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Due to the difficulty of collecting a sufficient number of photons within a narrow spectral window, hyperspectral images often have low spatial resolution. In order to solve this problem without increasing hardware costs, a large number of image super-resolution algorithms have been proposed and have shown promising results. However, the performance of these algorithms is inconsistent in different datasets. To fix this situation, we proposed a multi-model deep prior regularization method for multispectral and hyperspectral image fusion. This method leverages mechanistic knowledge to combine various super-resolution results into a single algorithm, significantly improving the quality of the hyperspectral images. Specifically, we first proposed a variable residual mixed attention network. This network takes a low-resolution hyperspectral image and a high-resolution multispectral image as inputs, producing deep prior results from three different modes by adjusting spatial and channel attention. Then, we reconstructed the prior results along the spectral dimension and used them as regularization terms in our mechanistic model. We transformed the problem of finding the optimal solution of the model into solving the Sylvester equation and applied a fast solver for this equation to create the high-resolution hyperspectral images. Additionally, we used a genetic algorithm to determine the best hyperparameters for different deep prior results. Through validation on three datasets, we have demonstrated that our method performs better than existing methods in both quantitative and qualitative comparisons. [ABSTRACT FROM AUTHOR]

Due to the powerful ability in capturing the global information, transformer has become an alternative architecture of CNNs for hyperspectral image classification. However, general transformer mainly considers the global spectral information while ignores the multiscale spatial information of the hyperspectral image. In this paper, we propose a multiscale spectral–spatial convolutional transformer (MultiFormer) for hyperspectral image classification. First, the developed method utilizes multiscale spatial patches as tokens to formulate the spatial transformer and generates multiscale spatial representation of each band in each pixel. Second, the spatial representation of all the bands in a given pixel are utilized as tokens to formulate the spectral transformer and generate the multiscale spectral–spatial representation of each pixel. Besides, a modified spectral–spatial CAF module is constructed in the MultiFormer to fuse cross‐layer spectral and spatial information. Therefore, the proposed MultiFormer can capture the multiscale spectral–spatial information and provide better performance than most of other architectures for hyperspectral image classification. Experiments are conducted over commonly used real‐world datasets and the comparison results show the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Seed vigor is an important trait closely related to improved seed quality and long-term germplasm conservation, and it gradually decreases during storage, which has become a major concern for agriculture. However, the underlying regulatory mechanisms of seed vigor loss in terms of genes remain largely unknown in quinoa. Here, two cultivars of quinoa seeds with different storage performance, Longli No.4 (L4) and Longli No.1 (L1), were subjected to transcriptome sequencing to decipher the pathways and genes possibly related to vigor loss under artificial aging. Multispectral imaging features and germination phenotypes showed significantly less seed vigor loss in L1 than in L4, indicating L1 seeds having stronger aging resistance and storability. Totally, 272 and 75 differentially expressed genes (DEGs) were, respectively, identified in L4 and L1 during aging. Transcriptomic analysis further revealed the differences in metabolic pathways, especially, flavonoid biosynthesis, TCA cycle, and terpenoid backbone biosynthesis were significantly enriched in L4 seeds, while carbon metabolism in L1 seeds, which involved key genes such as CHS , CHI , AACT , ENO1 , IDH , NADP-ME , and HAO2L. It indicated that the adverse effects on flavonoids and terpenoids induced by aging might be the significant reasons for more vigor loss in storage sensitive seeds, whereas storage tolerant seeds had a stronger ability to maintain carbon metabolism and energy supply. These findings elucidated the underlying molecular mechanism of seed vigor loss in quinoa, which also provided novel insights into improving seed vigor through modern molecular breeding strategies. [ABSTRACT FROM AUTHOR]

Aim: To use autofluorescence multispectral imaging (AFMI) to develop a non-invasive assay for the in-depth characterisation of human bone marrow derived mesenchymal stromal cells (hBM-MSCs). Methods: hBM-MSCs were imaged by AFMI on gridded dishes, stained for endpoints of interest (STRO-1 positivity, alkaline phosphatase, beta galactosidase, DNA content) then relocated and results correlated. Intensity, texture and morphological features were used to characterise the colour distribution of regions of interest, and canonical discriminant analysis was used to separate groups. Additionally, hBM-MSC lines were cultured to arrest, with AFMI images taken after each passage to investigate whether an assay could be developed for growth potential. Results: STRO-1 positivity could be predicted with a receiver operator characteristic area under the curve (AUC) of 0.67. For spontaneous differentiation this was 0.66, for entry to the cell-cycle it was 0.77 and for senescence it was 0.77. Growth potential (population doublings remaining) was estimated with an RMSPE = 2.296. The Mean Absolute Error of the final prediction model indicated that growth potential could be predicted with an error of ± 1.86 doublings remaining. Conclusions: This non-invasive methodology enabled the in-depth characterisation of hBM-MSCs from a single assay. This approach is advantageous for clinical applications as well as research and stands out for the characterisation of both present status as well as future behaviour. The use of data from five MSC lines with heterogenous AFMI profiles supports potential generalisability. [ABSTRACT FROM AUTHOR]

The amount of high‐resolution agricultural data has increased rapidly in the current decade. Integration of satellite multispectral imagery, combine harvester yield monitoring data, and soil moisture mapping allows managing for within‐field variation and better interpreting on‐farm experimentation. In this study, we investigated the effect of cover crops on yield in Finland by integrating Sentinel‐2 satellite imagery (normalized difference vegetation index), topographic soil moisture indexes, and high‐resolution yield data. The experiment was run by three farmers over 4 years and serves as an example for low‐cost on‐farm experimentation. Our results confirmed earlier findings that undersown cover crops result in approximately 5% yield loss. We also found that the effect is highly variable across farms and within fields. The highest yield losses were found in areas of the field, which were wetter in the spring seeding time. The competition between crop and cover crop could be observed in the vegetation maps for autumn and early summer. Combining NDVI and soil moisture maps allows delineating field zones, which require extra management to reduce the risk of yield loss from cover crop resource competition. Evaluating the overall effect of cover crops on yield would require replication on more farms. The within‐field variation results and workflow investigated in this study can guide placement of sampling areas within those fields. Core Ideas: Cover crop effect on yield varies within fields.Multispectral imaging and topography can find areas where yield loss is likely.Managing for field variability is critical also for cover crops. [ABSTRACT FROM AUTHOR]

The fusion of low-resolution hyperspectral images (LR-HSI) with high-resolution multispectral images (HR-MSI) provides a cost-effective approach to obtaining high-resolution hyperspectral images (HR-HSI). Existing methods primarily based on convolutional neural networks (CNNs) struggle to capture global features and do not adequately address the significant scale and spectral resolution differences between LR-HSI and HR-MSI. To tackle these challenges, our novel FCSwinU network leverages the spectral fast Fourier convolution (SFFC) module for spectral feature extraction and utilizes the Swin Transformer's self-attention mechanism for multi-scale global feature fusion. FCSwinU employs a UNet-like encoder–decoder framework to effectively merge spatiospectral features. The encoder integrates the Swin Transformer feature abstraction module (SwinTFAM) to encode pixel correlations and perform multi-scale transformations, facilitating the adaptive fusion of hyperspectral and multispectral data. The decoder then employs the Swin Transformer feature reconstruction module (SwinTFRM) to reconstruct the fused features, restoring the original image dimensions and ensuring the precise recovery of spatial and spectral details. Experimental results from three benchmark datasets and a real-world dataset robustly validate the superior performance of our method in both visual representation and quantitative assessment compared to existing fusion methods. [ABSTRACT FROM AUTHOR]

In power grid surveying, it is often necessary to fuse panchromatic and multispectral imagery for the design of power lines. Despite the abundance of deep learning networks for fusing these images, the results often suffer from spectral information loss or structural blurring. This study introduces a fusion model specifically tailored for power grid surveying that significantly enhances the representation of spatial–spectral features in remote sensing images. The model comprises three main modules: a TransforRS-Mamba module that integrates the sequence processing capabilities of the Mamba model with the attention mechanism of the Transformer to effectively merge spatial and spectral features; an improved spatial proximity-aware attention mechanism (SPPAM) that utilizes a spatial constraint matrix to greatly enhance the recognition of complex object relationships; and an optimized spatial proximity-constrained gated fusion module (SPCGF) that integrates spatial proximity constraints with residual connections to boost the recognition accuracy of key object features. To validate the effectiveness of the proposed method, extensive comparative and ablation experiments were conducted on GF-2 satellite images and the QuickBird (QB) dataset. Both qualitative and quantitative analyses indicate that our method outperforms 11 existing methods in terms of fusion effectiveness, particularly in reducing spectral distortion and spatial detail loss. However, the model's generalization performance across different data sources and environmental conditions has yet to be evaluated. Future research will explore the integration of various satellite datasets and assess the model's performance in diverse environmental contexts. [ABSTRACT FROM AUTHOR]

Diffractive multispectral optical imaging plays an essential role in optical sensing, which typically suffers from the image blurring problem caused by the spatially variant point spread function. Here, we propose a novel high-quality and efficient hybrid space calibrated 3D network "HSC3D" for spatially variant diffractive multispectral imaging that utilizes the 3D U-Net structure combined with space calibration modules of magnification and rotation effects to achieve high-accuracy eight-channel multispectral restoration. The algorithm combines the advantages of the space calibrated module and U-Net architecture with 3D convolutional layers to improve the image quality of diffractive multispectral imaging without the requirements of complex equipment modifications and large amounts of data. A diffractive multispectral imaging system is established by designing and manufacturing one diffractive lens and four refractive lenses, whose monochromatic aberration is carefully corrected to improve imaging quality. The mean peak signal-to-noise ratio and mean structural similarity index of the reconstructed multispectral images are improved by 3.33 dB and 0.08, respectively, presenting obviously improved image quality compared with a typical Unrolled Network algorithm. The new algorithm with high space calibrated ability and imaging quality has great application potential in diffraction lens spectroscopy and paves a new method for complex practical diffractive multispectral image sensing. [ABSTRACT FROM AUTHOR]

Multispectral pedestrian detection with visible light and infrared images is robust to changes in lighting conditions and therefore is of great importance to numerous applications that require all‐day environmental perception. This paper proposes a novel method named FCE‐RCNN, which integrates saliency detection as a sub‐task and utilizes global information for enhanced feature representation. The approach enhances thermal inputs by incorporating gradients at the raw‐data level before feature extraction. Utilizing a dual‐stream backbone, a global semantic information extraction module is introduced that combines pooling with horizontal–vertical attention mechanisms, capturing high‐quality global semantic information for lower‐level feature enrichment and guidance. Additionally, the pedestrian locality enhancement module is designed to enhance spatial locality information of pedestrians through saliency detection. Furthermore, to alleviate the challenges posed by positional shifts between cross‐spectral features, deformable convolution is innovatively employed. Experimental results on the KAIST dataset demonstrate that FCE‐RCNN significantly improves nighttime detection, achieving a log‐average miss rate of 6.92%, outperforming the new method ICAFusion by 0.93%. These results underscore the effectiveness of FCE‐RCNN, and the method also maintains competitive inference speed, making it suitable for real‐time applications. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) has been extensively applied in remote sensing applications. Nevertheless, it is a challenge to process and interpret SAR images. The key to interpreting SAR images lies in transforming them into other forms of remote sensing images to extract valuable hidden remote sensing information. Currently, the conversion of SAR images to optical images produces low-quality results and incomplete spectral information. To address these problems, an end-to-end network model, S2MS-GAN, is proposed for converting SAR images into multispectral images. In this process, to tackle the issues of noise and image generation quality, a TV-BM3D module is introduced into the generator model. Through TV regularization, block-matching, and 3D filtering, these two modules can preserve the edges and reduce the speckle noise in SAR images. In addition, spectral attention is added to improve the spectral features of the generated MS images. Furthermore, we construct a very high-resolution SAR-to-MS image dataset, S2MS-HR, with a spatial resolution of 0.3 m, which is currently the most comprehensive dataset available for high-resolution SAR-to-MS image interpretation. Finally, a series of experiments are conducted on the relevant dataset. Both quantitative and qualitative evaluations demonstrate that our method outperforms several state-of-the-art models in translation performance. The solution effectively facilitates high-quality transitions of SAR images across different types. [ABSTRACT FROM AUTHOR]

Surface water fraction mapping is an essential preprocessing step for the subpixel mapping (SPM) of surface water, providing valuable prior knowledge about surface water distribution at the subpixel level. In recent years, spectral mixture analysis (SMA) has been extensively applied to estimate surface water fractions in multispectral images by decomposing each mixed pixel into endmembers and their corresponding fractions using linear or nonlinear spectral mixture models. However, challenges emerge when introducing existing surface water fraction mapping methods to hyperspectral images (HSIs) due to insufficient exploration of spectral information. Additionally, inaccurate extraction of endmembers can result in unsatisfactory water fraction estimations. To address these issues, this paper proposes an adaptive unmixing method based on iterative multi-objective optimization for surface water fraction mapping (IMOSWFM) using Zhuhai-1 HSIs. In IMOSWFM, a modified normalized difference water fraction index (MNDWFI) was developed to fully exploit the spectral information. Furthermore, an iterative unmixing framework was adopted to dynamically extract high-quality endmembers and estimate their corresponding water fractions. Experimental results on the Zhuhai-1 HSIs from three test sites around Nanyi Lake indicate that water fraction maps obtained by IMOSWFM are closest to the reference maps compared with the other three SMA-based surface water fraction estimation methods, with the highest overall accuracy (OA) of 91.74%, 93.12%, and 89.73% in terms of pure water extraction and the lowest root-mean-square errors (RMSE) of 0.2506, 0.2403, and 0.2265 in terms of water fraction estimation. This research provides a reference for adapting existing surface water fraction mapping methods to HSIs. [ABSTRACT FROM AUTHOR]

Multispectral and hyperspectral image fusion (MS/HS fusion) aims to generate a high-resolution hyperspectral (HRHS) image by fusing a high-resolution multispectral (HRMS) and a low-resolution hyperspectral (LRHS) images. The deep unfolding-based MS/HS fusion method is a representative deep learning paradigm due to its excellent performance and sufficient interpretability. However, existing deep unfolding-based MS/HS fusion methods only rely on a fixed linear degradation model, which focuses on modeling the relationships between HRHS and HRMS, as well as HRHS and LRHS. In this paper, we break free from this observation model framework and propose a new observation model. Firstly, the proposed observation model is built based on the convolutional sparse coding (CSC) technique, and then a proximal gradient algorithm is designed to solve this model. Secondly, we unfold the iterative algorithm into a deep network, dubbed as MHF-CSCNet, where the proximal operators are learned using convolutional neural networks. Finally, all trainable parameters can be automatically learned end-to-end from the training pairs. Experimental evaluations conducted on various benchmark datasets demonstrate the superiority of our method both quantitatively and qualitatively compared to other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

This study evaluates the effectiveness of multispectral imaging via Unmanned Aerial Systems (UAS), in combination with advanced digital image processing techniques, for the detection and mapping of archaeological sites within diverse landscapes. The research focuses on six case studies located in the northwest of the Iberian Peninsula, a region marked by complex vegetation patterns and varying topography. The primary objective is to assess the potential of these non-invasive remote sensing techniques in identifying crop marks associated with buried structures from ancient, fortified settlements. By means of Principal Component Analysis (PCA) and vegetation indices, the study aims to pinpoint areas of interest that may indicate the presence of archaeological features, while effectively distinguishing them from modern disturbances or natural terrain variations. The research encountered several challenges, including seasonal variations in crop conditions and recent land-use changes. The methodology successfully identified distinct archaeological features. In some instances, natural vegetation variability, typically seen as an obstacle, enhanced the visibility of crop marks, aiding in the detection of underlying structures. These results offer a cost-effective and scalable option for preliminary archaeological surveys, particularly in refining survey methodologies and guiding future excavation efforts aimed at uncovering and preserving ancient, fortified settlements in the region. [ABSTRACT FROM AUTHOR]

Rice farming in Italy accounts for about 50% of the EU's rice area and production. Precision agriculture has entered the scene to enhance sustainability, cut pollution, and ensure food security. Various studies have used remote sensing tools like satellites and drones for multispectral imaging. While Sentinel-2 is highly regarded for precision agriculture, it falls short for specific applications, like at the "Riserva San Massimo" (Gropello Cairoli, Lombardia, Northern Italy) rice farm, where irregularly shaped crops need higher resolution and frequent revisits to deal with cloud cover. A prior study that compared Sentinel-2 and the higher-resolution PlanetScope constellation for vegetative indices found a seasonal miscalibration in the Normalized Difference Vegetation Index (NDVI) and in the Normalized Difference Red Edge Index (NDRE). Dr. Agr. G.N. Rognoni, a seasoned agronomist working with this farm, stresses the importance of studying the radiometric intercalibration between the PlanetScope and Sentinel-2 vegetative indices to leverage the knowledge gained from Sentinel-2 for him to apply variable rate application (VRA). A high-resolution SkySat image, taken almost simultaneously with a pair of Sentinel-2 and PlanetScope images, offered a chance to examine if the irregular distribution of vegetation and barren land within rice fields might be a factor in the observed miscalibration. Using an unsupervised pixel-based image classification technique on SkySat imagery, it is feasible to split rice into two subclasses and intercalibrate them separately. The results indicated that combining histograms and agronomists' expertise could confirm SkySat classification. Moreover, the uneven spatial distribution of rice does not affect the seasonal miscalibration object of past studies, which can be adjusted using the methods described here, even with images taken four days apart: the first method emphasizes accuracy using linear regression, histogram shifting, and histogram matching; whereas the second method is faster and utilizes only histogram matching. [ABSTRACT FROM AUTHOR]

Spectral imaging technique has been widely applied in plant phenotype analysis to improve plant trait selection and genetic advantages. The latest developments and applications of various optical imaging techniques in plant phenotypes were reviewed, and their advantages and applicability were compared. X-ray computed tomography (X-ray CT) and light detection and ranging (LiDAR) are more suitable for the three-dimensional reconstruction of plant surfaces, tissues, and organs. Chlorophyll fluorescence imaging (ChlF) and thermal imaging (TI) can be used to measure the physiological phenotype characteristics of plants. Specific symptoms caused by nutrient deficiency can be detected by hyperspectral and multispectral imaging, LiDAR, and ChlF. Future plant phenotype research based on spectral imaging can be more closely integrated with plant physiological processes. It can more effectively support the research in related disciplines, such as metabolomics and genomics, and focus on micro-scale activities, such as oxygen transport and intercellular chlorophyll transmission. [ABSTRACT FROM AUTHOR]

The synthesis of multiple narrow optical spectral lines, precisely and independently tuned across the near- to mid-infrared region, is a pivotal research area that enables selective and real-time detection of trace gas species within complex gas mixtures. However, existing methods for developing such light sources suffer from limited flexibility and very low pulse energy, particularly in the mid-infrared domain. Here, we introduce a concept that is based on the combination of an appropriate design of near-infrared fiber laser pump and cascaded configuration of gas-filled anti-resonant hollow-core fiber technology. This concept enables the synthesis of multiple independently tunable spectral lines, with >1 μJ high pulse energies and a few nanoseconds pulse width in the near- and mid-infrared regions. The number and wavelengths of the generated spectral lines can be dynamically reconfigured. A proof-of-concept laser beam synthesized of two narrow spectral lines at 3.99 µm and 4.25 µm wavelengths is demonstrated and combined with photoacoustic modality for real-time SO2 and CO2 detection. The proposed concept also constitutes a promising way for infrared multispectral microscopic imaging. Here, the authors develop a concept for the synthesis of multiple narrow and intense optical spectral lines over a broad infrared region. This concept constitutes a promising way for multi-gas detection and multispectral microscopic imaging. [ABSTRACT FROM AUTHOR]

Traditional diagnostic techniques including visual examination, ultrasound (US), and magnetic resonance imaging (MRI) have limitations of in-depth information for the detection of nail disorders, resolution, and practicality. This pilot study, for the first time, evaluates a dual-modality imaging system that combines photoacoustic tomography (PAT) with the US for the multiparametric quantitative assessment of human nail. The study involved a small cohort of five healthy volunteers who underwent PAT/US imaging for acquiring the nail unit data. The PAT/US dual-modality imaging successfully revealed the fine anatomical structures and microvascular distribution within the nail and nail bed. Moreover, this system utilized multispectral PAT to analyze functional tissue parameters, including oxygenated hemoglobin, deoxyhemoglobin, oxygen saturation, and collagen under tourniquet and cold stimulus tests to evaluate changes in the microcirculation of the nail bed. The quantitative analysis of multispectral PAT reconstructed images demonstrated heightened sensitivity in detecting alterations in blood oxygenation levels and collagen content within the nail bed, under simulated different physiological conditions. This pilot study highlights the potential of PAT/US dual-modality imaging as a real-time, noninvasive diagnostic modality for evaluating human nail health and for early detection of nail bed pathologies. [ABSTRACT FROM AUTHOR]

Summary: The application of computer technologies associated with sensors and artificial intelligence (AI) in the quantification and qualification of quality parameters of meat products of various domestic species is an area of research, development, and innovation of great relevance in the agri‐food industry. This review covers the most recent advances in this area, highlighting the importance of computer vision, artificial intelligence, and ultrasonography in evaluating quality and efficiency in meat products' production and monitoring processes. Various techniques and methodologies used to evaluate quality parameters such as colour, water holding capacity (WHC), pH, moisture, texture, and intramuscular fat, among others related to animal origin, breed and handling, are discussed. In addition, the benefits and practical applications of the technology in the meat industry are examined, such as the automation of inspection processes, accurate product classification, traceability, and food safety. While the potential of artificial intelligence associated with sensor development in the meat industry is promising, it is crucial to recognise that this is an evolving field. This technology offers innovative solutions that enable efficient, cost‐effective, and consumer‐oriented production. However, it also underlines the urgent need for further research and development of new techniques and tools such as artificial intelligence algorithms, the development of more sensitive and accurate multispectral sensors, advances in computer vision for 3D image analysis and automated detection, and the integration of advanced ultrasonography with other technologies. Also crucial is the development of autonomous robotic systems for the automation of inspection processes, the implementation of real‐time monitoring systems for traceability and food safety, and the creation of intuitive interfaces for human‐machine interaction. In addition, the automation of sensory analysis and the optimisation of sustainability and energy efficiency are key areas that require immediate attention to address the current challenges in this agri‐food and agri‐industrial sector, highlighting and emphasising the importance of ongoing innovation in the field. [ABSTRACT FROM AUTHOR]

Convolutional Neural Networks (CNN) for ship classification in multi-spectral images (RGB, IR, etc.) is proposed in this paper. Recent developments in deep learning have significantly advanced the field of ship recognition. However, since maritime light intensity is frequently disturbed, multispectral imaging is considered a more robust substitute for RGB imaging. The proposed architectures were fine-tuned after being trained from scratch on the publicly available dataset VAIS (RGB-IR pairs). Unfortunately, the classification results plateaued at 59.74% accuracy, which is unsatisfactory for most real-life applications. Such an accuracy wall was due to the small number of training images. In order to overcome the scarcity of IR ship images, we proposed a novel image data augmentation strategy that translates RGB images to IR. A Pix2Pix model and a Generative Adversarial Network (GAN) network were modified to carry out the generation process as an RGB to IR translator. The KAIST general-purpose RGB-IR image pairs dataset was used to train our RGB-to-IR image translator, whereas the VAIS dataset was held aside for validation purposes. Our proposed network improved the accuracy of the native network by 8% (from 59.74% to 67.74%), which is fairly satisfactory in the field of ship recognition. [ABSTRACT FROM AUTHOR]

Timely and accurate monitoring of typical coastal targets using remote sensing technology is crucial for maintainingmarine ecological stability. Hyperspectral target detection technology proves to be an effective tool in extracting various typical materials along the coastline. Traditional target detection methods using spectral domain information can effectively retain the intrinsic properties of the material. However, it is difficult to effectively recognize targets in homogeneous regions by using only spectral domain information, which may lead to insufficient utilization of spatial information. In this study, a detector based on signal-to-noise ratio fusion constrained energy minimization with low-rank sparse decomposition (SFLRSD) is proposed. This detector improves the separability of background and target by obtaining spatial domain information from hyperspectral images and fusing spectral domain information. First, total variation regularization and fractional Fourier transform are applied to process spatial and spectral domain information, respectively. The constrained energy minimization (CEM) detector is used to improve the separability between the target and background of the processed data. Then, the background and anomalies are represented as low-rank and sparse components, respectively, using low-rank sparse matrix factorization. This transforms the model solution into a covariance matrix problem, which is then solved using marginal distance difference (MDD) to isolate anomalous parts. Subsequently, the anomaly parts are fused with CEM detector results, weighted by their respective signal-to-noise ratios. This detection model leverages unified hyperspectral image features, enhancing spectral discreteness of anomalous targets and backgrounds. Finally, experiments on custom created hyperspectral dataset show that the proposed method outperforms other baseline methods in terms of visualization and quantitative performance. In this paper, we not only propose a new hyperspectral target detection method, but we also collect three typicalmarine litter of different materials bymeans of airborne hyperspectral remote sensing and construct four hyperspectral datasets in a real environment. All the simulation experiments in this paper are conducted in these four datasets. [ABSTRACT FROM AUTHOR]

Accurate shallow-water depth information for island areas is crucial for maritime safety, resource exploration, ecological conservation, and offshore economic activity. Traditional approaches like shipborne sounding and airborne bathymetric light detection and ranging (LiDAR) surveys are expensive, time-consuming, and are limited in politically sensitive regions. Moreover, satellite altimetry-predicted depths exhibit large errors over shallow waters. In contrast, satellite-derived bathymetry (SDB), estimated from multispectral imagery, provides a rapid, open source, and cost-effective technique to fully characterize the bathymetry of a region. Given the scarcity of in-situ water-depth data for the South China Sea (SCS), a shallow-water depth model, HHU24SWDSCS, was developed by integrating 1298 Ice, Cloud, and land Elevation Satellite (ICESat-2) tracks with 70 Sentinel-2 multispectral images. The model covers >120 islands and reefs in the SCS, with a resolution of 10 m. Validation against independent ICESat-2 depth data produced a root mean square error for the model of 0.81–1.35 m (<5 % of the maximum depth), with an average coefficient of determination of 0.91. Validation against independent airborne LiDAR bathymetry data revealed an accuracy of 1.01 m for the Lingyang Reef. Further comparisons with existing bathymetry models revealed the superior performance of the model. While the existing bathymetry models exhibit errors up to tens of meters or larger for island regions, and should therefore be used with caution, the HHU24SWDSCS model exhibited good accuracy in shallow waters across the SCS. This model thus provides a reference for mapping shallow-water depth close to islands and provides fundamental support for research in oceanography, geodesy, and other disciplines. [ABSTRACT FROM AUTHOR]

Timely and accurate monitoring of typical coastal targets using remote sensing technology is crucial for maintainingmarine ecological stability. Hyperspectral target detection technology proves to be an effective tool in extracting various typical materials along the coastline. Traditional target detection methods using spectral domain information can effectively retain the intrinsic properties of the material. However, it is difficult to effectively recognize targets in homogeneous regions by using only spectral domain information, which may lead to insufficient utilization of spatial information. In this study, a detector based on signal-to-noise ratio fusion constrained energy minimization with low-rank sparse decomposition (SFLRSD) is proposed. This detector improves the separability of background and target by obtaining spatial domain information from hyperspectral images and fusing spectral domain information. First, total variation regularization and fractional Fourier transform are applied to process spatial and spectral domain information, respectively. The constrained energy minimization (CEM) detector is used to improve the separability between the target and background of the processed data. Then, the background and anomalies are represented as low-rank and sparse components, respectively, using low-rank sparse matrix factorization. This transforms the model solution into a covariance matrix problem, which is then solved using marginal distance difference (MDD) to isolate anomalous parts. Subsequently, the anomaly parts are fused with CEM detector results, weighted by their respective signal-to-noise ratios. This detection model leverages unified hyperspectral image features, enhancing spectral discreteness of anomalous targets and backgrounds. Finally, experiments on custom created hyperspectral dataset show that the proposed method outperforms other baseline methods in terms of visualization and quantitative performance. In this paper, we not only propose a new hyperspectral target detection method, but we also collect three typicalmarine litter of different materials bymeans of airborne hyperspectral remote sensing and construct four hyperspectral datasets in a real environment. All the simulation experiments in this paper are conducted in these four datasets. [ABSTRACT FROM AUTHOR]

Seed vigor is an important trait closely related to improved seed quality and long-term germplasm conservation, and it gradually decreases during storage, which has become a major concern for agriculture. However, the underlying regulatory mechanisms of seed vigor loss in terms of genes remain largely unknown in quinoa. Here, two cultivars of quinoa seeds with different storage performance, Longli No.4 (L4) and Longli No.1 (L1), were subjected to transcriptome sequencing to decipher the pathways and genes possibly related to vigor loss under artificial aging. Multispectral imaging features and germination phenotypes showed significantly less seed vigor loss in L1 than in L4, indicating L1 seeds having stronger aging resistance and storability. Totally, 272 and 75 differentially expressed genes (DEGs) were, respectively, identified in L4 and L1 during aging. Transcriptomic analysis further revealed the differences in metabolic pathways, especially, flavonoid biosynthesis, TCA cycle, and terpenoid backbone biosynthesis were significantly enriched in L4 seeds, while carbon metabolism in L1 seeds, which involved key genes such as CHS, CHI, AACT, ENO1, IDH, NADP-ME, and HAO2L. It indicated that the adverse effects on flavonoids and terpenoids induced by aging might be the significant reasons for more vigor loss in storage sensitive seeds, whereas storage tolerant seeds had a stronger ability to maintain carbon metabolism and energy supply. These findings elucidated the underlying molecular mechanism of seed vigor loss in quinoa, which also provided novel insights into improving seed vigor through modern molecular breeding strategies. [ABSTRACT FROM AUTHOR]

Close-proximity operations play a crucial role in emerging mission concepts, such as Active Debris Removal or small celestial bodies exploration. When approaching a non-cooperative target, the increased risk of collisions and reduced reliance on ground intervention necessitate autonomous on-board relative pose (position and attitude) estimation. Although navigation strategies relying on monocular cameras which operate in the visible (VIS) spectrum have been extensively studied and tested in flight for navigation applications, their accuracy is heavily related to the target's illumination conditions, thus limiting their applicability range. The novelty of the paper is the introduction of a thermal-infrared (TIR) camera to complement the VIS one to mitigate the aforementioned issues. The primary goal of this work is to evaluate the enhancement in navigation accuracy and robustness by performing VIS-TIR data fusion within an Extended Kalman Filter (EKF) and to assess the performance of such navigation strategy in challenging illumination scenarios. The proposed navigation architecture is tightly coupled, leveraging correspondences between a known uncooperative target and feature points extracted from multispectral images. Furthermore, handover from one camera to the other is introduced to enable seamlessly operations across both spectra while prioritizing the most significant measurement sources. The pipeline is tested on Tango spacecraft synthetically generated VIS and TIR images. A performance assessment is carried out through numerical simulations considering different illumination conditions. Our results demonstrate that a combined VIS-TIR navigation strategy effectively enhances operational robustness and flexibility compared to traditional VIS-only navigation chains. [ABSTRACT FROM AUTHOR]

Environment 3D modeling is critical for the development of future intelligent unmanned systems. This paper proposes a multi-sensor robotic system for environmental geometric-physical modeling and the corresponding data processing methods. The system is primarily equipped with a millimeter-wave cascaded radar and a multispectral camera to acquire the electromagnetic characteristics and material categories of the target environment and simultaneously employs light detection and ranging (LiDAR) and an optical camera to achieve a three-dimensional spatial reconstruction of the environment. Specifically, the millimeter-wave radar sensor adopts a multiple input multiple output (MIMO) array and obtains 3D synthetic aperture radar images through 1D mechanical scanning perpendicular to the array, thereby capturing the electromagnetic properties of the environment. The multispectral camera, equipped with nine channels, provides rich spectral information for material identification and clustering. Additionally, LiDAR is used to obtain a 3D point cloud, combined with the RGB images captured by the optical camera, enabling the construction of a three-dimensional geometric model. By fusing the data from four sensors, a comprehensive geometric-physical model of the environment can be constructed. Experiments conducted in indoor environments demonstrated excellent spatial-geometric-physical reconstruction results. This system can play an important role in various applications, such as environment modeling and planning. [ABSTRACT FROM AUTHOR]

Accurate coffee plant counting is a crucial metric for yield estimation and a key component of precision agriculture. While multispectral UAV technology provides more accurate crop growth data, the varying spectral characteristics of coffee plants across different phenological stages complicate automatic plant counting. This study compared the performance of mainstream YOLO models for coffee detection and segmentation, identifying YOLOv9 as the best-performing model, with it achieving high precision in both detection (P = 89.3%, mAP50 = 94.6%) and segmentation performance (P = 88.9%, mAP50 = 94.8%). Furthermore, we studied various spectral combinations from UAV data and found that RGB was most effective during the flowering stage, while RGN (Red, Green, Near-infrared) was more suitable for non-flowering periods. Based on these findings, we proposed an innovative dual-channel non-maximum suppression method (dual-channel NMS), which merges YOLOv9 detection results from both RGB and RGN data, leveraging the strengths of each spectral combination to enhance detection accuracy and achieving a final counting accuracy of 98.4%. This study highlights the importance of integrating UAV multispectral technology with deep learning for coffee detection and offers new insights for the implementation of precision agriculture. [ABSTRACT FROM AUTHOR]

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

Understanding the molecular mechanisms of differentiation is important for regenerative medicine and developmental biology. This study aims to characterise the role of the glycolysis/oxidative phosphorylation balance as a driver of mesenchymal stem cell (MSC) differentiation. Cells were maintained in normal conditions or stimulated towards the MSC trilineage cell types over 21 days. Multispectral imaging of cell autofluorescence was applied as a non-invasive methodology to continuously image cultures in situ. Spectral signals for collagen, NAD(P)H, and flavins were unmixed. MSCs cultured under chondrogenic conditions exhibited increased collagen levels relative to controls. Following osteogenic induction, MSCs showed increased collagen levels relative to controls during the earlier stages of culture; however, control cells increased their collagen levels as they became confluent. MSCs cultured under adipogenic conditions exhibited lower levels of collagen than controls. The redox ratio (RR; NAD(P)H/flavins) immediately decreased during chondrogenesis, with this early effect persisting throughout the culture compared to control cells, which appeared to increase their RR, similar to osteogenesis. Adipogenesis resulted in a small increase in RR on day 2 relative to control cells, followed by a persistent decrease. Chondrogenic and adipogenic differentiation favoured oxidative phosphorylation, whereas osteogenesis and MSC overgrowth resulted in a glycolytic metabolism. Following consideration of these findings, as well as the diverse reports in the literature, it is concluded that neither enhanced oxidative phosphorylation nor glycolysis are fundamental to the canonical modes of differentiation, and researchers should avoid interpreting shifts as indicating differentiation. [ABSTRACT FROM AUTHOR]

The aftermath of a natural disaster leaves victims trapped in rubble which is challenging to detect by smart drones due to the victims in low visibility under the adverse disaster environments and victims in various sizes. To overcome the above challenges, a transformer fusion-based scale-aware attention network (TFSANet) is proposed to overcome adverse environmental impacts in disaster areas by robustly integrating the latent interactions between RGB and thermal images and to address the problem of various-sized victim detection. Firstly, a transformer fusion model is developed to incorporate a two-stream backbone network to effectively fuse the complementary characteristics between RGB and thermal images. This aims to solve the problem that the victims cannot be seen clearly due to the adverse disaster area, such as smog and heavy rain. In addition, a scale-aware attention mechanism is designed to be embedded into the head network to adaptively adjust the size of receptive fields aiming to capture victims with different scales. Extensive experiments on two challenging datasets indicate that our TFSANet achieves superior results. The proposed method achieves 86.56% average precision (AP) on the National Institute of Informatics—Chiba University (NII-CU) multispectral aerial person detection dataset, outperforming the state-of-the-art approach by 4.38%. On the drone-captured RGBT person detection (RGBTDronePerson) dataset, the proposed method significantly improves the AP of the state-of-the-art approach by 4.33%. [ABSTRACT FROM AUTHOR]

Photoacoustic microscopy (PAM) is a high‐resolution and non‐invasive imaging modality that provides optical absorption contrast. By employing dual‐ or multiple‐wavelength excitation, PAM extends its capabilities to offer valuable spectroscopic information. To achieve efficient multispectral PAM imaging, an essential requirement is a light source characterized by a high repetition rate and switching rate, a ≈microjoule pulse energy, and a ≈nanosecond pulse duration. However, there exists a notable deficiency in suitable light sources, particularly in the near‐infrared‐II window. In this study, a custom‐built all‐fiber‐based light source is reported that provides >3 µJ, 2 ns pulses with a repetition rate of 200 kHz. Digitally addressed semiconductor seed lasers, followed by stimulated Raman scattering amplification, enabled arbitrary sequences of pulses having wavelengths of either 1168.4 or 1202.1 nm. In a switching mode of operation, a 100 kHz switching rate is used to alternate between these wavelengths in even/odd pulses. Furthermore, a high‐resolution multispectral photoacoustic microscopy of three polymer samples is demonstrated with the proposed light source. [ABSTRACT FROM AUTHOR]

Plankton is essential to maintain healthy aquatic ecosystems since it influences the biological carbon pump globally. However, climate change‐induced alterations to oceanic properties threaten planktonic communities. It is therefore crucial to monitor their abundance to assess the health status of marine ecosystems. In situ optical tools unlock high‐resolution measurements of sub‐millimeter specimens, but state‐of‐the‐art underwater imaging techniques are limited to fixed and small close‐range volumes, requiring the instruments to be vertically dived. Here, a novel scanning multispectral confocal light detection and ranging (LiDAR) system for short‐range volumetric sensing in aquatic media is introduced. The system expands the inelastic confocal principle to multiple wavelength channels, allowing the acquisition of 4D point clouds combining near‐diffraction limited morphological and spectroscopic data that is used to train artificial intelligence (AI) models. Volumetric mapping and classification of microplastics is demonstrated to sort them by color and size. Furthermore, in vivo autofluorescence is resolved from a community of free‐swimming zooplankton and microalgae, and accurate spectral identification of different genera is accomplished. The deployment of this photonic platform alongside AI models overcomes the complex and subjective task of manual plankton identification and enables non‐intrusive sensing from fixed vantage points, thus constituting a unique tool for underwater environmental monitoring. [ABSTRACT FROM AUTHOR]

Ancient panel paintings on wood are, with the exception of the mesmerising mummy portraits, extremely rare. However, a small corpus of other types of Romano-Egyptian panel paintings is preserved in collections worldwide. The aim of this study is to explore the technical histories of these rare and intriguing artefacts. We present a comprehensive investigation of three Romano-Egyptian panel paintings from the collections of the Ny Carlsberg Glyptotek, Denmark, including their construction, materials, pigments, binding media, and dating. The panels are examined by various methods of analysis to provide much deeper insights into the materials and techniques used for their production, and to answer questions of chronology and classification. In total, this work offers a more thorough understanding of their function, significance, and original appearance, as well as insights into the art of painting during the Roman period. [ABSTRACT FROM AUTHOR]

Background: Remote sensing based on multispectral imaging may be useful for detecting vegetation stress responses in agriculture. Objectives: To evaluate the potential of orbital multispectral imaging in discriminating the most effective strategies for reducing plant-parasitic nematode populations, thereby preventing yield losses in coffee production. Methods: Coffee plants were treated with eleven treatments, including Bacillus spp. isolates, commercial biological products, commercial chemical nematicides, and water (control group). Initial and final nematode populations in the soil were quantified, and surface reflectance data were collected using the Planet orbital multispectral sensor. The data were classified using the random tree algorithm. Results: The population of plant-parasitic nematodes was reduced by 35.90% and 55.13% following the application of B. amyloliquefaciens isolate B266 and B. subtilis isolate B33, respectively. Under the conditions of this experiment, multispectral imaging accurately discriminated the most nematicidal treatments, with a global accuracy of 80%. Conclusions: Orbital multispectral imaging can discriminate the most effective treatments used for nematode management in coffee plants, highlighting its potential as a supportive tool in agriculture. [ABSTRACT FROM AUTHOR]

This study assesses the potential of using multispectral images collected by an unmanned aerial system (UAS) on machine learning (ML) frameworks to estimate cereal rye (Secale cereal L.) biomass. Multispectral images and ground-truth cereal rye biomass data were collected from 15 farmers' fields up to three times between March and May in northwest Ohio. Images were processed to derive 13 vegetation indices (VIs). Out of 13 VIs, six optimal sets of VIs, including excess green (ExG), normalized green red difference index (NGRDI), soil adjusted vegetation index (SAVI), blue green ratio (B_G_ratio), red-edge triangular vegetation index (RTVI), and normalized difference red-edge (NDRE) were selected using the variance inflation factor (VIF) based feature selection approach. Six regression models including a multiple linear regression (MLR), elastic net (ENET), multivariate adaptive regression splines (MARS), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGB) were investigated for estimation of cereal rye biomass based on the VIs. For most of the models, the six selected VIs performed better than or similar to the full set of 13 VIs with R2 ranging from 0.24 to 0.59 and RMSE ranging from 83.13 to 91.89 g/m2 during 10-fold cross-validation. During independent accuracy assessment with the selected set of VIs, XGB exhibited the highest R2 (0.67) and lowest RMSE (83.13 g/m2) and MAE (48.13 g/m2) followed by RF and ENET. For all the models, the agreement between observed and predicted biomass was high for biomass less than or equal to 200 g/m2 but decreased for biomass greater than 200 g/m2. When field-collected structural features were integrated with the selected VIs, the models showed improved performance, with R2 and RMSE of the models reaching up to 0.82 and 61.67 g/m2 respectively. Among the six VIs, SAVI showed the strongest impact on the model prediction for the best-performing RF and XGB regression models. The findings of this study demonstrate the potential of precisely estimating and mapping cereal rye biomass based on UAS-captured multispectral images. Timely information on cover crop growth can facilitate numerous decision-making processes, including planning the planting operations, and management of nutrients, weeds, and soil moisture to improve agronomic and environmental outcomes. [ABSTRACT FROM AUTHOR]

Seagrasses are in danger due to anthropogenic activities causing a reduction in seagrass area and global damage. It is crucial to map seagrass distribution, as there is limited information on its existence. Sentinel-2A satellite provides high-resolution multispectral data with a 10-m resolution. The study aimed to evaluate the ability of Sentinel-2A imagery from the Google Earth Engine (GEE) platform to classify with the random forest (RF) algorithm in mapping seagrass in shallow water and various other objects in the study area. The results showed the area’s presence of seagrass, coral, sand, sand seagrass, and rubble. Also, the photo transect method was used for collecting field data. The random forest algorithm had an accuracy of 76% in classifying each of the five classes. The combination of Sentinel-2A imagery and random forest algorithms can provide insight into the status and distribution of seagrass. [ABSTRACT FROM AUTHOR]

Multispectral remote sensing images contain abundant information about the distribution and reflectance of ground objects, playing a crucial role in target detection, environmental monitoring, and resource exploration. However, due to the complexity of the imaging process in multispectral remote sensing, image blur is inevitable, and the blur kernel is typically unknown. In recent years, many researchers have focused on blind image deblurring, but most of these methods are based on single-band images. When applied to CASEarth satellite multispectral images, the spectral correlation is unutilized. To address this limitation, this paper proposes a novel approach that leverages the characteristics of multispectral data more effectively. We introduce an inter-band gradient similarity prior and incorporate it into the patch-wise minimal pixel (PMP)-based deblurring model. This approach aims to utilize the spectral correlation across bands to improve deblurring performance. A solution algorithm is established by combining the half-quadratic splitting method with alternating minimization. Subjectively, the final experiments on CASEarth multispectral images demonstrate that the proposed method offers good visual effects while enhancing edge sharpness. Objectively, our method leads to an average improvement in point sharpness by a factor of 1.6, an increase in edge strength level by a factor of 1.17, and an enhancement in RMS contrast by a factor of 1.11. [ABSTRACT FROM AUTHOR]

Subpixel water area mapping based on spatial information processing at superpixel scale (SISS) for multispectral imagery is proposed in this paper. In SISS, the superpixels with irregular shape are first obtained by segmenting the first principal component of the improved imagery which is improved from the the original coarse multispectral imagery by using bicubic interpolation; The random walk algorithm is then introduced to calculate these superpixels to obtain the spatial information. Finally, according to this spatial information, the label of the water area or the non-water area is assigned to each subpixel by the class allocation method, producing the final mapping result. The experimental results on two datasets show that the proposed method can achieve the best performance in both visual effects and quantitative indicators. [ABSTRACT FROM AUTHOR]

Rapid advancement in satellite communication over the last decade have resulted in the widespread use of remote sensing images. Additionally, as satellite image transmission over the Internet has increased, secrecy concerns have also arisen. As a result, digitally transmitted images must have great imperceptibility and confidentiality. Multispectral images consist of multiple bands. It is very challenging to select the important spectral band for watermarking so that the structural and visual quality of the satellite Image can be retained. This work proposes an innovative blind watermarking model based on a hybrid optimization strategy performed with the following two processes: the embedding process and the extraction process. A novel hybrid optimization named FBIAO algorithm, which is the amalgamation of Archimedes Optimization (ArchOA) and Forensic Based Investigation Optimization (FBIO) algorithm is used to select spectral band for watermarking. The proposed novel FBIAO enhances the balances between the exploration and exploitation, boosts the solution diversity and improves the convergence of FBI based optimization for spectral band selection. The 3-level Discrete Wavelet Transform (DWT) is used to embed the watermark logo in the selected spectral band image and then position selection is applied to identify the location for embedding the watermark. Further, the watermark image is scrambled using Arnold Map technique to avoid the correlation between image pixel. The proposed method provides a peak signal-to-noise ratio (PSNR) in the range of 35.57 dB to 36.80 dB and, a structural similarity index (SSIM) between 0.91 to 0.93 without attack for six sample datasets. It provides robustness for different attacks and offers SSIM in between 0.6 to 0.87 and normalized Correlation (NC) in between 0.8 to 0.91 which is superior over traditional techniques. [ABSTRACT FROM AUTHOR]

Precise classification of Light Detection and Ranging (LiDAR) point cloud is a fundamental process in various applications, such as land cover mapping, forestry management, and autonomous driving. Due to the lack of spectral information, the existing research on single wavelength LiDAR classification is limited. Spectral information from images could address this limitation, but data fusion suffers from varying illumination conditions and the registration problem. A novel multispectral LiDAR successfully obtains spatial and spectral information as a brand-new data type, namely, multispectral point cloud, thereby improving classification performance. However, spatial and spectral information of multispectral LiDAR has been processed separately in previous studies, thereby possibly limiting the classification performance of multispectral LiDAR. To explore the potential of this new data type, the current spatial–spectral classification framework for multispectral LiDAR that includes four steps: (1) neighborhood selection, (2) feature extraction and selection, (3) classification, and (4) label smoothing. Three novel highlights were proposed in this spatial – spectral classification framework. (1) We improved the popular eigen entropy-based neighborhood selection by spectral angle match to extract a more precise neighborhood. (2) We evaluated the importance of geometric and spectral features to compare their contributions and selected the most important features to reduce feature redundancy. (3) We conducted spatial label smoothing by a conditional random field, accounting for the spatial and spectral information of the neighborhood points. The proposed method demonstrated by a multispectral LiDAR with three channels: 466 nm (blue), 527 nm (green), and 628 nm (red). Experimental results demonstrate the effectiveness of the proposed spatial – spectral classification framework. Moreover, this research takes advantages of the complementation of spatial and spectral information, which could benefit more precise neighborhood selection, more effective features, and satisfactory refinement of classification result. Finally, this study could serve as an inspiration for future efficient spatial–spectral process for multispectral point cloud. [ABSTRACT FROM AUTHOR]

Mural paintings hold significant historical information and possess substantial artistic and cultural value. However, murals are inevitably damaged by natural environmental factors such as wind and sunlight, as well as by human activities. For this reason, the study of damaged areas is crucial for mural restoration. These damaged regions differ significantly from undamaged areas and can be considered abnormal targets. Traditional manual visual processing lacks strong characterization capabilities and is prone to omissions and false detections. Hyperspectral imaging can reflect the material properties more effectively than visual characterization methods. Thus, this study employs hyperspectral imaging to obtain mural information and proposes a mural anomaly detection algorithm based on a hyperspectral multi-scale residual attention network (HM-MRANet). The innovations of this paper include: (1) Constructing mural painting hyperspectral datasets. (2) Proposing a multi-scale residual spectral-spatial feature extraction module based on a 3D CNN (Convolutional Neural Networks) network to better capture multi-scale information and improve performance on small-sample hyperspectral datasets. (3) Proposing the Enhanced Residual Attention Module (ERAM) to address the feature redundancy problem, enhance the network’s feature discrimination ability, and further improve abnormal area detection accuracy. The experimental results show that the AUC (Area Under Curve), Specificity, and Accuracy of this paper’s algorithm reach 85.42%, 88.84%, and 87.65%, respectively, on this dataset. These results represent improvements of 3.07%, 1.11% and 2.68% compared to the SSRN algorithm, demonstrating the effectiveness of this method for mural anomaly detection. [ABSTRACT FROM AUTHOR]

To reveal the effects of illuminance and spectral band changes on coal and gangue image and spectral parameters, and to provide fundamental data for using multispectral images in coal and gangue recognition research, this paper analyzes image parameters (standard deviation, skewness, entropy, and contrast) and the spectral parameter using experimental methods, and compares the differences between coal and gangue parameter values. First, we analyze the variations of parameters, and find that as illuminance increases, the image parameters fluctuate in shorter spectral bands but change slightly in longer ones, while the spectral parameter increases across all bands; as wavelength increases, the image parameters rise or fluctuate within 546–622 nm, while the spectral parameter increases; in other bands, all parameters initially decrease before stabilizing. Second, we compare parameter deviation by normalizing coal and gangue values, and find that coal and gangue have the largest deviation in the spectral parameter, and the smallest in skewness; they have the largest fluctuation range in entropy, and the smallest in skewness; image parameters are more sensitive to changes in illuminance and spectral bands than the spectral parameter; and the deviations in both image and spectral parameters are larger at 7000lux and 585 nm than at other settings. [ABSTRACT FROM AUTHOR]

The prompt acquisition of precise land cover categorization data is indispensable for the strategic development of contemporary farming practices, especially within the realm of forestry oversight and preservation. Forests are complex ecosystems that require precise monitoring to assess their health, biodiversity, and response to environmental changes. The existing methods for classifying remotely sensed imagery often encounter challenges due to the intricate spacing of feature classes, intraclass diversity, and interclass similarity, which can lead to weak perceptual ability, insufficient feature expression, and a lack of distinction when classifying forested areas at various scales. In this study, we introduce the DASR-Net algorithm, which integrates a dual attention network (DAN) in parallel with the Residual Network (ResNet) to enhance land cover classification, specifically focusing on improving the classification of forested regions. The dual attention mechanism within DASR-Net is designed to address the complexities inherent in forested landscapes by effectively capturing multiscale semantic information. This is achieved through multiscale null attention, which allows for the detailed examination of forest structures across different scales, and channel attention, which assigns weights to each channel to enhance feature expression using an improved BSE-ResNet bilinear approach. The two-channel parallel architecture of DASR-Net is particularly adept at resolving structural differences within forested areas, thereby avoiding information loss and the excessive fusion of features that can occur with traditional methods. This results in a more discriminative classification of remote sensing imagery, which is essential for accurate forest monitoring and management. To assess the efficacy of DASR-Net, we carried out tests with 10m Sentinel-2 multispectral remote sensing images over the Heshan District, which is renowned for its varied forestry. The findings reveal that the DASR-Net algorithm attains an accuracy rate of 96.36%, outperforming classical neural network models and the transformer (ViT) model. This demonstrates the scientific robustness and promise of the DASR-Net model in assisting with automatic object recognition for precise forest classification. Furthermore, we emphasize the relevance of our proposed model to hyperspectral datasets, which are frequently utilized in agricultural and forest classification tasks. DASR-Net's enhanced feature extraction and classification capabilities are particularly advantageous for hyperspectral data, where the rich spectral information can be effectively harnessed to differentiate between various forest types and conditions. By doing so, DASR-Net contributes to advancing remote sensing applications in forest monitoring, supporting sustainable forestry practices and environmental conservation efforts. The findings of this study have significant practical implications for urban forestry management. The DASR-Net algorithm can enhance the accuracy of forest cover classification, aiding urban planners in better understanding and monitoring the status of urban forests. This, in turn, facilitates the development of effective forest conservation and restoration strategies, promoting the sustainable development of the urban ecological environment. [ABSTRACT FROM AUTHOR]

Rising food demands require new techniques to achieve higher genetic gains for crop production, especially in regions where climate can negatively affect agriculture. Wheat is a staple crop that often encounters this challenge, and ideotype breeding with optimized canopy traits for grain yield, such as determinate tillering, synchronized flowering, and stay-green (SG), can potentially improve yield under terminal drought conditions. Among these traits, SG has emerged as a key factor for improving grain quality and yield by prolonging photosynthetic activity during reproductive stages. This study aims to highlight the importance of growth dynamics in a wheat mapping population by using multispectral images obtained from uncrewed aerial vehicles as a high-throughput phenotyping technique to assess the effectiveness of using such images for determining correlations between vegetation indices and grain yield, particularly regarding the SG trait. Results show that the determinate group exhibited a positive correlation between NDVI and grain yield, indicating the effectiveness of these traits in yield improvement. In contrast, the indeterminate group, characterized by excessive vegetative growth, showed no significant NDVI–grain yield relationship, suggesting that NDVI values in this group were influenced by sterile tillers rather than contributing to yield. These findings provide valuable insights for crop breeders by offering a non-destructive approach to enhancing genetic gains through the improved selection of resilient wheat genotypes. [ABSTRACT FROM AUTHOR]

Accurate and timely estimation of oat biomass is crucial for the development of sustainable and efficient agricultural practices. This research focused on estimating and predicting forage oat biomass using UAV and agronomic variables. A Matrice 300 equipped with a multispectral camera was used for 14 flights, capturing 21 spectral indices per flight. Concurrently, agronomic data were collected at six stages synchronized with UAV flights. Data analysis involved correlations and Principal Component Analysis (PCA) to identify significant variables. Predictive models for forage biomass were developed using various machine learning techniques: linear regression, Random Forests (RFs), Support Vector Machines (SVMs), and Neural Networks (NNs). The Random Forest model showed the best performance, with a coefficient of determination R2 of 0.52 on the test set, followed by Support Vector Machines with an R2 of 0.50. Differences in root mean square error (RMSE) and mean absolute error (MAE) among the models highlighted variations in prediction accuracy. This study underscores the effectiveness of photogrammetry, UAV, and machine learning in estimating forage biomass, demonstrating that the proposed approach can provide relatively accurate estimations for this purpose. [ABSTRACT FROM AUTHOR]

Single-tree segmentation on multispectral UAV images shows significant potential for effective forest management such as automating forest inventories or detecting damage and diseases when using an additional classifier. We propose an automated workflow for segmentation on high-resolution data and provide our trained models in a Toolbox for ArcGIS Pro on our GitHub repository for other researchers. The database used for this study consists of multispectral UAV data (RGB, NIR and red edge bands) of a forest area in Germany consisting of a mix of tree species consisting of five deciduous trees and three conifer tree species in the matured closed canopy stage at approximately 90 years. Information of NIR and Red Edge bands are evaluated for tree segmentation using different vegetation indices (VIs) in comparison to only using RGB information. We trained Faster R-CNN, Mask R-CNN, TensorMask and SAM in several experiments and evaluated model performance on different data combinations. All models with the exception of SAM show good performance on our test data with the Faster R-CNN model trained on the red and green bands and the Normalized Difference Red Edge Index (NDRE) achieving best results with an F1-Score of 83.5% and an Intersection over Union of 65.3% on highly detailed labels. All models are provided in our TreeSeg toolbox and allow the user to apply the pre-trained models on new data. [ABSTRACT FROM AUTHOR]

Floods are significant global disasters, but their impact in developing countries is greater due to the lower shock tolerance, many subsistence farmers, land fragmentation, poor adaptation strategies, and low technical capacity, which worsen food security and livelihoods. Therefore, accurate and timely monitoring of flooded crop areas is crucial for both disaster impact assessments and adaptation strategies. However, most existing methods for monitoring flooded crops using remote sensing focus solely on estimating the flood damage, neglecting the need for adaptation decisions. To address these issues, we have developed an approach to mapping flooded rice fields using Earth observation and machine learning. This approach integrates high-resolution multispectral satellite images with Sentinel-1 data. We have demonstrated the reliability and applicability of this approach by using a manually labelled dataset related to a devastating flood event in north-western Nigeria. Additionally, we have developed a land suitability model to evaluate potential areas for paddy rice cultivation. Our crop extent and land use/land cover classifications achieved an overall accuracy of between 93% and 95%, while our flood mapping achieved an overall accuracy of 99%. Our findings indicate that the flood event caused damage to almost 60% of the paddy rice fields. Based on the land suitability assessment, our results indicate that more land is suitable for cultivation during natural floods than is currently being used. We propose several recommendations as adaptation measures for stakeholders to improve livelihoods and mitigate flood disasters. This study highlights the importance of integrating multispectral and synthetic aperture radar (SAR) data for flood crop mapping using machine learning. Decision-makers will benefit from the flood crop mapping framework developed in this study in a number of spatial planning applications. [ABSTRACT FROM AUTHOR]

Pansharpening refers to enhancing the spatial resolution of multispectral images through panchromatic images while preserving their spectral features. However, existing traditional methods or deep learning methods always have certain distortions in the spatial or spectral dimensions. This paper proposes a remote sensing spatial–spectral fusion method based on a multi-scale dual-stream convolutional neural network, which includes feature extraction, feature fusion, and image reconstruction modules for each scale. In terms of feature fusion, we propose a multi cascade module to better fuse image features. We also design a new loss function aim at enhancing the high degree of consistency between fused images and reference images in terms of spatial details and spectral information. To validate its effectiveness, we conduct thorough experimental analyses on two widely used remote sensing datasets: GeoEye-1 and Ikonos. Compared with the nine leading pansharpening techniques, the proposed method demonstrates superior performance in multiple key evaluation metrics. [ABSTRACT FROM AUTHOR]

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