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The detection and monitoring of burned areas is crucial for vegetation recovery, loss assessment, and anomaly analysis. Although vegetation indices (VIs) have been widely used, accurate vegetation detection is challenging due to potential confusion in the spectra of different types of land cover and the interference of shadow effects caused by terrain. In this work, a novel Vegetation Anomaly Spectral Texture Index (VASTI) is proposed, which leverages the merits of both spectral and spatial texture features to identify abnormal pixels for extracting burned vegetation areas. The performance of the VASTI and its components, the Global Environmental Monitoring Index (GEMI), the Enhanced Vegetation Index (EVI), and the texture feature Autocorrelation (AC) were assessed based on a global dataset previously established, which contains 1774 pairs of samples from 10 different sites. The results illustrated that, compared with the GEMI and EVI, the VASTI improved the user's accuracy (UA), producer's accuracy (PA), and kappa coefficient across the ten study areas by approximately 5% to 10%. Compared to AC, the VASTI improved the accuracy of abnormal vegetation detection by 13% to 25%. The improvements were mainly caused by the fact that the incorporation of texture features can reduce spectral confusion between pixels. The innovation of the VASTI is that it considers the relationship between anomalous pixels and surrounding pixels by explicitly integrating spatial texture features with traditional spectral features. [ABSTRACT FROM AUTHOR]

Date palm (Phoenix dactylifera L.) fruit samples belonging to the 'Mejhoul' and 'Boufeggous' cultivars were harvested at the Tamar stage and used in our experiments. Before scanning, date samples were dried using convective drying at 60 °C and infrared drying at 60 °C with a frequency of 50 Hz, and then they were scanned. The scanning trials were performed for two hundred date palm fruit in fresh, convective-dried, and infrared-dried forms of each cultivar using a flatbed scanner. The image-texture parameters of date fruit were extracted from images converted to individual color channels in RGB, Lab, XYZ, and UVS color models. The models to classify fresh and dried samples were developed based on selected image textures using machine learning algorithms belonging to the groups of Bayes, Trees, Lazy, Functions, and Meta. For both the 'Mejhoul' and 'Boufeggous' cultivars, models built using Random Forest from the group of Trees turned out to be accurate and successful. The average classification accuracy for fresh, convective-dried, and infrared-dried 'Mejhoul' reached 99.33%, whereas fresh, convective-dried, and infrared-dried samples of 'Boufeggous' were distinguished with an average accuracy of 94.33%. In the case of both cultivars and each model, the higher correctness of discrimination was between fresh and infrared-dried samples, whereas the highest number of misclassified cases occurred between fresh and convective-dried fruit. Thus, the developed procedure may be considered an innovative approach to the non-destructive assessment of drying impact on the external quality characteristics of date palm fruit. [ABSTRACT FROM AUTHOR]

Ultrasound B-scan images are traditionally formed from the envelope of the received radiofrequency echoes, but the image texture is dominated by granular speckle patterns. Longstanding efforts at speckle reduction and deconvolution have been developed to lessen the detrimental aspects of speckle. However, we now propose an alternative approach to estimation (and image rendering) of the underlying fine grain scattering density of tissues based on power law constraints. The key steps are a whitening of the spectrum of the received signal while conforming to the original envelope shape and statistics, followed by a power law filtering in accordance with the known scattering behavior of tissues. This multiple step approach results in a high-spatial-resolution map of scattering density that is constrained by the most important properties of scattering from tissues. Examples from in vivo liver scans are shown to illustrate the change in image properties from this framework. [ABSTRACT FROM AUTHOR]

The differences in sensor imaging mechanisms, observation angles, and scattering characteristics of terrestrial objects significantly limit the registration performance of synthetic aperture radar (SAR) and optical heterologous images. Traditional methods particularly struggle in weak feature regions, such as harbors and islands with substantial water coverage, as well as in desolate areas like deserts. This paper introduces a robust heterologous image registration technique based on region-adaptive keypoint selection that integrates image texture features, targeting two pivotal aspects: feature point extraction and matching point screening. Initially, a dual threshold criterion based on block region information entropy and variance products effectively identifies weak feature regions. Subsequently, it constructs feature descriptors to generate similarity maps, combining histogram parameter skewness with non-maximum suppression (NMS) to enhance matching point accuracy. Extensive experiments have been conducted on conventional SAR-optical datasets and typical SAR-optical images with different weak feature regions to assess the method's performance. The findings indicate that this method successfully removes outliers in weak feature regions and completes the registration task of SAR and optical images with weak feature regions. [ABSTRACT FROM AUTHOR]

The loop closure detection (LCD) methods in Unmanned Aerial Vehicle (UAV) Visual Inertial Navigation System (VINS) are often affected by issues such as insufficient image texture information and limited observational perspectives, resulting in constrained UAV positioning accuracy and reduced capability to perform complex tasks. This study proposes a Bag-of-Words (BoW) LCD method based on Neural Radiance Field (NeRF), which estimates camera poses from existing images and achieves rapid scene reconstruction through NeRF. A method is designed to select virtual viewpoints and render images along the flight trajectory using a specific sampling approach to expand the limited observational angles, mitigating the impact of image blur and insufficient texture information at specific viewpoints while enlarging the loop closure candidate frames to improve the accuracy and success rate of LCD. Additionally, a BoW vector construction method that incorporates the importance of similar visual words and an adapted virtual image filtering and comprehensive scoring calculation method are designed to determine loop closures. Applied to VINS-Mono and ORB-SLAM3, and compared with the advanced BoW model LCDs of the two systems, results indicate that the NeRF-based BoW LCD method can detect more than 48% additional accurate loop closures, while the system's navigation positioning error mean is reduced by over 46%, validating the effectiveness and superiority of the proposed method and demonstrating its significant importance for improving the navigation accuracy of VINS. [ABSTRACT FROM AUTHOR]

The image texture features obtained from 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) images of non-small cell lung cancer (NSCLC) have revealed tumor heterogeneity. A combination of genomic data and radiomics may improve the prediction of tumor prognosis. This study aimed to predict NSCLC metastasis using a graph neural network (GNN) obtained by combining a protein–protein interaction (PPI) network based on gene expression data and image texture features. 18F-FDG PET/CT images and RNA sequencing data of 93 patients with NSCLC were acquired from The Cancer Imaging Archive. Image texture features were extracted from 18F-FDG PET/CT images and area under the curve receiver operating characteristic curve (AUC) of each image feature was calculated. Weighted gene co-expression network analysis (WGCNA) was used to construct gene modules, followed by functional enrichment analysis and identification of differentially expressed genes. The PPI of each gene module and genes belonging to metastasis-related processes were converted via a graph attention network. Images and genomic features were concatenated. The GNN model using PPI modules from WGCNA and metastasis-related functions combined with image texture features was evaluated quantitatively. Fifty-five image texture features were extracted from 18F-FDG PET/CT, and radiomic features were selected based on AUC (n = 10). Eighty-six gene modules were clustered by WGCNA. Genes (n = 19) enriched in the metastasis-related pathways were filtered using DEG analysis. The accuracy of the PPI network, derived from WGCNA modules and metastasis-related genes, improved from 0.4795 to 0.5830 (p < 2.75 × 10−12). Integrating PPI of four metastasis-related genes with 18F-FDG PET/CT image features in a GNN model elevated its accuracy over a without image feature model to 0.8545 (95% CI = 0.8401–0.8689, p-value < 0.02). This model demonstrated significant enhancement compared to the model using PPI and 18F-FDG PET/CT derived from WGCNA (p-value < 0.02), underscoring the critical role of metastasis-related genes in prediction model. The enhanced predictive capability of the lymph node metastasis prediction GNN model for NSCLC, achieved through the integration of comprehensive image features with genomic data, demonstrates promise for clinical implementation. [ABSTRACT FROM AUTHOR]

Since Neural Radiation Field (NeRF) was first proposed, a large number of studies dedicated to them have emerged. These fields achieved very good results in their respective contexts, but they are not sufficiently practical for our project. If we want to obtain novel images of satellites photographed in space by another satellite, we must face problems like inaccurate camera focal lengths and poor image texture. There are also some small structures on satellites that NeRF-like algorithms cannot render well. In these cases, the NeRF's performance cannot sufficiently meet the project's needs. In fact, the images rendered by the NeRF will have many incomplete structures, while the MipNeRF will blur the edges of the structures on the satellite and obtain unrealistic colors. In response to these problems, we proposed STs-NeRF, which improves the quality of the new perspective images through an encoding module and a new network structure. We found a method for calculating poses that are suitable for our dataset and that enhance the network's input learning effect by recoding the sampling points and viewing directions through a dynamic encoding (DE) module. Then, we input them into our layer-by-layer normalized multi-layer perceptron (LLNMLP). By simultaneously inputting points and directions into the network, we avoid the mutual influence between light rays, and through layer-by-layer normalization, we ease the model's overfitting from a training perspective. Since real images should not be made public, we created a synthetic dataset and conducted a series of experiments. The experiments showed that our method achieves the best results in reconstructing captured satellite images, compared with the NeRF, the MipNeRF, the NeuS and the NeRF2Mesh, and improves the Peak Signal-to-Noise Ratio (PSNR) by 19%. We have also tested on public datasets, and our NeRF can still render acceptable images on datasets with better textures. [ABSTRACT FROM AUTHOR]

Pasture degradation poses significant economic, social, and environmental impacts in the Brazilian savanna ecosystem. Despite these impacts, effectively detecting varying intensities of agronomic and biological degradation through remote sensing remains challenging. This study explores the potential of the eight-band PlanetScope SuperDove satellite constellation to discriminate between five classes of pasture degradation: non-degraded pasture (NDP); pastures with low- (LID) and moderate-intensity degradation (MID); severe agronomic degradation (SAD); and severe biological degradation (SBD). Using a set of 259 cloud-free images acquired in 2022 across five sites located in central Brazil, the study aims to: (i) identify the most suitable period for discriminating between various degradation classes; (ii) evaluate the Random Forest (RF) classification performance of different SuperDove attributes; and (iii) compare metrics of accuracy derived from two predicted scenarios of pasture degradation: a more challenging one involving five classes (NDP, LID, MID, SAD, and SBD), and another considering only non-degraded and severely degraded pastures (NDP, SAD, and SBD). The study assessed individual and combined sets of SuperDove attributes, including band reflectance, vegetation indices, endmember fractions from spectral mixture analysis (SMA), and image texture variables from Gray-level Co-occurrence Matrix (GLCM). The results highlighted the effectiveness of the transition from the rainy to the dry season and the period towards the beginning of a new seasonal rainy cycle in October for discriminating pasture degradation. In comparison to the dry season, more favorable discrimination scenarios were observed during the rainy season. In the dry season, increased amounts of non-photosynthetic vegetation (NPV) complicate the differentiation between NDP and SBD, which is characterized by high soil exposure. Pastures exhibiting severe biological degradation showed greater sensitivity to water stress, manifesting earlier reflectance changes in the visible and near-infrared bands of SuperDove compared to other classes. Reflectance-based classification yielded higher overall accuracy (OA) than the approaches using endmember fractions, vegetation indices, or texture metrics. Classifications using combined attributes achieved an OA of 0.69 and 0.88 for the five-class and three-class scenarios, respectively. In the five-class scenario, the highest F1-scores were observed for NDP (0.61) and classes of agronomic (0.71) and biological (0.88) degradation, indicating the challenges in separating low and moderate stages of pasture degradation. An initial comparison of RF classification results for the five categories of degraded pastures, utilizing reflectance data from MultiSpectral Instrument (MSI)/Sentinel-2 (400–2500 nm) and SuperDove (400–900 nm), demonstrated an enhanced OA (0.79 versus 0.66) with Sentinel-2 data. This enhancement is likely to be attributed to the inclusion of shortwave infrared (SWIR) spectral bands in the data analysis. Our findings highlight the potential of satellite constellation data, acquired at high spatial resolution, for remote identification of pasture degradation. [ABSTRACT FROM AUTHOR]

Faced with the problem of incompatibility between traditional information acquisition mode and spaceborne earth observation tasks, starting from the general mathematical model of compressed sensing, a theoretical model of block compressed sensing was established, and a full-process adaptive coding and decoding compressed sensing framework for remote sensing images was proposed, which includes five parts: mode selection, feature factor extraction, adaptive shape segmentation, adaptive sampling rate allocation and image reconstruction. Unlike previous semi-adaptive or local adaptive methods, the advantages of the adaptive encoding and decoding method proposed in this paper are mainly reflected in four aspects: (1) Ability to select encoding modes based on image content, and maximizing the use of the richness of the image to select appropriate sampling methods; (2) Capable of utilizing image texture details for adaptive segmentation, effectively separating complex and smooth regions; (3) Being able to detect the sparsity of encoding blocks and adaptively allocate sampling rates to fully explore the compressibility of images; (4) The reconstruction matrix can be adaptively selected based on the size of the encoding block to alleviate block artifacts caused by non-stationary characteristics of the image. Experimental results show that the method proposed in this article has good stability for remote sensing images with complex edge textures, with the peak signal-to-noise ratio and structural similarity remaining above 35 dB and 0.8. Moreover, especially for ocean images with relatively simple image content, when the sampling rate is 0.26, the peak signal-to-noise ratio reaches 50.8 dB, and the structural similarity is 0.99. In addition, the recovered images have the smallest BRISQUE value, with better clarity and less distortion. In the subjective aspect, the reconstructed image has clear edge details and good reconstruction effect, while the block effect is effectively suppressed. The framework designed in this paper is superior to similar algorithms in both subjective visual and objective evaluation indexes, which is of great significance for alleviating the incompatibility between traditional information acquisition methods and satellite-borne earth observation missions. [ABSTRACT FROM AUTHOR]

When an unmanned surface vehicle (USV) navigates in narrow waterway scenarios, its ability to detect vanishing points accurately and quickly is highly important for safeguarding its navigation safety and realizing automated navigation. We propose a novel approach for detecting vanishing points based on an improved lightweight AlexNet. First, a similarity evaluation calculation method based on image texture features is proposed, by which some scenarios are selected from the filtered Google Street Road Dataset (GSRD). These filtered scenarios, together with the USV Inland Dataset (USVID), compose the training dataset, which is manually labeled according to a non-uniformly distributed grid level. Next, the classical AlexNet was adjusted and optimized by constructing sequential connections of four convolutional layers and four pooling layers and incorporating the Inception A and Inception C structures in the first two convolutional layers. During model training, we formulate vanishing point detection as a classification problem using an output layer with 225 discrete possible vanishing point locations. Finally, we compare and analyze the labeled vanishing point with the detected vanishing point. The experimental results show that the accuracy of our method and the state-of-the-art algorithmic vanishing point detector improves, indicating that our improved lightweight AlexNet can be applied in narrow waterway navigation scenarios and can provide a technical reference for autonomous navigation of USVs. [ABSTRACT FROM AUTHOR]

In the modern times of technological development, it is important to select adequate methods to support various food and industrial problems, including innovative techniques with the help of artificial intelligence (AI). Effective analysis and the speed of algorithm implementation are key points in assessing the quality of food products. Non-invasive solutions are being sought to achieve high accuracy in the classification and evaluation of various food products. This paper presents various machine learning algorithm architectures to evaluate the efficiency of identifying blackcurrant powders (i.e., blackcurrant concentrate with a density of 67 °Brix and a color coefficient of 2.352 (E520/E420) in combination with the selected carrier) based on information encoded in microscopic images acquired via scanning electron microscopy (SEM). Recognition of blackcurrant powders was performed using texture feature extraction from images aided by the gray-level co-occurrence matrix (GLCM). It was evaluated for quality using individual single classifiers and a metaclassifier based on metrics such as accuracy, precision, recall, and F1-score. The research showed that the metaclassifier, as well as a single random forest (RF) classifier most effectively identified blackcurrant powders based on image texture features. This indicates that ensembles of classifiers in machine learning is an alternative approach to demonstrate better performance than the existing traditional solutions with single neural models. In the future, such solutions could be an important tool to support the assessment of the quality of food products in real time. Moreover, ensembles of classifiers can be used for faster analysis to determine the selection of an adequate machine learning algorithm for a given problem. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Systems (UASs) are increasingly vital in precision agriculture, offering detailed, real-time insights into plant health across multiple spectral domains. However, this technology's precision in estimating plant traits associated with Nitrogen Use Efficiency (NUE), and the factors affecting this precision, are not well-documented. This review examines the capabilities of UASs in assessing NUE in crops. Our analysis specifically highlights how different growth stages critically influence NUE and biomass assessments in crops and reveals a significant impact of specific signal processing techniques and sensor types on the accuracy of remote sensing data. Optimized flight parameters and precise sensor calibration are underscored as key for ensuring the reliability and validity of collected data. Additionally, the review delves into how different canopy structures, like planophile and erect leaf orientations, uniquely influence spectral data interpretation. The study also recognizes the untapped potential of image texture features in UAV-based remote sensing for detailed analysis of canopy micro-architecture. Overall, this research not only underscores the transformative impact of UAS technology on agricultural productivity and sustainability but also demonstrates its potential in providing more accurate and comprehensive insights for effective crop health and nutrient management strategies. [ABSTRACT FROM AUTHOR]

Current image feature extraction methods fail to adapt to the fine features of apple image texture, resulting in image matching errors and degraded image processing accuracy. A multi-view orthogonal image acquisition system was constructed with apples as the research object. The system consists of four industrial cameras placed around the apple at different angles and one camera placed on top. Following the image acquisition through the system, synthetic image pairs—both before and after transformation—were generated as the input dataset. This generation process involved each image being subjected to random transformations. Through learning to extract more distinctive and descriptive features, the deep learning-based keypoint detection method surpasses traditional techniques by broadening the application range and enhancing detection accuracy. Therefore, a lightweight network called ALIKE-APPLE was proposed for surface feature point detection. The baseline model for ALIKE-APPLE is ALIKE, upon which improvements have been made to the image feature encoder and feature aggregation modules. It comprises an Improved Convolutional Attention Module (ICBAM) and a Boosting Resolution Sampling Module (BRSM). The proposed ICBAM replaced max pooling in the original image feature encoder for downsampling. It enhanced the feature fusion capability of the model by utilizing spatial contextual information and learning region associations in the image. The proposed BRSM replaced the bilinear interpolation in the original feature aggregator for upsampling, overcoming the apple side image's geometric distortion and effectively preserving the texture details and edge information. The model size was shrunk by optimizing the number of downsampling operations from the image encoder of the original model. The experimental results showed that the average number of observed keypoints and the average matching accuracy were improved by 166.41% and 37.07%, respectively, compared with the baseline model. The feature detection model of ALIKE-APPLE was found to perform better than the optimal SuperPoint. The feature point distribution of ALIKE-APPLE showed an improvement of 10.29% in average standard deviation (Std), 8.62% in average coefficient of variation (CV), and 156.12% in average feature point density (AFPD). Moreover, the mean matching accuracy (MMA) of ALIKE-APPLE improved by 125.97%. Thus, ALIKE-APPLE boasts a more consistent allocation of feature points and greater precision in matching. [ABSTRACT FROM AUTHOR]

To obtain timely, accurate, and reliable information on wheat yield dynamics. The UAV DJI Wizard 4-multispectral version was utilized to acquire multispectral images of winter wheat during the tasseling, grouting, and ripening periods, and to manually acquire ground yield data. Sixteen vegetation indices were screened by correlation analysis, and eight textural features were extracted from five single bands in three fertility periods. Subsequently, models for estimating winter wheat yield were developed utilizing multiple linear regression (MLR), partial least squares (PLS), BP neural network (BPNN), and random forest regression (RF), respectively. (1) The results indicated a consistent correlation between the two variable types and yield across various fertility periods. This correlation consistently followed a sequence: heading period > filling period > mature stage. (2) The model's accuracy improves significantly when incorporating both texture features and vegetation indices for estimation, surpassing the accuracy achieved through the estimation of a single variable type. (3) Among the various models considered, the partial least squares (PLS) model integrating texture features and vegetation indices exhibited the highest accuracy in estimating winter wheat yield. It achieved a coefficient of determination (R2) of 0.852, a root mean square error (RMSE) of 74.469 kg·hm−2, and a normalized root mean square error (NRMSE) of 7.41%. This study validates the significance of utilizing image texture features along with vegetation indices to enhance the accuracy of models estimating winter wheat yield. It demonstrates that UAV multispectral images can effectively establish a yield estimation model. Combining vegetation indices and texture features results in a more accurate and predictive model compared to using a single index. [ABSTRACT FROM AUTHOR]

Recently, explainability in machine and deep learning has become an important area in the field of research as well as interest, both due to the increasing use of artificial intelligence (AI) methods and understanding of the decisions made by models. The explainability of artificial intelligence (XAI) is due to the increasing consciousness in, among other things, data mining, error elimination, and learning performance by various AI algorithms. Moreover, XAI will allow the decisions made by models in problems to be more transparent as well as effective. In this study, models from the 'glass box' group of Decision Tree, among others, and the 'black box' group of Random Forest, among others, were proposed to understand the identification of selected types of currant powders. The learning process of these models was carried out to determine accuracy indicators such as accuracy, precision, recall, and F1-score. It was visualized using Local Interpretable Model Agnostic Explanations (LIMEs) to predict the effectiveness of identifying specific types of blackcurrant powders based on texture descriptors such as entropy, contrast, correlation, dissimilarity, and homogeneity. Bagging (Bagging_100), Decision Tree (DT0), and Random Forest (RF7_gini) proved to be the most effective models in the framework of currant powder interpretability. The measures of classifier performance in terms of accuracy, precision, recall, and F1-score for Bagging_100, respectively, reached values of approximately 0.979. In comparison, DT0 reached values of 0.968, 0.972, 0.968, and 0.969, and RF7_gini reached values of 0.963, 0.964, 0.963, and 0.963. These models achieved classifier performance measures of greater than 96%. In the future, XAI using agnostic models can be an additional important tool to help analyze data, including food products, even online. [ABSTRACT FROM AUTHOR]

The ecosystems within the Qilian Mountain National Nature Reserve (QMNNR) and its surrounding areas have been significantly affected by changes in climate and land use, which have, in turn, constrained the region's socio-economic development. This study investigates the regional characteristics and application requirements of the ecological environment in the arid and semi-arid zones of the reserve. In view of the saturated characteristics of NDVI in the reserve and the high-altitude saline-alkali environmental conditions, this study proposed a Modified Remote Sensing Ecology Index (MRSEI) by introducing the kernel NDVI and comprehensive salinity index (CSI). This approach enhances the applicability of the remote sensing ecological index. The temporal and spatial dynamics of ecological and environmental quality within the QMNNR from 2000 to 2022 were quantitatively assessed using the MRSEI. The effect of land use on ecological quality was quantified by analyzing the MRSEI contribution rate. The findings in this paper indicate that (1) in arid and semi-arid regions, the MRSEI provides a more precise representation of surface ecological environmental quality compared to the remote sensing ecological index (RSEI). The high correlation (R2 = 0.908) and significant difference between MRSEI and RSEI demonstrate that MRSEI enhances the accuracy of evaluating ecological environmental quality. The impact of land use on ecological quality was quantitatively assessed by analyzing the contribution rate of the MRSEI. (2) The ecological quality of the QMNNR exhibited an upward trend from 2000 to 2022, with an increase rate of 1.3 × 10−3 y−1. The area characterized by improved ecological and environmental quality constitutes approximately 53.68% of the total area. Conversely, the ecological quality of the degraded areas accounts for roughly 28.77%. (3) Among the various land use types, the improvement in ecological environmental quality within the reserve is primarily attributed to the expansion of forest and grassland areas, along with a reduction in unused land. Forest and grassland types account for over 90% of the total area classified with "good" and "excellent" ecological grades, whereas unused land types represent more than 44% of the total area classified with "poor" ecological grades. Overall, this study provides a valuable framework for analyzing ecological and environmental changes in arid and semi-arid regions. [ABSTRACT FROM AUTHOR]

Populus euphratica is a unique constructive tree species within riparian desert areas that is essential for maintaining oasis ecosystem stability. The Tarim River Basin contains the most densely distributed population of P. euphratica forests in the world, and obtaining accurate distribution data in the mainstream of the Tarim River would provide important support for its protection and restoration. We propose a new method for automatically extracting P. euphratica using Sentinel-1 and 2 and Landsat-8 images based on the Google Earth Engine cloud platform and the random forest algorithm. A mask of the potential distribution area of P. euphratica was created based on prior knowledge to save computational resources. The NDVI (Normalized Difference Vegetation Index) time series was then reconstructed using the preferred filtering method to obtain phenological parameter features, and the random forest model was input by combining the phenological parameter, spectral index, textural, and backscattering features. An active learning method was employed to optimize the model and obtain the best model for extracting P. euphratica. Finally, the map of natural P. euphratica forests with a resolution of 10 m in the mainstream of the Tarim River was obtained. The overall accuracy, producer's accuracy, user's accuracy, kappa coefficient, and F1-score of the map were 0.96, 0.98, 0.95, 0.93, and 0.96, respectively. The comparison experiments showed that simultaneously adding backscattering and textural features improved the P. euphratica extraction accuracy, while textural features alone resulted in a poor extraction effect. The method developed in this study fully considered the prior and posteriori information and determined the feature set suitable for the P. euphratica identification task, which can be used to quickly obtain accurate large-area distribution data of P. euphratica. The method can also provide a reference for identifying other typical desert vegetation. [ABSTRACT FROM AUTHOR]

Monitoring aircraft using synthetic aperture radar (SAR) images is a very important task. Given its coherent imaging characteristics, there is a large amount of speckle interference in the image. This phenomenon leads to the scattering information of aircraft targets being masked in SAR images, which is easily confused with background scattering points. Therefore, automatic detection of aircraft targets in SAR images remains a challenging task. For this task, this paper proposes a framework for speckle reduction preprocessing of SAR images, followed by the use of an improved deep learning method to detect aircraft in SAR images. Firstly, to improve the problem of introducing artifacts or excessive smoothing in speckle reduction using total variation (TV) methods, this paper proposes a new nonconvex total variation (NTV) method. This method aims to ensure the effectiveness of speckle reduction while preserving the original scattering information as much as possible. Next, we present a framework for aircraft detection based on You Only Look Once v8 (YOLOv8) for SAR images. Therefore, the complete framework is called SAR-NTV-YOLOv8. Meanwhile, a high-resolution small target feature head is proposed to mitigate the impact of scale changes and loss of depth feature details on detection accuracy. Then, an efficient multi-scale attention module was proposed, aimed at effectively establishing short-term and long-term dependencies between feature grouping and multi-scale structures. In addition, the progressive feature pyramid network was chosen to avoid information loss or degradation in multi-level transmission during the bottom-up feature extraction process in Backbone. Sufficient comparative experiments, speckle reduction experiments, and ablation experiments are conducted on the SAR-Aircraft-1.0 and SADD datasets. The results have demonstrated the effectiveness of SAR-NTV-YOLOv8, which has the most advanced performance compared to other mainstream algorithms. [ABSTRACT FROM AUTHOR]

Visible and infrared image fusion is a strategy that effectively extracts and fuses information from different sources. However, most existing methods largely neglect the issue of lighting imbalance, which makes the same fusion models inapplicable to different scenes. Several methods obtain low-level features from visible and infrared images at an early stage of input or shallow feature extraction. However, these methods do not explore how low-level features provide a foundation for recognizing and utilizing the complementarity and common information between the two types of images. As a result, the complementarity and common information between the images is not fully analyzed and discussed. To address these issues, we propose a Self-Attention Progressive Network for the fusion of infrared and visible images in this paper. Firstly, we construct a Lighting-Aware Sub-Network to analyze lighting distribution, and introduce intensity loss to measure the probability of scene illumination. This approach enhances the model's adaptability to lighting conditions. Secondly, we introduce self-attention learning to design a multi-state joint feature extraction module (MSJFEM) that fully utilizes the contextual information among input keys. It guides the learning of a dynamic attention matrix to strengthen the capacity for visual representation. Finally, we design a Difference-Aware Propagation Module (DAPM) to extract and integrate edge details from the source images while supplementing differential information. The experiments across three benchmark datasets reveal that the proposed approach exhibits satisfactory performance compared to existing methods. [ABSTRACT FROM AUTHOR]

The purpose of this study was to evaluate the predictive features of baseline F-18-fluorodeoxy-D-glucose positron emission tomography (18F-FDG PET)/computed tomography (CT) parameters in patients with dedifferentiated liposarcomas (DDLPSs) and well-differentiated liposarcomas (WDLPSs) receiving systemic treatment. A total of 24 patients with liposarcoma who underwent longitudinal 18F-FDG PET/CT in systemic therapy were included. All volumetric segmentation of each tumor section and semiquantitative imaging parameters were extracted from the axial field of view from both PET and CT images. Maximum, mean, and minimum standardized uptake values (SUVmax, SUVmean, and SUVmin), Hounsfield units (HUs), and their respective changes from baseline and posttreatment were calculated. The voxel values from unenhanced CT images were correlated with PET-derived parameters. The 18F-FDG uptake decreased by more than 56% on average in responders for both SUVmax and SUVmean in DDLPS. There was a decrease in HUmax in DDLPS among responders. Using AUC > 0.8 as a reasonable predictor, we found that the ratios of SUVmaxD/HUmean, SUVmaxD/HUmedian, and SUVmeanD/HUmedian at baseline were significant indicators of the response to treatment in patients with liposarcoma. The changes in SUVmean and not just SUVmax parameters could be considered as accurate tumor response indicators. For the first time, we introduced baseline SUV/HU ratios as a valuable diagnostic tool in predicting liposarcoma treatment outcomes. This ability was not revealed by classic semiquantitative PET or CT parameters at baseline. [ABSTRACT FROM AUTHOR]

Defect detection is a crucial part of the pipeline in many industries. In the textile industry, it is especially important, as it will affect the quality and price of the final product. However, it is mostly performed by human agents, who have been reported to have poor performance, along with requiring a costly and time-consuming training process. As such, methods to automate the process have been increasingly explored throughout the last 20 years. While there are many traditional approaches to this problem, with the advent of deep learning, machine learning-based approaches now constitute the majority of all possible approaches. Other articles have explored traditional approaches and machine learning approaches in a more general way, detailing their evolution over time. In this review, we summarize the most important advancements in the last 5 years and focus mostly on machine learning-based approaches. We also outline the most promising avenues of research in the future. [ABSTRACT FROM AUTHOR]

Speech-driven lip synchronization is a crucial technology for generating realistic facial animations, with broad application prospects in virtual reality, education, training, and other fields. However, existing methods still face challenges in generating high-fidelity facial animations, particularly in addressing lip jitter and facial motion instability issues in continuous frame sequences. This study presents VividWav2Lip, an improved speech-driven lip synchronization model. Our model incorporates three key innovations: a cross-attention mechanism for enhanced audio-visual feature fusion, an optimized network structure with Squeeze-and-Excitation (SE) residual blocks, and the integration of the CodeFormer facial restoration network for post-processing. Extensive experiments were conducted on a diverse dataset comprising multiple languages and facial types. Quantitative evaluations demonstrate that VividWav2Lip outperforms the baseline Wav2Lip model by 5% in lip sync accuracy and image generation quality, with even more significant improvements over other mainstream methods. In subjective assessments, 85% of participants perceived VividWav2Lip-generated animations as more realistic compared to those produced by existing techniques. Additional experiments reveal our model's robust cross-lingual performance, maintaining consistent quality even for languages not included in the training set. This study not only advances the theoretical foundations of audio-driven lip synchronization but also offers a practical solution for high-fidelity, multilingual dynamic face generation, with potential applications spanning virtual assistants, video dubbing, and personalized content creation. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) are effective tools for identifying road anomalies with limited detection coverage due to the discrete spatial distribution of roads. Despite computational, storage, and transmission challenges, existing detection algorithms can be improved to support this task with robustness and efficiency. In this study, the K-means clustering algorithm was used to calculate the best prior anchor boxes; Faster R-CNN (region-based convolutional neural network), YOLOX-s (You Only Look Once version X-small), YOLOv5-s, YOLOv7-tiny, YOLO-MobileNet, and YOLO-RDD models were built based on image data collected by UAVs. YOLO-MobileNet has the most lightweight model but performed worst in accuracy, but greatly reduces detection accuracy. YOLO-RDD (road distress detection) performed best with a mean average precision (mAP) of 0.701 above the Intersection over Union (IoU) value of 0.5 and achieved relatively high accuracy in detecting all four types of distress. The YOLO-RDD model most successfully detected potholes with an AP of 0.790. Significant or severe distresses were better identified, and minor cracks were relatively poorly identified. The YOLO-RDD model achieved an 85% computational reduction compared to YOLOv7-tiny while maintaining high detection accuracy. [ABSTRACT FROM AUTHOR]

Heat stress impacts ruminant livestock production on varied levels in this alarming climate breakdown scenario. The drastic effects of the global climate change-associated heat stress in ruminant livestock demands constructive evaluation of animal performance bordering on effective monitoring systems. In this climate-smart digital age, adoption of advanced and developing Artificial Intelligence (AI) technologies is gaining traction for efficient heat stress management. AI has widely penetrated the climate sensitive ruminant livestock sector due to its promising and plausible scope in assessing production risks and the climate resilience of ruminant livestock. Significant improvement has been achieved alongside the adoption of novel AI algorithms to evaluate the performance of ruminant livestock. These AI-powered tools have the robustness and competence to expand the evaluation of animal performance and help in minimising the production losses associated with heat stress in ruminant livestock. Advanced heat stress management through automated monitoring of heat stress in ruminant livestock based on behaviour, physiology and animal health responses have been widely accepted due to the evolution of technologies like machine learning (ML), neural networks and deep learning (DL). The AI-enabled tools involving automated data collection, pre-processing, data wrangling, development of appropriate algorithms, and deployment of models assist the livestock producers in decision-making based on real-time monitoring and act as early-stage warning systems to forecast disease dynamics based on prediction models. Due to the convincing performance, precision, and accuracy of AI models, the climate-smart livestock production imbibes AI technologies for scaled use in the successful reducing of heat stress in ruminant livestock, thereby ensuring sustainable livestock production and safeguarding the global economy. [ABSTRACT FROM AUTHOR]

This study developed a new metallography–property relationship neural network (MPR-Net) to predict the relationship between the microstructure and mechanical properties of 316L stainless steel built by laser powder bed fusion (LPBF). The accuracy R2 of MPR-Net was 0.96 and 0.91 for tensile strength and Vickers hardness predictions, respectively, based on optical metallurgy images. Feature visualisation methods, such as gradient-weighted class activation mapping (Grad-CAM) and clustering, were employed to interpret the abstract features within the MPR-Net, providing insights into the molten pool morphology and grain formation mechanisms during the LPBF process. Experimental results showed that the optimal process parameters—190 W laser power and 700 mm/s scanning speed—yielded a maximum tensile strength of 762.83 MPa and a Vickers hardness of 253.07 HV0.2 with nearly full densification (99.97%). The study marks the first application of a convolutional neural network (MPR-Net) to predict the mechanical properties of 316L stainless steel samples manufactured through laser powder bed fusion (LPBF) based on metallography. It innovatively employs techniques such as gradient-weighted class activation mapping (Grad-CAM), spatial coherence testing, and clustering to provide deeper insights into the workings of the machine learning model, enhancing the interpretability of complex neural network decisions in material science. [ABSTRACT FROM AUTHOR]

The accurate mapping of age structure and access to spatially explicit information are essential to optimal planning and policy-making for forest ecosystems, including forest management and sustainable economic development. Specifically, surveying and mapping the age structure of forests is crucial for calculating the carbon sequestration capacity of forest ecosystems. However, spatial heterogeneity and limited accessibility make forest age mapping in mountainous areas challenging. Here, we present a new workflow using ICESat-2 LiDAR data integrated with multisource remote sensing imagery to estimate forest age in Shangri-La, China. Two methods—a climate-driven exponential model and a random forest algorithm—are compared to infer the age structure of the five dominant species in Shangri-La. The climate-driven model, with an R2 of 0.67 and an RMSE of 12.79 years, outperforms the random forest model. The derived wall-to-wall forest age map at 30 m resolution reveals that nearly all forests in Shangri-La are mature or overmature, especially among the high-elevation species Abies fabri (Mast.) Craib and Picea asperata Mast., compared with Pinus yunnanensis Franch., Quercus aquifolioides Rehd. and E.H. Wils. and Pinus densata Mast., where the age structure is more evenly distributed across different elevation ranges. Younger forests are frequently found around human settlements and along the Jinsha River valley, whereas older forests are located in remote and high-elevation areas that are less disturbed. The combined use of active and passive remote sensing data has resulted in substantial improvements in the spatial detail and accuracy of wall-to-wall age mapping, which is expected to be a cost-effective approach for supporting forest management and carbon accounting in this important ecological region. The method developed here can be scaled to other mountain areas both to understand the age patterns and structure of mountain forests and to provide critical information for forestation, reforestation and carbon accounting in surface-to-high mountain areas, which are increasingly crucial for climate mitigation. [ABSTRACT FROM AUTHOR]

Biomass can serve as an important indicator for measuring the effectiveness of slope ecological restoration, and unmanned aerial vehicle (UAV) remote sensing provides technical support for the rapid and accurate measurement of vegetation biomass on slopes. Considering a highway slope as the experimental area, in this study, we integrate UAV data and Sentinel-2A images; apply a deep learning method to integrate remote sensing data; extract slope vegetation features from vegetation probability, vegetation indices, and vegetation texture features; and construct a slope vegetation biomass inversion model. The R2 of the slope vegetation biomass inversion model is 0.795, and the p-value in the F-test is less than 0.01, which indicates that the model has excellent regression performance and statistical significance. Based on laboratory biomass measurements, the regression model error is small and reasonable, with RMSE = 0.073, MAE = 0.064, and SE = 0.03. The slope vegetation biomass can be accurately estimated using remote-sensing images with a high precision and good applicability. This study will provide a methodological reference and demonstrate its application in estimating vegetation biomass and carbon stock on highway slopes, thus providing data and methodological support for the simulation of the carbon balance process in slope restoration ecosystems. [ABSTRACT FROM AUTHOR]

Accurately assessing tree mortality probability in the context of global climate changes is important for formulating scientific and reasonable forest management scenarios. In this study, we developed a climate-sensitive individual tree mortality model for Masson pine using data from the seventh (2004), eighth (2009), and ninth (2014) Chinese National Forest Inventory (CNFI) in Hunan Province, South–Central China. A generalized linear mixed-effects model with plots as random effects based on logistic regression was applied. Additionally, a hierarchical partitioning analysis was used to disentangle the relative contributions of the variables. Among the various candidate predictors, the diameter (DBH), Gini coefficient (GC), sum of basal area for all trees larger than the subject tree (BAL), mean coldest monthly temperature (MCMT), and mean summer (May–September) precipitation (MSP) contributed significantly to changes in Masson pine mortality. The relative contribution of climate variables (MCMT and MSP) was 44.78%, larger than tree size (DBH, 32.74%), competition (BAL, 16.09%), and structure variables (GC, 6.39%). The model validation results based on independent data showed that the model performed well and suggested an influencing mechanism of tree mortality, which could improve the accuracy of forest management decisions under a changing climate. [ABSTRACT FROM AUTHOR]

In this paper, a method for augmenting samples of side-scan sonar seafloor sediment images based on CBAM-BCEL1-INGAN is proposed, aiming to address the difficulties in acquiring and labeling datasets, as well as the insufficient diversity and quantity of data samples. Firstly, a Convolutional Block Attention Module (CBAM) is integrated into the residual blocks of the INGAN generator to enhance the learning of specific attributes and improve the quality of the generated images. Secondly, a BCEL1 loss function (combining binary cross-entropy and L1 loss functions) is introduced into the discriminator, enabling it to focus on both global image consistency and finer distinctions for better generation results. Finally, augmented samples are input into an AlexNet classifier to verify their authenticity. Experimental results demonstrate the excellent performance of the method in generating images of coarse sand, gravel, and bedrock, as evidenced by significant improvements in the Frechet Inception Distance (FID) and Inception Score (IS). The introduction of the CBAM and BCEL1 loss function notably enhances the quality and details of the generated images. Moreover, classification experiments using the AlexNet classifier show an increase in the recognition rate from 90.5% using only INGAN-generated images of bedrock to 97.3% using images augmented using our method, marking a 6.8% improvement. Additionally, the classification accuracy of bedrock-type matrices is improved by 5.2% when images enhanced using the method presented in this paper are added to the training set, which is 2.7% higher than that of the simple method amplification. This validates the effectiveness of our method in the task of generating seafloor sediment images, partially alleviating the scarcity of side-scan sonar seafloor sediment image data. [ABSTRACT FROM AUTHOR]

Scientific knowledge of image-based crack detection methods is limited in understanding their performance across diverse crack sizes, types, and environmental conditions. Builders and engineers often face difficulties with image resolution, detecting fine cracks, and differentiating between structural and non-structural issues. Enhanced algorithms and analysis techniques are needed for more accurate assessments. Hence, this research aims to generate an intelligent scheme that can recognize the presence of cracks and visualize the percentage of cracks from an image along with an explanation. The proposed method fuses features from concrete surface images through a ResNet-50 convolutional neural network (CNN) and curvelet transform handcrafted (HC) method, optimized by linear discriminant analysis (LDA), and the eXtreme gradient boosting (XGB) classifier then uses these features to recognize cracks. This study evaluates several CNN models, including VGG-16, VGG-19, Inception-V3, and ResNet-50, and various HC techniques, such as wavelet transform, counterlet transform, and curvelet transform for feature extraction. Principal component analysis (PCA) and LDA are assessed for feature optimization. For classification, XGB, random forest (RF), adaptive boosting (AdaBoost), and category boosting (CatBoost) are tested. To isolate and quantify the crack region, this research combines image thresholding, morphological operations, and contour detection with the convex hulls method and forms a novel algorithm. Two explainable AI (XAI) tools, local interpretable model-agnostic explanations (LIMEs) and gradient-weighted class activation mapping++ (Grad-CAM++) are integrated with the proposed method to enhance result clarity. This research introduces a novel feature fusion approach that enhances crack detection accuracy and interpretability. The method demonstrates superior performance by achieving 99.93% and 99.69% accuracy on two existing datasets, outperforming state-of-the-art methods. Additionally, the development of an algorithm for isolating and quantifying crack regions represents a significant advancement in image processing for structural analysis. The proposed approach provides a robust and reliable tool for real-time crack detection and assessment in concrete structures, facilitating timely maintenance and improving structural safety. By offering detailed explanations of the model's decisions, the research addresses the critical need for transparency in AI applications, thus increasing trust and adoption in engineering practice. [ABSTRACT FROM AUTHOR]

The biomedical imaging field has grown enormously in the past decade. In the era of digitization, the demand for computer-assisted diagnosis is increasing day by day. The COVID-19 pandemic further emphasized how retrieving meaningful information from medical repositories can aid in improving the quality of patient's diagnosis. Therefore, content-based retrieval of medical images has a very prominent role in fulfilling our ultimate goal of developing automated computer-assisted diagnosis systems. Therefore, this paper presents a content-based medical image retrieval system that extracts multi-resolution, noise-resistant, rotation-invariant texture features in the form of a novel pattern descriptor, i.e., M s N r R i T x P , from medical images. In the proposed approach, the input medical image is initially decomposed into three neutrosophic images on its transformation into the neutrosophic domain. Afterwards, three distinct pattern descriptors, i.e., M s T r P , N r T x P , and R i T x P , are derived at multiple scales from the three neutrosophic images. The proposed M s N r R i T x P pattern descriptor is obtained by scale-wise concatenation of the joint histograms of M s T r P × R i T x P and N r T x P × R i T x P . To demonstrate the efficacy of the proposed system, medical images of different modalities, i.e., CT and MRI, from four test datasets are considered in our experimental setup. The retrieval performance of the proposed approach is exhaustively compared with several existing, recent, and state-of-the-art local binary pattern-based variants. The retrieval rates obtained by the proposed approach for the noise-free and noisy variants of the test datasets are observed to be substantially higher than the compared ones. [ABSTRACT FROM AUTHOR]

The pitaya is a common fruit in southern China, but the growing environment of pitayas is complex, with a high density of foliage. This intricate natural environment is a significant contributing factor to misidentification and omission in the detection of the growing state of pitayas. In this paper, the growth states of pitayas are classified into three categories: flowering, immature, and mature. In order to reduce the misidentification and omission in the recognition process, we propose a detection model based on an improvement of the network structure of YOLOv8, namely YOLOv8n-CBN. The YOLOv8n-CBN model is based on the YOLOv8n network structure, with the incorporation of a CBAM attention mechanism module, a bidirectional feature pyramid network (BiFPN), and a C2PFN integration. Additionally, the C2F module has been replaced by a C2F_DCN module containing a deformable convolution (DCNv2). The experimental results demonstrate that YOLOv8n-CBN has enhanced the precision, recall, and mean average precision of the YOLOv8n model with an IoU threshold of 0.5. The model demonstrates a 91.1% accuracy, a 3.1% improvement over the original model, and an F1 score of 87.6%, a 3.4% enhancement over the original model. In comparison to YOLOv3-tiny, YOLOv5s, and YOLOv5m, which are highly effective target detection models, the mAP@0.50–0.95 of our proposed YOLOv8n-CBN is observed to be 10.1%, 5.0%, and 1.6% higher, respectively. This demonstrates that YOLOv8n-CBN is capable of more accurately identifying and detecting the growth status of pitaya in a natural environment. [ABSTRACT FROM AUTHOR]

At the current stage of autonomous driving, monitoring the behavior of safety stewards (drivers) is crucial to establishing liability in the event of an accident. However, there is currently no method for the quantitative assessment of safety steward behavior that is trusted by multiple stakeholders. In recent years, deep-learning-based methods can automatically detect abnormal behaviors with surveillance video, and blockchain as a decentralized and tamper-resistant distributed ledger technology is very suitable as a tool for providing evidence when determining liability. In this paper, a trusted supervision paradigm for autonomous driving (TSPAD) based on multimodal data authentication is proposed. Specifically, this paradigm consists of a deep learning model for driving abnormal behavior detection based on key frames adaptive selection and a blockchain system for multimodal data on-chaining and certificate storage. First, the deep-learning-based detection model enables the quantification of abnormal driving behavior and the selection of key frames. Second, the key frame selection and image compression coding balance the trade-off between the amount of information and efficiency in multiparty data sharing. Third, the blockchain-based data encryption sharing strategy ensures supervision and mutual trust among the regulatory authority, the logistic platform, and the enterprise in the driving process. [ABSTRACT FROM AUTHOR]

Reliable microstructure characterization is essential for establishing process–microstructure–property links and effective quality control. Traditional manual microstructure analysis often struggles with objectivity, reproducibility, and scalability, particularly in complex materials. Machine learning methods offer a promising alternative but are hindered by the challenge of assigning an accurate and consistent ground truth, especially for complex microstructures. This paper introduces a methodology that uses correlative microscopy—combining light optical microscopy, scanning electron microscopy, and electron backscatter diffraction (EBSD)—to create objective, reproducible pixel-by-pixel annotations for ML training. In a semi-automated manner, EBSD-based annotations are employed to generate an objective ground truth mask for training a semantic segmentation model for quantifying simple light optical micrographs. The training masks are directly derived from raw EBSD data using modern deep learning methods. By using EBSD-based annotations, which incorporate crystallographic and misorientation data, the correctness and objectivity of the training mask creation can be assured. The final approach is capable of reproducibly and objectively differentiating bainite and martensite in optical micrographs of complex quenched steels. Through the reduction in the microstructural evaluation to light optical micrographs as the simplest and most widely used method, this way of quantifying microstructures is characterized by high efficiency as well as good scalability. [ABSTRACT FROM AUTHOR]

The Inner Mongolia Autonomous Region, known for its famous Mongolian horses, faces significant environmental challenges due to the large-scale rearing of these animals, which produces a substantial amount of manure. If not managed effectively, this manure can lead to severe environmental pollution. The aim of this study was to investigate whether a small-scale intelligent aeration and heating composting system is effective in treating Mongolian horse manure, with the objective of enhancing composting efficiency and resource utilization to support sustainable agricultural development in the region. The equipment was utilized to treat a compost mixture of Mongolian horse manure and corn stover, allowing for an analysis of the changes in key indicators throughout the composting process. The results demonstrated that the equipment maintained high temperatures for up to eight days during the composting process, effectively inactivating pathogens and promoting the efficient decomposition of organic matter. The system also successfully controlled humidity to 12.7% and maintained oxygen concentration within the optimal range. Post-composting analysis revealed that the final compost contained 2.3% nitrogen, 1.3% phosphorus, and 1.2% potassium, with a pH of 6.4 and conductivity of approximately 5.2 mS/cm. Additionally, the carbon-to-nitrogen ratio decreased significantly from 27.3 to 15.9, indicating substantial organic matter degradation. Seed germination tests showed germination rates of 80%, 86%, and 75% for corn, mung bean, and wheat, respectively, with a final seed germination index of 104%. This study concluded that the small aeration and heating composting equipment is highly effective in treating Mongolian horse manure, producing high-quality organic fertilizers that significantly enhance soil fertility and demonstrate considerable potential for supporting sustainable agricultural practices and improving environmental management in the Inner Mongolia Autonomous Region. [ABSTRACT FROM AUTHOR]

Soil texture is a significant attribute of soil properties. Obtaining insight into the soil texture is beneficial when making agricultural decisions during production. Nevertheless, assessing the soil texture in specific laboratory conditions entails substantial dedication, which is time-consuming and includes a high cost. In this paper, we propose a soil texture detection network by embedding the frequency channel attention network and a texture encoding network into the representation learning paradigm of the ResNet framework. Concretely, the former is reliable in exploiting the feature correlations among multi-frequency, while the latter focuses on encoding feature variables, jointly enhancing the ability of feature expression. Meanwhile, the clay, silt, and sand particles present in the soil are exported through a ResNet18 fully linked layer. Experimental results show that the correlation coefficient for predicting clay, silt, and sand content are 0.931, 0.936, and 0.957, respectively. For the root mean square error, the quantitative scores are 2.106%, 3.390%, and 3.602%, respectively. The proposed network also exhibits proposing generalization capability, yielding quite considerable results on different soil samples. Notably, the detection results are almost in agreement with the conventional laboratory measurements, and, at the same time, outperform other competitors, making it highly attractive for practical applications. [ABSTRACT FROM AUTHOR]

Traditional soil salinity measurement methods are generally complex and labor-intensive, restricting the long-term monitoring of soil salinity, particularly in arid areas. In this context, the soil salt content (SSC) data from farms in the Heihe River Basin in Northwest China were collected in three consecutive years (2021, 2022, and 2023). In addition, the spectral reflectance and texture features of different sampling sites in the study area were extracted from long-term unmanned aerial vehicle (UAV) multispectral images to replace the red and near-infrared bands with a newly introduced red edge band. The spectral index was calculated in this study before using four sensitive variable combinations to predict soil salt contents. A Pearson correlation analysis was performed in this study to screen 57 sensitive features. In addition, 36 modeling scenarios were conducted based on the Extreme Gradient Boosting (XGBoost Implemented using R language 4.3.1), Backpropagation Neural Network (BPNN), and Random Forest (RF) algorithms. The most optimal algorithms for predicting the soil salt contents in farmland located in the Heihe River Basin, in the arid region of Northwest China, were determined. The results showed a higher prediction accuracy for the XGBoost algorithm than the RF and BPNN algorithms, accurately reflecting the actual soil salt contents in the arid area. On the other hand, the most accurate predicted soil salt contents were obtained in 2023 using the XGBoost algorithm, with coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE) ranges of 0.622–0.820, 0.086–0.157, and 0.078–0.134, respectively, whereas the most stable prediction results were obtained using the collected data in 2022. From the perspective of different sensitive variable input combinations, the implementation of the XGBoost algorithm using the spectral index–spectral reflectance–texture feature input combination resulted in comparatively higher prediction accuracies than those of the other variable combinations in 2022 and 2023. Specifically, the R2, RMSE, and MAE values obtained using the spectral index–spectral reflectance–texture feature input combination were 0.674, 0.133, and 0.086 in 2022 and 0.820, 0.165, and 0.134 in 2023, respectively. Therefore, our results demonstrated that the spectral index–spectral reflectance–texture feature was the optimal sensitive variable input combination for the machine learning algorithms, of which the XGBoost algorithm is the most optimal model for predicting soil salt contents. The results of this study provide a theoretical basis for the rapid and accurate prediction of soil salinity in arid areas. [ABSTRACT FROM AUTHOR]

The concept of geosites and geodiversity sites that document selected elements of geodiversity has proved to be extremely useful in the preservation and communication of the abiotic diversity of the Earth. However, the process of the designation, description, evaluation and, finally, promotion of geosites requires a fair amount of effort. This hinders the recognition of geosites and the development of geoparks in many areas that would otherwise benefit from their rich but undervalued abiotic environment. To rectify this, the present study introduces the use of automated geographic information system (GIS) mapping and generative artificial intelligence (GAI) for the designation and promotion of points of geological interest and potential geodiversity sites. When used effectively, these techniques permit the rapid development of geodiversity site inventories and, eventually, their dissemination to the general public and decision-makers. In this study, GAI is employed to produce diverse promotional content, both textual and visual, that facilitates geoscientific communication. A case study of an aspiring geopark located in Poland (Central Europe) is discussed, showing that GAI has the potential to enable the rapid development of easy-to-understand and diverse educational materials, limiting the amount of resources and labour required for the effective promotion of geodiversity. [ABSTRACT FROM AUTHOR]

This paper proposes a multiplexing multi-scale features network (MMF-Network) for salient target detection to tackle the issue of incomplete detection structures when identifying salient targets across different scales. The network, based on encoder–decoder architecture, integrates a multi-scale aggregation module and a multi-scale visual interaction module. Initially, a multi-scale aggregation module is constructed, which, despite potentially introducing a small amount of noise, significantly enhances the high-level semantic and geometric information of features. Subsequently, SimAM is employed to emphasize feature information, thereby highlighting the significant target. A multi-scale visual interaction module is designed to enable compatibility between low-resolution and high-resolution feature maps, with dilated convolutions utilized to expand the receptive field of high-resolution feature maps. Finally, the proposed MMF-Network is tested on three datasets: DUTS-Te, HUK-IS, and PSCAL-S, achieving scores of 0.887, 0.811, and 0.031 in terms of its F-value SSIM and MA, respectively. The experimental results demonstrate that the MMF-Network exhibits a superior performance in salient target detection. [ABSTRACT FROM AUTHOR]

Proximate analysis, including ash, volatile matter, moisture, fixed carbon, and calorific value, is a fundamental aspect of fuel testing and serves as the primary method for evaluating coal quality, which is critical for the processing and utilization of coal. The traditional analytical methods involve time-consuming and costly combustion processes, particularly when applied to large volumes of coal that need to be sampled in massive batches. Hyperspectral imaging is promising for the rapid and nondestructive determination of coal quality indices. In this study, a fast and nondestructive coal proximate analysis method with combined spectral-spatial features was developed using a hyperspectral imaging system in the 450–2500 nm range. The processed spectra were evaluated using PLSR, with the most effective MSC spectra selected. To reduce the spectral redundancy and improve the accuracy, the SPA, Boruta, iVISSA, and CARS algorithms were adopted to extract the characteristic wavelengths, and 16 prediction models were constructed and optimized based on the PLSR, RF, BPNN, and LSSVR algorithms within the Optuna framework for each quality indicator. For spatial information, the histogram statistics, gray-level covariance matrix, and Gabor filters were employed to extract the texture features within the characteristic wavelengths. The texture feature-based and combined spectral-texture feature-based prediction models were constructed by applying the spectral modeling strategy, respectively. Compared with the models based on spectral or texture features only, the LSSVR models with combined spectral-texture features achieved the highest prediction accuracy in all quality metrics, with R p 2 values of 0.993, 0.989, 0.979, 0.948, and 0.994 for Ash, VM, MC, FC, and CV, respectively. This study provides a technical reference for hyperspectral imaging technology as a new method for the rapid, nondestructive proximate analysis and quality assessment of coal. [ABSTRACT FROM AUTHOR]

Considering the defect detection issues in electroluminescence (EL) of photovoltaic (PV) cell systems, lots of factors result in performance degradation, including defect diversity, data imbalance, scale difference, etc. Focal-EIoU loss, an effective defect detection solution for EL, is proposed based on the improved YOLOv5. Firstly, by analyzing the detection background and scale characteristics of EL defects, a binary classification is carried out in the system. Subsequently, a cascade detection network based on YOLOv5 is designed to further extract features from the binary-classified defects. The defect localization and classification are achieved in this way. To address the problem of imbalanced defect samples, a loss function is designed based on EIoU and Focal-F1 Loss. Experimental results are illustrated to show the effectiveness. Compared with the existing CNN-based deep learning approaches, the proposed focal loss calculation-based method can effectively improve the performance of handling sample imbalance. Moreover, in the detection of 12 types of defects, the Yolov5 algorithms can always obtain higher MAP (mean average precision) even with different parameter levels (Yolov5m: 0.791 vs. 0.857, Yolov5l: 0.798 vs. 0.862, Yolov5x: 0.802 vs. 0.867, Yolov5s: 0.793 vs. 0.865). [ABSTRACT FROM AUTHOR]

Single-image super-resolution (SISR) seeks to elucidate the mapping relationships between low-resolution and high-resolution images. However, high-performance network models often entail a significant number of parameters and computations, presenting limitations in practical applications. Therefore, prioritizing a light weight and efficiency becomes crucial when applying image super-resolution (SR) to real-world scenarios. We propose a straightforward and efficient method, the Multi-Branch Feature Extraction Residual Network (MFERN), to tackle lightweight image SR through the fusion of multi-information self-calibration and multi-attention information. Specifically, we have devised a Multi-Branch Residual Feature Fusion Module (MRFFM) that leverages a multi-branch residual structure to succinctly and effectively fuse multiple pieces of information. Within the MRFFM, we have designed the Multi-Scale Attention Feature Fusion Block (MAFFB) to adeptly extract features via convolution and self-calibration attention operations. Furthermore, we introduce a Dual Feature Calibration Block (DFCB) to dynamically fuse feature information using dynamic weight values derived from the upper and lower branches. Additionally, to overcome the limitation of convolution in solely extracting local information, we incorporate a Transformer module to effectively integrate global information. The experimental results demonstrate that our MFERN exhibits outstanding performance in terms of model parameters and overall performance. [ABSTRACT FROM AUTHOR]

Most of the existing dehazing methods ignore some global and local detail information when processing images and fail to fully combine feature information at different levels, which leads to contrast imbalance and residual haze in the dehazed images. To this end, this article proposes a image dehazing network based on hybrid parallel attention feature fusion, called the HPA-HFF network. This network is an optimization of the basic network, FFA-Net. First, the hybrid parallel attention (HPA) module is introduced, which uses parallel connections to mix different types of attention mechanisms, which can not only enhance the extraction and fusion capabilities of global spatial context information but also enhance the expression capabilities of features and have better dehazing effects on uneven distribution of haze. Second, the hierarchical feature fusion (HFF) module is introduced, which dynamically fuses feature maps from different paths to adaptively increase their receptive field and refine and enhance image features. Experimental results demonstrate that the HPA-HFF network proposed in this article is contrasted with eight mainstream dehazing networks on the public dataset RESIDE. The HPA-HFF network achieves the highest PSNR (39.41) and SSIM (0.9967) and obtains a good dehazing effect in subjective vision. [ABSTRACT FROM AUTHOR]

Simple Summary: Magnetic resonance imaging (MRI) plays a crucial role in rectal cancer management, offering valuable information for staging, treatment response, and patient prognosis. MRI biomarkers, such as circumferential resection margin (CRM), extramural venous invasion (EMVI), tumour deposits, and MRI tumour regression grade (mrTRG) alongside functional imaging techniques such as diffusion-weighted imaging (DWI) and dynamic contrast enhancement (DCE) are essential in clinical decision-making. Additionally, emerging technologies like radiomics and artificial intelligence (AI) are showing promise in improving the precision of rectal cancer care. As the focus increasingly shifts toward non-invasive management, such as 'watch-and-wait' approach, this review discusses the role of predictive and prognostic MRI biomarkers in rectal cancer and how they integrate into everyday clinical practice. The role of magnetic resonance imaging (MRI) in rectal cancer management has significantly increased over the last decade, in line with more personalized treatment approaches. Total neoadjuvant treatment (TNT) plays a pivotal role in the shift from traditional surgical approach to non-surgical approaches such as 'watch-and-wait'. MRI plays a central role in this evolving landscape, providing essential morphological and functional data that support clinical decision-making. Key MRI-based biomarkers, including circumferential resection margin (CRM), extramural venous invasion (EMVI), tumour deposits, diffusion-weighted imaging (DWI), and MRI tumour regression grade (mrTRG), have proven valuable for staging, response assessment, and patient prognosis. Functional imaging techniques, such as dynamic contrast-enhanced MRI (DCE-MRI), alongside emerging biomarkers derived from radiomics and artificial intelligence (AI) have the potential to transform rectal cancer management offering data that enhance T and N staging, histopathological characterization, prediction of treatment response, recurrence detection, and identification of genomic features. This review outlines validated morphological and functional MRI-derived biomarkers with both prognostic and predictive significance, while also exploring the potential of radiomics and artificial intelligence in rectal cancer management. Furthermore, we discuss the role of rectal MRI in the 'watch-and-wait' approach, highlighting important practical aspects in selecting patients for non-surgical management. [ABSTRACT FROM AUTHOR]

Entomological radar is one of the most effective tools for monitoring insect migration, capable of detecting migratory insects concentrated in layers and facilitating the analysis of insect migration behavior. However, traditional entomological radar, with its low resolution, can only provide a rough observation of layer concentrations. The advent of High-Resolution Phased Array Radar (HPAR) has transformed this situation. With its high range resolution and high data update rate, HPAR can generate detailed concentration spatiotemporal distribution heatmaps. This technology facilitates the detection of changes in insect concentrations across different time periods and altitudes, thereby enabling the observation of large-scale take-off, landing, and layering phenomena. However, the lack of effective techniques for extracting insect concentration data of different phenomena from these heatmaps significantly limits detailed analyses of insect migration patterns. This paper is the first to apply instance segmentation technology to the extraction of insect data, proposing a method for segmenting and extracting insect concentration data from spatiotemporal distribution heatmaps at different phenomena. To address the characteristics of concentrations in spatiotemporal distributions, we developed the Heatmap Feature Fusion Network (HFF-Net). In HFF-Net, we incorporate the Global Context (GC) module to enhance feature extraction of concentration distributions, utilize the Atrous Spatial Pyramid Pooling with Depthwise Separable Convolution (SASPP) module to extend the receptive field for understanding various spatiotemporal distributions of concentrations, and refine segmentation masks with the Deformable Convolution Mask Fusion (DCMF) module to enhance segmentation detail. Experimental results show that our proposed network can effectively segment concentrations of different phenomena from heatmaps, providing technical support for detailed and systematic studies of insect migration behavior. [ABSTRACT FROM AUTHOR]

Remote sensing allows us to conduct large-scale scientific studies that require extensive mapping and the amalgamation of numerous images. However, owing to variations in radiation, atmospheric conditions, sensor perspectives, and land cover, significant color discrepancies often arise between different images, necessitating color consistency adjustments for effective image mosaicking and applications. Existing methods for color consistency adjustment in remote sensing images struggle with complex one-to-many nonlinear color-mapping relationships, often resulting in texture distortions. To address these challenges, this study proposes a convolutional neural network-based color consistency method for remote sensing cartography that considers both global and local color mapping and texture mapping constrained by the source domain. This method effectively handles complex color-mapping relationships while minimizing texture distortions in the target image. Comparative experiments on remote sensing images from different times, sensors, and resolutions demonstrated that our method achieved superior color consistency, preserved fine texture details, and provided visually appealing outcomes, assisting in generating large-area data products. [ABSTRACT FROM AUTHOR]

The Leaf Area Index (LAI) strongly influences vegetation evapotranspiration and photosynthesis rates. Timely and accurately estimating the LAI is crucial for monitoring vegetation growth. The unmanned aerial vehicle (UAV) multispectral digital camera platform has been proven to be an effective tool for this purpose. Currently, most remote sensing estimations of LAIs focus on cereal crops, with limited research on economic crops such as apples. In this study, a method for estimating the LAI of an apple orchard by extracting spectral and texture information from UAV multispectral images was proposed. Specifically, field measurements were conducted to collect LAI data for 108 sample points during the final flowering (FF), fruit setting (FS), and fruit expansion (FE) stages of apple growth in 2023. Concurrently, UAV multispectral images were obtained to extract spectral and texture information (Gabor transform). The Support Vector Regression Recursive Feature Elimination (SVR-REF) was employed to select optimal features as inputs for constructing models to estimate the LAI. Finally, the optimal model was used for LAI mapping. The results indicate that integrating spectral and texture information effectively enhances the accuracy of LAI estimation, with the relative prediction deviation (RPD) for all models being greater than 2. The Categorical Boosting (CatBoost) model established for FF exhibits the highest accuracy, with a validation set R2, root mean square error (RMSE), and RPD of 0.867, 0.203, and 2.482, respectively. UAV multispectral imagery proves to be valuable in estimating apple orchard LAIs, offering real-time monitoring of apple growth and providing a scientific basis for orchard management. [ABSTRACT FROM AUTHOR]

The speckle noise inherent in synthetic aperture radar (SAR) imaging has long posed a challenge for SAR data processing, significantly affecting image interpretation and recognition. Recently, deep learning-based SAR speckle removal algorithms have shown promising results. However, most existing algorithms rely on convolutional neural networks (CNN), which may struggle to effectively capture global image information and lead to texture loss. Besides, due to the different characteristics of optical images and synthetic aperture radar (SAR) images, the results of training with simulated SAR data may bring instability to the real-world SAR data denoising. To address these limitations, we propose an innovative approach that integrates swin transformer blocks into the prediction noise network of the denoising diffusion probabilistic model (DDPM). By harnessing DDPM's robust generative capabilities and the Swin Transformer's proficiency in extracting global features, our approach aims to suppress speckle while preserving image details and enhancing authenticity. Additionally, we employ a post-processing strategy known as pixel-shuffle down-sampling (PD) refinement to mitigate the adverse effects of training data and the training process, which rely on spatially uncorrelated noise, thereby improving its adaptability to real-world SAR image despeckling scenarios. We conducted experiments using both simulated SAR image datasets and real SAR image datasets, evaluating our algorithm from subjective and objective perspectives. The visual results demonstrate significant improvements in noise suppression and image detail restoration. The objective results demonstrate that our method obtains state-of-the-art performance, which outperforms the second-best method by an average peak signal-to-noise ratio (PSNR) of 0.93 dB and Structural Similarity Index (SSIM) of 0.03, affirming the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

In the infrared remote sensing imaging system, the output of the detector is uneven, resulting in noticeable stripe noise in the image, which significantly reduces the image quality. Therefore, in response to the problem of establishing a variational model for the image globally and fixing the fractional order of the regularization term in the existing stripe noise removal algorithms based on model optimization, a stripe noise removal algorithm for infrared remote sensing images based on an adaptive weighted variable order model is proposed. In this algorithm, the vertical and approximate components containing stripe noise in the image are first separated through multi-level and multi-scale wavelet transform, and only these two components are processed; secondly, the global sparsity and the gradient sparsity of stripe noise and the variable order gradient sparsity of the information component are constrained by L1 norm, so as to establish the stripe noise removal model. The order of the fractional derivative is adaptively assigned to each pixel of the information component through local variance; then, an adaptive weight operator is introduced, which can assign different weights to the pixels in the variable order derivative of the information component on the basis of the image gradient information. Finally, through the ADMM algorithm, we can obtain the optimal solution of this model. The processing results of simulated and actual data indicate that the proposed algorithm performs well in all indexes and has obvious advantages in removing stripe noise and preserving image details. [ABSTRACT FROM AUTHOR]

Accurate estimation of soil moisture content (SMC) in the field is a critical aspect of precise irrigation management. The development of unmanned aerial vehicle (UAV) platforms has provided an economically efficient means for field-scale SMC measurements. However, previous studies have mostly focused on single-sensor estimates of SMC. Additionally, the lack of differentiation between various crops and their growth stages has resulted in an unclear understanding of how crop types and growth stages affect the accuracy of SMC estimation at different soil depths. Therefore, the purpose of this paper was to use UAV multimodal remote sensing and a machine learning algorithm to estimate the SMC in agricultural fields and investigate estimation's effectiveness under different scenarios. The results indicated the following: (1) The multispectral remote sensing method provided higher accuracy in SMC estimation compared to thermal infrared remote sensing. Moreover, the integration of multimodal data improved the accuracy of SMC estimation, enhancing the coefficient of determination (R2) by approximately 14% over that achieved through the use of multispectral data alone and 39% over that of thermal infrared data alone. (2) Across the entire growth period, the optimal soil depths of SMC estimation for soybean were 10 cm and 20 cm (average R2 were 0.81 and 0.82, respectively), while for corn, they were 10 cm, 20 cm, and 40 cm (average R2 were 0.59, 0.60, and 0.55, respectively). (3) The SMC estimation model performed better for both crops during the first three growth stages, with accuracy declining in the maturity stage. These results demonstrate that this approach can provide relatively accurate root zone SMC estimates for different crops throughout their main growth periods. Thus, it can be employed for SMC monitoring and precision irrigation system design. [ABSTRACT FROM AUTHOR]