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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]

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]

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]

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]

Remote measurement of rice crop parameters; Leaf-Chlorophyll, Above-ground biomass, Plant Height, Leaf Moisture and Rice Yield of rice plants before the actual harvest are vital for the early management of rice crops. This study was conducted in controlled rice fields and extended to farmer rice fields in the Maha season in Sri Lanka. The multispectral aerial images of the field were acquired by an Unmanned Ariel Vehicle (UAV) and processed. Vegetation indices (VIs) were then derived and selected the best combination of VIs explaining the respective groundmeasured parameters. The combination of selected VIs showed significant association (R²=0.84, RMSE=0.28; R²=0.81, RMSE=0.06 kg/m2; R²=0.63, RMSE=0.11 cm; R²=0.57, RMSE= 0.16% and R²=0.98, RMSE=128.5 kg/ha, Leaf-Chlorophyll, Above-ground biomass, Plant height, Leaf moisture, and Rice yield respectively) with the ground data in the controlled rice field experiment conducted at the Rice Research and Development Institute (RRDI), Bathalagoda, Sri Lanka. When these selected vegetation indices were transferred to a farmer rice field they showed a relationship as R²=0.57, RMSE=1.35; R²=0.60, RMSE=1.23 kg/m²; R²=0.41, RMSE= 9.95 cm; R²=0.36, RMSE= 34.4% and R²=0.53, RMSE=563.04 kg/ha respectively., The vegetation indices derived from UAV-multispectral images were able to associate and estimate the rice crop parameters like Leaf-Chlorophyll, Above-ground biomass and Rice Yield at booting stage and 25 m flying height. Therefore, VIs developed using UAV multispectral imagery to measure Leaf-Chl, AGB, PHt, LM and RY behave differently across rice fields in different soils, rice varieties, weedy conditions and agronomic practices. [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]

Maize lodging is a prevalent stress that can significantly diminish corn yield and quality. Unmanned aerial vehicles (UAVs) remote sensing is a practical means to quickly obtain lodging information at field scale, such as area, severity, and distribution. However, existing studies primarily use machine learning (ML) methods to qualitatively analyze maize lodging (lodging and non-lodging) or estimate the maize lodging percentage, while there is less research using deep learning (DL) to quantitatively estimate maize lodging parameters (type, severity, and direction). This study aims to introduce advanced DL algorithms into the maize lodging classification task using UAV-multispectral images and investigate the advantages of DL compared with traditional ML methods. This study collected a UAV-multispectral dataset containing non-lodging maize and lodging maize with different lodging types, severities, and directions. Additionally, 22 vegetation indices (VIs) were extracted from multispectral data, followed by spatial aggregation and image cropping. Five ML classifiers and three DL models were trained to classify the maize lodging parameters. Finally, we compared the performance of ML and DL models in evaluating maize lodging parameters. The results indicate that the Random Forest (RF) model outperforms the other four ML algorithms, achieving an overall accuracy (OA) of 89.29% and a Kappa coefficient of 0.8852. However, the maize lodging classification performance of DL models is significantly better than that of ML methods. Specifically, Swin-T performs better than ResNet-50 and ConvNeXt-T, with an OA reaching 96.02% and a Kappa coefficient of 0.9574. This can be attributed to the fact that Swin-T can more effectively extract detailed information that accurately characterizes maize lodging traits from UAV-multispectral data. This study demonstrates that combining DL with UAV-multispectral data enables a more comprehensive understanding of maize lodging type, severity, and direction, which is essential for post-disaster rescue operations and agricultural insurance claims. [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]

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]

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]

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]

Introduction: Effective monitoring and evaluation of floodwaters are essential for disaster prevention and mitigation. The flood inundation range can be obtained by using traditional simulation methods, but these methods still have shortcomings. This work proposes an optimization method for traditional methods. Methods: This study aims to introduce an effective solution for the rapid and accurate extraction of flood inundation areas, emphasizing the enhancement of extraction speed and dynamic monitoring throughout the flood event. The solution uses a normalized difference water index (NDWI), a refined threshold method, and a filtering process for microwave (radar) images. Sentinel-1 SAR (Synthetic Aperture Radar) and Sentinel-2 MSI (Multi-spectral Image) images served as the primary data sources. The Sentinel-2 images were preprocessed to extract pre-flood water bodies, while the Sentinel-1 SAR images were processed using the proposed filtering method to identify post-flood inundation areas. Results: The application and validation of this framework are demonstrated through the case of the 2020 flood event in Tongling, Anhui Province. The framework's performance was validated through comparison with ground truth data, yielding high kappa accuracies of 98% for optical images and 89% for Synthetic Aperture Radar. The findings highlight the framework's ability to capture high-accuracy changes in flood inundation areas and to characterize the dynamic process of flood inundation area changes. Discussion: This study contributes to the field by enhancing the extraction speed and scope of water bodies from SAR images and improving the quality of microwave remote sensing data processing. It offers valuable insights for emergency rapid response and situational awareness in the context of extreme weather events and associated flood disasters. [ABSTRACT FROM AUTHOR]

Oil pollution is the main environmental problem of the Caspian Sea, and a significant contribution to the total oil pollution is made by natural hydrocarbon showings at the seabed. In this paper, we discuss the spatial and temporal variability of the trajectories of natural oil slicks (NOSs) after their emerging to the surface. The study is based on satellite synthetic aperture radar data and data from multispectral satellite sensors in the optical range obtained over 5 years of a survey from 2017 to 2021 in two test areas in the southern part of the Caspian Sea. These areas are a water area near the southwest coast eastward of Cape Sefid Rud (Gilan Province, Iran) and a water area westward of the Cheleken Peninsula, which administratively belongs to Turkmenistan. Natural hydrocarbon seepages at the seabed were discovered in these regions through satellite data. Our main results include the discovery of significant seasonal variability in the NOS distribution directions in both test regions caused by the influence of local winds and surface currents that prevail in different seasons. Various types of NOS distribution trajectories were considered, and assumptions were made on the mechanisms of their formation. The impact of vortex dynamics on the spreading of the NOS and its contribution to the cross-shelf transport of oil pollution was noted. [ABSTRACT FROM AUTHOR]

This study investigates the impact of environmental conditions on reflectance values obtained from multispectral Unmanned Aerial System (UAS) imagery in inland waters, focusing on sun glint, cloud glint, wind-generated waves, and cloud shading projections. Conducted in two reservoirs with differing water qualities, UAS platforms equipped with MicaSense Altum and DJI Phantom 4 Multispectral sensors were used to collect multispectral images. The results show that sun glint significantly increases reflectance variability as solar elevation rises, particularly beyond 54°, compromising data quality. Optimal flight operations should occur within a solar elevation angle range of 25° to 47° to minimize these effects. Cloud shading introduces complex variability, reducing median reflectance. Wind-generated waves enhance sun glint, increasing variability across all spectral bands, while cloud glints amplify reflectance non-uniformly, leading to inconsistent data variability. These findings underscore the need for precise correction techniques and strategic UAS deployment to mitigate environmental interferences. This study offers valuable insights for improving UAS-based monitoring and guiding future research in diverse aquatic environments. [ABSTRACT FROM AUTHOR]

Target monitoring is an important subject in machine vision. Hyperspectral image (HSI) can effectively assist the target detection and recognition effect of traditional optical images because of its rich spectral information. However, limited by pixel mixing, the resolution of HSI is generally lower than that of optical image, which restricts the monitoring distance and accuracy. Therefore, a fusion method of HSI and panchromatic image (PAN) based on coupled spatial-spectral constrained convolution neural network is proposed in this paper to improve the spatial resolution of HSI and reduce the spectral distortion. Through this approach, the linear spectral mixing model and the spatial-spectral transformation constraint model are incorporated into the learning stage of the coupled convolutional neural network, aiming to make full use of the spatial-spectral information of HSI and PAN, and improve the spectral fidelity of fused images. Experiments on several groups of HSI and PAN data sets show that compared with some currently proposed HSI and PAN fusion methods, the proposed approach has better spectral fidelity and lower fusion errors, so as to improve the monitoring distance and accuracy of machine vision in engineering applications and expand the engineering application scenarios. [ABSTRACT FROM AUTHOR]

The editorial in the journal "Remote Sensing" discusses the rapid development of remote sensing technology, particularly in multispectral imaging. The editorial highlights advancements in data processing methods, including the use of drones for data collection and artificial intelligence for analysis. The editorial also summarizes key chapters from the special issue, focusing on topics such as image fusion, data compression, and neural network approaches. The chapters cover a range of practical applications and innovative techniques aimed at improving remote sensing capabilities for various purposes. [Extracted from the article]

Cereal crops play a critical role in global food security, but their productivity is increasingly threatened by climate change. This study evaluates the feasibility of using PlanetScope satellite imagery and a UAV equipped with the MicaSense RedEdge multispectral imaging sensor in monitoring winter wheat under various fertilizer treatments in a Mediterranean climate. Eleven fertilizer treatments, including organic-mineral fertilizer (OMF) pellets, were tested. The results show that conventional inorganic fertilization provided the highest yield (8618 kg ha⁻1), while yields from OMF showed a comparable performance to traditional fertilizers, indicating their potential for sustainable agriculture. PlanetScope data demonstrated moderate accuracy in predicting canopy cover (R2 = 0.68), crop yield (R2 = 0.54), and grain quality parameters such as protein content (R2 = 0.49), starch (R2 = 0.56), and hectoliter weight (R2 = 0.51). However, its coarser resolution limited its ability to capture finer treatment-induced variability. MicaSense, despite its higher spatial resolution, performed poorly in predicting crop components, with R2 values below 0.35 for yield and protein content. This study highlights the complementary use of remote sensing technologies to optimize wheat management and support climate-resilient agriculture through the integration of sustainable fertilization strategies. [ABSTRACT FROM AUTHOR]

Spectral information plays a crucial role in fractional vegetation cover (FVC) estimation, and selecting the appropriate spectral information is essential for improving the accuracy of FVC estimation. Traditionally, spectral feature selection is primarily guided by physical mechanisms or empirical statistical models. This has led to the use of multispectral and hyperspectral images, which often result in missing or redundant information, thereby decreasing the efficiency and accuracy of FVC estimation. This study proposes a novel dual-attention network to select the feature bands of Sentinel-2 multispectral images for the accurate FVC estimation of winter wheat. In the first step, the importance of hyperspectral band reflectances was determined using simulated data from the PROSAIL model, by combining the dual-attention mechanism with the convolutional neural network (DAM-CNN). In the second step, the importance of Sentinel-2 multispectral bands was converted from the hyperspectral band importance identified in the previous stage, and subsequently ranked accordingly. Based on the feature ranking results, multispectral simulated data translated from hyperspectral simulated data were used for CNN training, and multispectral feature selection was conducted based on FVC accuracy. Finally, the selected features were assessed based on their performance in FVC estimation using a CNN model with real data. The experimental results indicate that during the key growth period of winter wheat, the combination of red, green, and red-edge bands significantly influences the FVC estimation accuracy. Band 3 (Green), band 4 (Red), band 5 (Red-edge 1), and band 6 (Red-edge 2) of Sentinel-2 satellite images contribute most significantly to winter wheat FVC estimation, achieving an accuracy comparable to that obtained using all bands, while reducing the training time by 19.1%, as confirmed by field survey data. [ABSTRACT FROM AUTHOR]

Increasing global plastic usage has raised critical concerns regarding marine pollution. This study addresses the pressing issue of floating marine macro-litter (FMML) by developing a novel monitoring system using a multi-spectral sensor and drones along the southern coast of South Korea. Subsequently, a convolutional neural network (CNN) model was utilized to classify four distinct marine litter materials: film, fiber, fragment, and foam. Automatic atmospheric correction with the drone data atmospheric correction (DROACOR) method, which is specifically designed for currently available drone-based sensors, ensured consistent reflectance across altitudes in the FMML dataset. The CNN models exhibited promising performance, with precision, recall, and F1 score values of 0.9, 0.88, and 0.89, respectively. Furthermore, gradient-weighted class activation mapping (Grad-CAM), an object recognition technique, allowed us to interpret the classification performance. Overall, this study will shed light on successful FMML identification using multi-spectral observations for broader applications in diverse marine environments. [ABSTRACT FROM AUTHOR]

Several studies have used aerial images to predict physiological maturity (R8 stage) in soybeans (Glycine max (L.) Merr.). However, information for making predictions in the current growing season using models fitted in previous years is still necessary. Using the Random Forest machine learning algorithm and time series of RGB (red, green, blue) and multispectral images taken from a drone, this work aimed to study, in three breeding experiments of plant rows, how maturity predictions are impacted by a number of factors. These include the type of camera used, the number and time between flights, and whether models fitted with data obtained in one or more environments can be used to make accurate predictions in an independent environment. Applying principal component analysis (PCA), it was found that compared to the full set of 8–10 flights (R2 = 0.91–0.94; RMSE = 1.8–1.3 days), using data from three to five fights before harvest had almost no effect on the prediction error (RMSE increase ~0.1 days). Similar prediction accuracy was achieved using either a multispectral or an affordable RGB camera, and the excess green index (ExG) was found to be the important feature in making predictions. Using a model trained with data from two previous years and using fielding notes from check cultivars planted in the test season, the R8 stage was predicted, in 2020, with an error of 2.1 days. Periodically adjusted models could help soybean breeding programs save time when characterizing the cycle length of thousands of plant rows each season. [ABSTRACT FROM AUTHOR]

Under global climate change, glaciers on the Tibetan Plateau are experiencing severe retreat, which significantly impacts the regional water cycle and the occurrence of natural hazards. However, detailed insights into the spatiotemporal patterns of this retreat and its climatic drivers remain insufficiently explored. In this study, an adaptive glacier extraction index (AGEI) is proposed based on the analysis of multispectral Landsat images integrated with the Google Earth Engine, and comprehensive and high-resolution mapping of glaciers on the Tibetan Plateau is realized at 5-year intervals from 1988 to 2022. Subsequently, the ERA5-Land air temperature and precipitation data are downscaled to a finer 1 km resolution. Finally, the impacts of the annual and seasonal changes in the downscaled meteorological factors on the glacier extent are quantified. Results demonstrate a rapid yet heterogeneous pattern of glacier retreat across the Tibetan Plateau between 1988 and 2022, with retreat rates ranging from 0.14 ± 0.07 % to 0.51 ± 0.09 % annually. A notable trend is observed: most glaciers experienced a decrease in extent from 1990 to 2000 followed by a slight increase from 2000 to 2010. From 2010, a majority of the glaciers exhibited either a static state or minimal retreat. The most pronounced impact of annual temperature on glacier retreat is observed in the southern Himalayas, with a value of - 9.34 × 103 km2°C-1 , and the most restraining impact of precipitation on glacier retreat reaches 261 km2mm-1 , which is observed in the Karakoram Range for the spring season. These insights are pivotal in comprehending the temporal and spatial heterogeneity of glacier retreats and in understanding the effects of climatic variations on the state of glaciers on the Tibetan Plateau. [ABSTRACT FROM AUTHOR]

Multispectral transmission imaging has emerged as a promising technique for imaging breast tissue with high resolution. However, the method encounters challenges such as low grayscale, noisy transmission images with weak signals, primarily due to the strong absorption and scattering of light in breast tissue. A common approach to improve the signal-to-noise ratio (SNR) and overall image quality is frame accumulation. However, factors such as camera jitter and respiratory motion during image acquisition can cause frame misalignment, degrading the quality of the accumulated image. To address these issues, this study proposes a novel image registration method. A hybrid approach combining a genetic algorithm (GA) and a constriction factor-based particle swarm optimization (CPSO), referred to as GA-CPSO, is applied for image registration before frame accumulation. The efficiency of this hybrid method is enhanced by incorporating a squared constriction factor (SCF), which speeds up the registration process and improves convergence towards optimal solutions. The GA identifies potential solutions, which are then refined by CPSO to expedite convergence. This methodology was validated on the sequence of breast frames taken at 600 nm, 620 nm, 670 nm, and 760 nm wavelength of light and proved the enhancement of accuracy by various mathematical assessments. It demonstrated high accuracy (99.93%) and reduced registration time. As a result, the GA-CPSO approach significantly improves the effectiveness of frame accumulation and enhances overall image quality. This study explored the groundwork for precise multispectral transmission image segmentation and classification. [ABSTRACT FROM AUTHOR]

Pan-sharpening aims to generate high-resolution multispectral (HRMS) images by combining high-resolution panchromatic (PAN) images with low-resolution multispectral (LRMS) data, while maintaining the symmetry of spatial and spectral characteristics. Traditional convolutional neural networks (CNNs) struggle with global dependency modeling due to local receptive fields, and Transformer-based models are computationally expensive. Recent Mamba models offer linear complexity and effective global modeling. However, existing Mamba-based methods lack sensitivity to local feature variations, leading to suboptimal fine-detail preservation. To address this, we propose a Conditional Skipping Mamba Network (CSMN), which enhances global-local feature fusion symmetrically through two modules: (1) the Adaptive Mamba Module (AMM), which improves global perception using adaptive spatial-frequency integration; and (2) the Cross-domain Mamba Module (CDMM), optimizing cross-domain spectral-spatial representation. Experimental results on the IKONOS and WorldView-2 datasets demonstrate that CSMN surpasses existing state-of-the-art methods in achieving superior spectral consistency and preserving spatial details, with performance that is more symmetric in fine-detail preservation. [ABSTRACT FROM AUTHOR]

CubeSats have emerged as a promising alternative to satellite missions for studying remote areas where satellite data are scarce and insufficient, such as coastal and marine environments. However, their standard size and weight limitations make integrating remote sensing optical instruments challenging. This work presents the development of Bentayga-I, a CubeSat designed to validate PANDORA, a self-made, lightweight, cost-effective multispectral camera with interchangeable spectral optical filters, in near-space conditions. Its four selected spectral bands are relevant for ocean studies. Alongside the camera, Bentayga-I integrates a power system for short-time operation capacity; a thermal subsystem to maintain battery function; environmental sensors to monitor the CubeSat's internal and external conditions; and a communication subsystem to transmit acquired data to a ground station. The first helium balloon launch with B2Space proved that Bentayga-I electronics worked correctly in near-space environments. During this launch, the spectral capabilities of PANDORA alongside the spectrum were validated using a hyperspectral camera. Its scientific applicability was also tested by capturing images of coastal areas. A second launch is planned to further validate the multispectral camera in a real-world scenario. The integration of Bentayga-I and PANDORA presents promising results for future low-cost CubeSats missions. [ABSTRACT FROM AUTHOR]

Aiming the problems that the classification performance of hyperspectral images in existing classification algorithms is highly dependent on spatial-spectral information and that detailed features are ignored in single convolutional channel feature extraction, resulting in poor generalization performance of the feature extraction model, a multi-scale multi-channel convolutional neural network (MMC-CNN) model is proposed in this paper. First, the data set is divided into two kinds of pixel module, and then different channels are used for feature extraction. A channel-space attention mechanism module is also introduced, and a multi-scale multichannel convolutional neural network (CSAM-MMC) model with the introduction of channel-space attention mechanism is proposed for deeper spatial-spectral feature extraction of hyperspectral image elements while reducing the redundancy of convolutional pooling parameters to achieve better HSI classification. To evaluate the effectiveness of the proposed model, experiments were conducted on Indian Pines, Pavia Center and KSC datasets respectively, and the overall classification accuracies of this paper's MMC-CNN model in the HSI dataset were 97.23%, 98.50%, and 97.85%, thus verifying the model's high feature extraction accuracy. The CSAM-MMC model in this paper further improves 0.13%, 0.35%, and 0.71% relative to the MMC-CNN model, which provides higher overall accuracies relative to other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

In recent years, the agricultural sector has undergone a revolutionary shift toward "smart farming", integrating advanced technologies to strengthen crop health and productivity significantly. This paradigm shift holds profound implications for food safety and the broader economy. At the forefront of this transformation is deep learning, a subset of artificial intelligence based on artificial neural networks, which emerged as a powerful tool in detection and classification tasks. Specifically, Convolutional Neural Networks (CNNs), a specialized type of deep learning and computer vision models, demonstrated remarkable proficiency in analyzing crop imagery, whether sourced from satellites, aircraft, or terrestrial cameras. These networks often leverage vegetation indices and multispectral imagery to enhance their analytical capabilities. Such models contribute to the development of systems that could enhance agricultural outcomes. This review encapsulates the current state of the art in using CNNs in agriculture. It details the image types utilized within this context, including, but not limited to, multispectral images and vegetation indices. Furthermore, it catalogs accessible online datasets pertinent to this field. Collectively, this paper underscores the pivotal role of CNNs in agriculture and highlights the transformative impact of multispectral images in this rapidly evolving domain. [ABSTRACT FROM AUTHOR]

During disaster response, it is very important to obtain the information of collapsed building distribution accurately and quickly. However, limited by some practical factors, existed methods often suffer from the contradiction between the accuracy and efficiency of building damage extraction. This paper proposed a simple and effective framework to rapid recognize collapsed building objects using pre-disaster building distribution maps and post-disaster quasi-panchromatic remote sensing images. The proposed method is validated using several historical disasters in the xBD dataset and tested using three cases of earthquakes in terms of both effectiveness and efficiency. In addition, we have verified that the texture information of optical remote sensing images can be used as the main basis to judge whether a building is collapsed or not, so the panchromatic images are sufficient to enable the deep learning model to correctly recognize collapsed buildings. The experimental results indicate that using quasi-panchromatic images can alleviate the influence of style variations and diverse roof colors present in multi-spectral images on the model's generalization performance, resulting in an average overall accuracy improvement of 2.4%. Additionally, the reduced data volume leads to an improvement in inference efficiency. [ABSTRACT FROM AUTHOR]

Unmanned aircraft vehicles (UAV) are widely used for yield estimations in agricultural production. Many significant improvements have been made towards the usage of hyperspectral and thermal sensors. The practical application of these new techniques meanwhile has been limited by the cost of data collection and the complexities of data processing. The objective of this paper is to evaluate the effectiveness of wheat yield estimations based on integrating vegetation indices (VI), solar radiation and crop height (CH), all of which are characterized by lower cost of data collection and processing. The VIs, solar radiation and CH were calculated based on UAV-based multispectral images obtained from two separate plots in Southern Germany and validated with data from a third plot. We compare the individual and joint predictive performance of different VIs, CH, and solar radiation by contrasting the estimated yield with actual yield based on multiple linear regression and quantile regression. The best predictive power was found for a combined estimation with CH, solar radiation and a Normalized Difference Red-edge Index (R2 = 0.75, RMSE = 0.53). This combined estimation resulted in a 15–20% improvement in the prediction of wheat yield accuracy as compared with utilizing any of the indices separately. [ABSTRACT FROM AUTHOR]

Timely and accurate monitoring of above-ground biomass (AGB) is of great significance for indicating crop growth status, predicting yield, and assessing carbon dynamics. Compared with the traditional time-consuming and laborious method through destructive sampling, UAV remote sensing provides a timely and efficient strategy for estimating biomass. However, the universality of remote sensing retrieval models with multi-feature fusion under different management practices and cultivars are unknown. The spectral, textural, and structural features extracted by UAV multispectral and RGB imaging, coupled with agricultural meteorological parameters, were integrated to estimate the AGB in potato during the whole growth period. Six advanced modeling algorithms, including random forest (RF), partial least squares regression (PLSR), multiple linear regression (MLR), simple linear regression (SLR), ridge regression (RR), and lasso regression (LR) models, were adopted to evaluate the ability of estimating AGB by single feature and multi-feature information fusion. The results indicate the following: (1) The newly proposed variety-dependent indicator growth process ratio (GPR) can improve the model accuracy by over 20%. (2) The fusion of vegetation indices, canopy cover, growing degree days, and GPR achieved higher accuracy to estimate AGB at all growth stages compared with single feature model. (3) RF model performed best for the estimation of AGB during the whole growth period with R2 0.79 and rRMSE 0.24 ton/ha. The study demonstrated that the fusion of multi-feature coupled with the machine learning algorithm achieved the best performance for estimating potato AGB under different management practices and cultivars, which can be a potential and useful phenotyping strategy for estimating AGB at refined plot scale during the whole growth period. [ABSTRACT FROM AUTHOR]

This study investigates radiomic efficacy in post-surgical traumatic spinal cord injury (SCI), overcoming MRI limitations from metal artifacts to enhance diagnosis, severity assessment, and lesion characterization or prognosis and therapy guidance. Traumatic spinal cord injury (SCI) causes severe neurological deficits. While MRI allows qualitative injury evaluation, standard imaging alone has limitations for precise SCI diagnosis, severity stratification, and pathology characterization, which are needed to guide prognosis and therapy. Radiomics enables quantitative tissue phenotyping by extracting a high-dimensional set of descriptive texture features from medical images. However, the efficacy of postoperative radiomic quantification in the presence of metal-induced MRI artifacts from spinal instrumentation has yet to be fully explored. A total of 50 healthy controls and 12 SCI patients post-stabilization surgery underwent 3D multi-spectral MRI. Automated spinal cord segmentation was followed by radiomic feature extraction. Supervised machine learning categorized SCI versus controls, injury severity, and lesion location relative to instrumentation. Radiomics differentiated SCI patients (Matthews correlation coefficient (MCC) 0.97; accuracy 1.0), categorized injury severity (MCC: 0.95; ACC: 0.98), and localized lesions (MCC: 0.85; ACC: 0.90). Combined T1 and T2 features outperformed individual modalities across tasks with gradient boosting models showing the highest efficacy. The radiomic framework achieved excellent performance, differentiating SCI from controls and accurately categorizing injury severity. The ability to reliably quantify SCI severity and localization could potentially inform diagnosis, prognosis, and guide therapy. Further research is warranted to validate radiomic SCI biomarkers and explore clinical integration. [ABSTRACT FROM AUTHOR]

Accurate and rapid estimation of the leaf area index (LAI) is essential for assessing crop growth and nutritional status, guiding farm management, and providing valuable phenotyping data for plant breeding. Compared to the tedious and time-consuming manual measurements of the LAI, remote sensing has emerged as a valuable tool for rapid and accurate estimation of the LAI; however, the empirical inversion modeling methods face challenges of low efficiency for actual LAI measurements and poor model interpretability. The integration of radiative transfer models (RTMs) can overcome these problems to some extent. The aim of this study was to explore the potential of combining the PROSAIL model with high-resolution unmanned aerial vehicle (UAV) multispectral imaging to estimate the LAI across different growth stages at the plot scale. In this study, four inversion strategies for estimating the LAI were tested. Firstly, two types of lookup tables (LUTs) were built to estimate potato LAI of different varieties across different growth stages. Specifically, LUT1 was based on band reflectance, and LUT2 was based on vegetation index. Secondly, the hybrid models combining LUTs generated by PROSAIL and two machine learning algorithms (random forest (RF), Partial Least Squares Regression (PLSR)) are built to estimate potato LAI. The determination of coefficient (R2) of models for estimating LAI by LUTs ranged from 0.24 to 0.64. The hybrid method that integrates UAV multispectral, PROSAIL, and machine learning significantly improved the accuracy of LAI estimation. Compared to the results based on LUT2, the hybrid model achieved higher accuracy with the R2 of the inversion model improved by 30% to 263%. The LAI retrieval model using the PROSAIL model and PLSR achieved an R2 as high as 0.87, while the R2 using the RF algorithm ranged from 0.33 to 0.81. The proposed hybrid model, integrated with UAV multispectral data, PROSAIL, and PLSR can achieve approximate accuracy compared with the empirical inversion models, which can alleviate the labor-intensive process of handheld LAI measurements for building inversion models. Thus, the hybrid approach provides a feasible and efficient strategy for estimating the LAI of potato varieties across different growth stages at the plot scale. [ABSTRACT FROM AUTHOR]

Total nitrogen in soil (STN) serves as a crucial indicator of soil nutrient content and provides an essential nitrogen source necessary for crop growth. Precisely inversion of STN content is crucial for the sustainable management of soil resources and the advancement of agricultural development, particularly to achieve efficient fertilization—reduction in fertilizer usage without compromising yield or increase in yield while maintaining the total fertilization amount. Spectroscopy technology is regarded as an ideal non-destructive method for nutrient detection. However, due to the weak spectral signals of STN and its spatial heterogeneity, hyperspectral imaging technology presents significant potential for high-resolution measurements and precise characterization of STN heterogeneity. In this paper, the STN content was selected as the study subject, and three aspects of soil spectral feature enhancement, multi-source remote sensing data differentiated fusion, and STN content inversion model construction were studied. Therefore, a differentiated fusion of enhanced multispectral image bands (DFE_MSIBs) method combined with Random Forest (RF) algorithms was developed for spectral inversion of STN content. The findings demonstrate the following: 1. The enhanced spectral characteristics and differentiated fusion method not only strengthen the relationship between STN and Sentinel-2A MSI data but also enhance the precision of regional STN inversion models. 2. For the differentiated fusion of enhanced multispectral image bands (DFE_MSIBs) method combined with Random Forest (RF) algorithms, the R2 was 0.95, RMSE was 0.10 g/kg, and LCCC was 0.89. 3. Compared to the unfused model, the average R2 value was increased by 0.02, the average RMSE was decreased by 0.01 g/kg, and the average LCCC was increased by 0.03. These findings hold practical significance for utilizing multi-source remote sensing data in STN mapping and precision fertilization in agricultural fields. [ABSTRACT FROM AUTHOR]