. Regular measurements were made throughout the grain filling phase for more than 600 winter wheat genotypes across three experiments in a high-yielding environment of temperate Europe. At the plot level, multispectral and RGB indices were extracted, and time series were modelled using different parametric and semi-parametric models. The capability of these approaches to track senescence was evaluated based on estimated model parameters, with corresponding parameters derived from repeated visual scorings as a reference. This approach represents the need for remote-sensing based proxies that capture the entire process, from the onset to the conclusion of senescence, as well as the rate of the progression. Results: Our results indicated the efficacy of both RGB and multispectral reflectance indices in monitoring senescence dynamics and accurately identifying key temporal parameters characterizing this phase, comparable to more sophisticated proximal sensing techniques that offer limited throughput. Correlation coefficients of up to 0.8 were observed between multispectral (NDVIred668-index) and visual scoring, respectively 0.9 between RGB (ExGR-index) and visual scoring. Sub-sampling of measurement events demonstrated that the timing and frequency of measurements were highly influential, arguably even more than the choice of sensor. Conclusions: Remote-sensing based proxies derived from both RGB and multispectral sensors can capture the senescence process accurately. The sub-sampling emphasized the importance of timely and frequent assessments, but also highlighted the need for robust methods that enable such frequent assessments to be made under variable environmental conditions. The proposed measurement and data processing strategies can improve the measurement and understanding of senescence dynamics, facilitating adaptive crop breeding strategies in the context of climate change. [ABSTRACT FROM AUTHOR]

Sustainable agriculture is being transformed by drone-IoT integration, improving precision, efficiency, and sustainability. This study examines the pros and downsides of using various technologies to handle connectivity, data management, and power consumption issues. We assess existing integration methods, such as multispectral imaging, real-time IoT monitoring, and machine learning-driven predictive analytics, to gain actionable insights into soil health, crop conditions, and pest control. We also explore regulatory frameworks and technical constraints, including data security and affordability that prevent widespread use. Research shows that drone IoT solutions improve agricultural output, resource consumption, and farm efficiency, but cost and infrastructure hurdles limit availability, especially for smallholder farmers. These findings show that supporting regulatory frameworks and economical technology solutions are needed to increase adoption. Advances in agricultural autonomous decision-making could increase food security and sustainable farming worldwide. [ABSTRACT FROM AUTHOR]

Polarization multispectral imaging has advanced significantly due to its robust information representation capability. Imaging application requires rigorous simulation evaluation and experimental validation using standardized datasets. However, the current full-Stokes polarization multispectral images (FSPMI) dataset, while providing simulation data, is limited by image drift and spectral bands. To overcome these limitations and supplement experimental data, this paper introduces the multi-angle and full-Stokes polarization multispectral images (MAFS-PMI) dataset. The imaging system utilizes a rotatable quarter-wave plate (QWP) and a fixed liquid crystal tunable filter (LCTF) to modulate polarization information. Meanwhile, the LCTF allows switching between multiple spectral bands. The acquired multi-angle polarization multispectral images facilitate the experimental validation of encoding strategies and reconstruction algorithms. Additionally, the derived full-Stokes polarization multispectral images enable the simulation evaluation of imaging methods. The MAFS-PMI dataset involves 73 fast axis angles (0° to 180°), four Stokes parameters, five polarization parameters, 35 spectral bands (520 nm to 690 nm), 400 × 400 pixels, and 12 distinct objects. This dataset offers a valuable resource for developing advanced imaging methods. [ABSTRACT FROM AUTHOR]

This research is focused on developing an AI model that utilizes multispectral camera data from the Souss-Massa region. The model aims to estimate the yield and identify a range of diseases in Argan trees through meticulous on-field investigations. Initially, the work is focused on understanding the resistance of Argan plants against different diseases, based on non-irrigated and irrigated Argan trees as well as planted ones. The results show that disease resistance is high in the case of non-irrigated Argan trees and low in the case of irrigated trees. In addition, we conducted a detailed study of the Argan trees to provide a comprehensive view of the plant's health. Utilizing machine-learning techniques, the yield estimation model suggests that it is possible to achieve up to 97% accuracy in yield estimation, processing data at an impressive rate of 33 images per second. After careful consideration and analysis, we have concluded that machine counting is the most suitable technique for Argan plants. Machine counting and disease detection offer high precision, fast and efficient data processing and cost-effectiveness. Additionally, it is less labour-intensive and can be easily integrated into the existing production process. [ABSTRACT FROM AUTHOR]

The Visible Infrared Imaging Radiometer Suite (VIIRS) is a visible, near and shortwave, to thermal infrared multispectral scanning instrument operational on three polar orbiting satellites, Suomi-NPP, JPSS-1, and JPSS-2. In the present paper, the processing of VIIRS using ACOLITE is introduced, using the Dark Spectrum Fitting (DSF) algorithm for processing of the visible to shortwave infrared bands. ACOLITE now includes support for processing both the imaging (I) and moderate (M) resolution bands at 375 m and 750 m spatial resolution, respectively. In most conditions encountered in the present study, the SWIR bands (either I or M) are automatically selected by the DSF for performing the aerosol correction. The processing is evaluated for turbid water remote sensing via autonomous hyperspectral radiometry from four sites across coastal and estuarine waters: two sites in Belgium and one each in France and Argentina. Through analysis of hundreds of matchups between the satellite and in situ measurements, a generally good performance is found for both I and M bands, especially for bands with the largest water signal, i.e. bands between 490 and 670 nm, where on average relative differences of 10–15% were found. Reflectance biases are generally less than 0.01, with a negative sign in the green and red bands and a positive sign in the blue and NIR bands. Similar matchup results are found for the I and M red and NIR bands, with a slightly higher scatter for the NIR bands. An additional comparison with OCSSW/l2gen processing of the M band data is performed for various configurations. Overall, DSF performance is better in the visible bands, whereas l2gen outputs are more closely aligned with the in situ measurements in the NIR. On average, negative biases are found for all l2gen configurations, up to −0.02 in the blue bands. Using either the SWIR1 + 2 or SWIR1 + 3 bands for the aerosol correction gives the best performance for l2gen processing. For the three VIIRS instruments separately, the average spectral differences with in situ measurements are comparable, with the most important deviation occurring at the Suomi-NPP shortest blue bands, where DSF processing gives a larger positive bias, up to nearly 0.02. For these bands, results from l2gen correspond more closely across the three instruments – although with significant negative biases for all three sensors up to −0.02 – presumably due to the use of system vicarious calibration gains in that processor. An operational network of autonomous hyperspectral instruments provides validation data for any overpassing optical imaging satellite in its commissioning or operational phase and eliminates the need for spectral interpolation or band shifting. In the case of VIIRS specifically, the hyperspectral instruments provide adequate data for the validation of the 20, 40 and 80 nm wide bands. With three operational wide-swath instruments, which provide largely interoperable data, a high frequency of observations is available, especially for study areas at higher latitudes. The novel exploitation of the I bands is now possible, thanks to the free and open source availability of ACOLITE. The advantage of the higher resolution I band data, combined with multiple VIIRS overpasses per day, is demonstrated for mapping turbidity in nearshore regions with high spatial variabilty and for detecting under-resolved floating algae. HIGHLIGHTS: The open-source ACOLITE processor was adapted for VIIRS I (375 m) and M (750 m) data Three operational VIIRS (Suomi-NPP, JPSS-1 and JPSS-2) were processed and validated In situ autonomous hyperspectral radiometry was used for performance evaluation ACOLITE I and M band outputs compared well across hundreds of turbid water matchups Turbidity and FAI product resolution were improved with ACOLITE I bandprocessing [ABSTRACT FROM AUTHOR]

New challenges will be experienced by the agriculture sector in the near future, especially due to the effects of climate change. For example, rising temperatures could result in increased evapotranspiration demand, causing difficulties in the management of irrigation practices. Generally, an important predictor of plant water status to be taken into account for irrigation monitoring and management is the stem water potential. However, it requires a huge amount of time-consuming fieldwork, particularly when an adequate data amount is necessary to fully investigate the spatial and temporal variability of large areas under monitoring. In this study, the integration of machine learning and satellite remote sensing (Sentinel-2) was investigated to obtain a model able to predict the stem water potential in viticulture using multispectral imagery. Vine water status data were acquired within a Montepulciano vineyard in the south of Italy (Puglia region), under semi-arid conditions; data were acquired over two years during the irrigation seasons. Different machine learning algorithms (lasso, ridge, elastic net, and random forest) were compared using vegetation indices and spectral bands as predictors in two independent analyses. The results show that it is possible to remotely estimate vine water status with random forest from vegetation indices (R2 = 0.72). Integrating machine learning techniques and satellite remote sensing could help farmers and technicians manage and plan irrigation, avoiding or reducing fieldwork. [ABSTRACT FROM AUTHOR]

Mixed pixels often hinder accurate cropland mapping from remote sensing images with coarse spatial resolution. Image spatial super-resolution reconstruction technology is widely applied to address this issue, typically transforming coarse-resolution remote sensing images into fine spatial resolution images, which are then used to generate fine-resolution land cover maps using classification techniques. Deep learning has been widely used for image spatial super-resolution reconstruction; however, collecting training samples is often difficult for cropland mapping. Given that the quality of spatial super-resolution reconstruction directly impacts classification accuracy, this study aims to assess the impact of different types of training samples on image spatial super-resolution reconstruction and cropland mapping results by employing a Residual Channel Attention Network (RCAN) model combined with a spatial attention mechanism. Four types of samples were used for spatial super-resolution reconstruction model training, namely fine-resolution images and their corresponding coarse-resolution images, including original Sentinel-2 and degraded Sentinel-2 images, original GF-2 and degraded GF-2 images, histogram-matched GF-2 and degraded GF-2 images, and registered original GF-2 and Sentinel-2 images. The results indicate that the samples acquired by the histogram-matched GF-2 and degraded GF-2 images can resolve spectral band mismatches when simulating training samples from fine spatial resolution imagery, while the other three methods have limitations in their inability to fully address spectral and spatial mismatches. The histogram-matched method yielded the best image quality with PSNR, SSIM, and QNR values of 42.2813, 0.9778, and 0.9872, respectively, and produced the best mapping results, achieving an overall accuracy of 0.9306. By assessing the impact of training samples on image spatial super-resolution reconstruction and classification, this study addresses data limitations and contributes to improving the accuracy of cropland mapping, which is crucial for agricultural management and decision-making. [ABSTRACT FROM AUTHOR]

Pan-sharpening aims to generate high-resolution (HR) multispectral (MS) images by fusing HR panchromatic (PAN) and low-resolution (LR) MS images covering the same area. However, due to the lack of real HR MS reference images, how to accurately evaluate the quality of a fused image without reference is challenging. On the one hand, most methods evaluate the quality of the fused image using the full-reference indices based on the simulated experimental data on the popular Wald's protocol; however, this remains controversial to the full-resolution data fusion. On the other hand, existing limited no reference methods, most of which depend on manually crafted features, cannot fully capture the sensitive spatial/spectral distortions of the fused image. Therefore, this paper proposes a perceptual quality assessment method based on deep feature similarity measure. The proposed network includes spatial/spectral feature extraction and similarity measure (FESM) branch and overall evaluation network. The Siamese FESM branch extracts the spatial and spectral deep features and calculates the similarity of the corresponding pair of deep features to obtain the spatial and spectral feature parameters, and then, the overall evaluation network realizes the overall quality assessment. Moreover, we propose to quantify both the overall precision of all the training samples and the variations among different fusion methods in a batch, thereby enhancing the network's accuracy and robustness. The proposed method was trained and tested on a large subjective evaluation dataset comprising 13,620 fused images. The experimental results suggested the effectiveness and the competitive performance. [ABSTRACT FROM AUTHOR]

Multispectral imaging (MSI) enables the acquisition of spatial and spectral image-based information in one process. Spectral scene information can be used to determine the characteristics of materials based on reflection or absorption and thus their material compositions. This work focuses on so-called multi aperture imaging, which enables a simultaneous capture (snapshot) of spectrally selective and spatially resolved scene information. There are some limiting factors for the spectral resolution when implementing this imaging principle, e.g., usable sensor resolutions and area, and required spatial scene resolution or optical complexity. Careful analysis is therefore needed for the specification of the multispectral system properties and its realisation. In this work we present a systematic approach for the application-related implementation of this kind of MSI. We focus on spectral system modeling, data analysis, and machine learning to build a universally usable multispectral loop to find the best sensor configuration. The approach presented is demonstrated and tested on the classification of waste, a typical application for multispectral imaging. [ABSTRACT FROM AUTHOR]