Search Results (136)

Vegetation canopy water content (CWC) crucially affects stomatal conductance and photosynthesis and, consequently, is a key state variable in advanced ecosystem models. Remote sensing has been shown to be an effective tool for retrieving CWCs. However, the retrieval of the CWC from satellite remote sensing data is affected by the vegetation canopy structure and soil background. This study proposes a methodology that combines a modified spectral down-scaling model with a high-universality leaf water content inversion model to retrieve the CWC through constraining the impacts of canopy structure and soil background on CWC retrieval. First, canopy spectra acquired by satellite sensors were down-scaled to leaf reflectance spectra according to the probabilities of viewing the sunlit foliage (PT) and background (PG) and the estimated spectral multiple scattering factor (M). Then, leaf water content, or equivalent water thickness (EWT), was obtained from the down-scaled leaf reflectance spectra via a leaf-scale EWT inversion model calibrated with PROSPECT simulation data. Finally, the CWC was calculated as the product of the estimated leaf EWT and canopy leaf area index. Validation of this coupled model was performed using satellite-ground synchronous observation data across various vegetation types within the study area, affirming the model’s broad applicability. Results indicate that the modified spectral down-scaling model accurately retrieves leaf reflectance spectra, aligning closely with site-level measured spectra. Compared to the direct inversion approach, which performs poorly with Hyperion satellite images, the down-scale strategy notably excels. Specifically, the Similarity Water Index (SWI)-based canopy EWT coupled model achieved the most precise estimation, with a normalized Root Mean Square Error (nRMSE) of 15.28% and an adjusted R² of 0.77, surpassing the performance of the best index Shortwave Angle Normalized Index (SANI)-based model (nRMSE = 15.61%, adjusted R² = 0.52). Given its calibration using simulated data, this coupled model proved to be a potent method for extracting canopy EWT from satellite imagery, suggesting its applicability to retrieve other vegetative biochemical components from satellite data. Full article

Objective: This study aimed to assess the impact of artificial intelligence (AI)-driven noise reduction algorithms on metal artifacts and image quality parameters in cone-beam computed tomography (CBCT) images of the oral cavity. Materials and Methods: This retrospective study included 70 patients, 61 of whom were analyzed after excluding those with severe motion artifacts. CBCT scans, performed using a Hyperion X9 PRO 13 × 10 CBCT machine, included images with dental implants, amalgam fillings, orthodontic appliances, root canal fillings, and crowns. Images were processed with the ClariCT.AI deep learning model (DLM) for noise reduction. Objective image quality was assessed using metrics such as the differentiation between voxel values (ΔVVs), the artifact index (AIx), and the contrast-to-noise ratio (CNR). Subjective assessments were performed by two experienced readers, who rated overall image quality and artifact intensity on predefined scales. Results: Compared with native images, DLM reconstructions significantly reduced the AIx and increased the CNR (p < 0.001), indicating improved image clarity and artifact reduction. Subjective assessments also favored DLM images, with higher ratings for overall image quality and lower artifact intensity (p < 0.001). However, the ΔVV values were similar between the native and DLM images, indicating that while the DLM reduced noise, it maintained the overall density distribution. Orthodontic appliances produced the most pronounced artifacts, while implants generated the least. Conclusions: AI-based noise reduction using ClariCT.AI significantly enhances CBCT image quality by reducing noise and metal artifacts, thereby improving diagnostic accuracy and treatment planning. Further research with larger, multicenter cohorts is recommended to validate these findings. Full article

Background/Objectives: The purpose of this preliminary study was to evaluate the diagnostic performance of an AI-driven platform, Diagnocat (Diagnocat Ltd., San Francisco, CA, USA), for assessing endodontic treatment outcomes using panoramic radiographs (PANs). Materials and Methods: The study included 55 PAN images of 55 patients (15 males and 40 females, aged 12–70) who underwent imaging at a private dental center. All images were acquired using a Hyperion X9 PRO digital cephalometer and were evaluated using Diagnocat, a cloud-based AI platform. The AI system assessed the following endodontic treatment features: filling probability, obturation adequacy, density, overfilling, voids in filling, and short filling. Two human observers independently evaluated the images, and their consensus served as the reference standard. The diagnostic accuracy metrics were calculated. Results: The AI system demonstrated high accuracy (90.72%) and a strong F1 score (95.12%) in detecting the probability of endodontic filling. However, the system showed variable performance in other categories, with lower accuracy metrics and unacceptable F1 scores for short filling and voids in filling assessments (8.33% and 14.29%, respectively). The accuracy for detecting adequate obturation and density was 55.81% and 62.79%, respectively. Conclusions: The AI-based system showed very high accuracy in identifying endodontically treated teeth but exhibited variable diagnostic accuracy for other qualitative features of endodontic treatment. Full article

Imaging spectroscopy allows us to identify surface materials by analyzing the spectra resulting from the light–material interaction. In this preliminary study, we analyze a pair of hyperspectral cubes acquired by PRISMA (on 20 April 2021) and EO1- Hyperion (on 4 July 2015) over the Indonesian Lusi mud eruption. We show the potential suitability of using the two sensors for characterizing the mineralogical features in demanding “wet and muddy” environments such as Lusi. We use spectral library reflectance spectra like Illite Chlorite from the USGS spectral library, which are known to be associated with Lusi volcanic products, to identify minerals. In addition, we have measured the reflectance spectra and composition of Lusi sampled mud collected in November 2014. Finally, we compare them with reflectance spectra from EO1-Hyperion and PRISMA. The use of hyperspectral sensors at improved SNR, such as PRISMA, has shown the potential to determine the mineral composition of Lusi PRISMA data, which allowed the distinction of areas with different turbidities as well. Artifacts in the VNIR spectral region of the L2 PRISMA reflectance product were found, suggesting that future work needs to take into account an independent atmospheric correction rather than using the L2D PRISMA product. Full article

Polygraph tests have been used for many years as a means of detecting deception, but their accuracy has been the subject of much debate. In recent years, researchers have explored the use of neural networks in polygraph scoring to improve the accuracy of deception detection. The purpose of this scoping review is to offer a comprehensive overview of the existing research on the subject of neural network applications in scoring polygraph tests. A total of 57 relevant papers were identified and analyzed for this review. The papers were examined for their research focus, methodology, results, and conclusions. The scoping review found that neural networks have shown promise in improving the accuracy of polygraph tests, with some studies reporting significant improvements over traditional methods. However, further research is needed to validate these findings and to determine the most effective ways of integrating neural networks into polygraph testing. The scoping review concludes with a discussion of the current state of the field and suggestions for future research directions. Full article

Two key hurdles to the adoption of Machine Learning (ML) techniques in hyperspectral data compression are computational complexity and scalability for large numbers of bands. These are due to the limited computing capacity available in remote sensing platforms and the high computational cost of compression algorithms for hyperspectral data, especially when the number of bands is large. To address these issues, a channel clusterisation strategy is proposed, which reduces the computational demands of learned compression methods for real scenarios and is scalable for different sources of data with varying numbers of bands. The proposed method is compatible with an embedded implementation for state-of-the-art on board hardware, a first for a ML hyperspectral data compression method. In terms of coding performance, our proposal surpasses established lossy methods such as JPEG 2000 preceded by a spectral Karhunen-Loève Transform (KLT), in clusters of 3 to 7 bands, achieving a PSNR improvement of, on average, 9 dB for AVIRIS and 3 dB for Hyperion images. Full article

As a clean energy resource, coalbed methane (CBM) is an important industry in China’s dual-carbon strategic planning. Despite the immense potential of CBM resources in China, the current exploration level remains low due to outdated survey technology, impeding large-scale exploration and development. This study investigates the application of hyperspectral data in CBM enrichment areas, specifically focusing on the extraction of alteration minerals in the Hudi coal mine area of the Qinshui Basin using ZY-1 02D and Hyperion hyperspectral data. The hyperspectral alteration mineral identification methods are summarized and analyzed. A method that combines spectral feature matching and diagnostic characteristic parameters is proposed for mineral extraction based on the spectral characteristics of different minerals. The extraction results are verified through field samples using X-ray diffraction analysis. Results show that (1) both ZY-1 02D and Hyperion hyperspectral data yield favorable extraction results for clay and carbonate minerals; (2) the overall accuracy of clay and carbonate minerals extraction is higher using ZY-1 02D data compared with Hyperion data, with accuracies of 81.67% and 79.03%, respectively; (3) the proposed method effectively extracts alteration minerals in CBM enrichment areas using hyperspectral data, thereby providing valuable technical support for the application of hyperspectral data. Full article

Since individual neural networks have limited deep expressiveness and effectiveness, many learning frameworks face difficulties in the availability and balance of sample selection. As a result, in change detection, it is difficult to upgrade the hit rate of a high-performance model on both positive and negative pixels. Therefore, supposing that the sacrificed components coincide perfectly with the important evaluation objectives, such as positives, it would lose more than gain. To address this issue, in this paper, we propose a multi-visual collaborative deep network (MV-CDN) served by three collaborative network members that consists of three subdivision approaches, the CDN with one collaborator (CDN-C), CDN with two collaborators (CDN-2C), and CDN with three collaborators (CDN-3C). The purpose of the collaborator is to re-evaluate the feature elements in the network transmission, and thus to translate the group-thinking into a more robust field of vision. We use three sets of public double-temporal hyperspectral images taken by the AVIRIS and HYPERION sensors to show the feasibility of the proposed schema. The comparison results have confirmed that our proposed schema outperforms the existing state-of-the-art algorithms on the three tested datasets, which demonstrates the broad adaptability and progressiveness of the proposal. Full article

This research aimed to develop a novel dark hyperspectral absolute calibration (DAHAC) model using stable dark targets of “Global Cluster-36” (GC-36), one of the clusters from the “300 Class Global Classification”. The stable dark sites were identified from GC-36 called “Dark EPICS-Global” covering the surface types viz. dark rock, volcanic area, and dark sand. The Dark EPICS-Global shows a temporal variation of 0.02 unit reflectance. This work used the Landsat-8 (L8) Operational Land Imager (OLI), Sentinel-2A (S2A) Multispectral Instrument (MSI), and Earth Observing One (EO-1) Hyperion data for the DAHAC model development, where well-calibrated L8 and S2A were used as the reference sensors, while EO-1 Hyperion with a 10 nm spectral resolution was used as a hyperspectral library. The dark hyperspectral dataset (DaHD) was generated by combining the normalized hyperspectral profile of L8 and S2A for the DAHAC model development. The DAHAC model developed in this study takes into account the solar zenith and azimuth angles, as well as the view zenith and azimuth angles in Cartesian coordinates form. This model is capable of predicting TOA reflectance in all existing spectral bands of any sensor. The DAHAC model was then validated with the Landsat-7 (L7), Landsat-9 (L9), and Sentinel-2B (S2B) satellites from their launch dates to March 2022. These satellite sensors vary in terms of their spectral resolution, equatorial crossing time, spatial resolution, etc. The comparison between the DAHAC model and satellite measurements showed an accuracy within 0.01 unit reflectance across the overall spectral band. The proposed DAHAC model uncertainty level was determined using Monte Carlo simulation and found to be 0.04 and 0.05 unit reflectance for the VNIR and SWIR channels, respectively. The DAHAC model double ratio was used as a tool to perform the inter-comparison between two satellites. The sensor inter-comparison results for L8 and L9 showed a 2% difference and 1% for S2A and S2B across all spectral bands. Full article

Graph convolutional neural network architectures combine feature extraction and convolutional layers for hyperspectral image classification. An adaptive neighborhood aggregation method based on statistical variance integrating the spatial information along with the spectral signature of the pixels is proposed for improving graph convolutional network classification of hyperspectral images. The spatial-spectral information is integrated into the adjacency matrix and processed by a single-layer graph convolutional network. The algorithm employs an adaptive neighborhood selection criteria conditioned by the class it belongs to. Compared to fixed window-based feature extraction, this method proves effective in capturing the spectral and spatial features with variable pixel neighborhood sizes. The experimental results from the Indian Pines, Houston University, and Botswana Hyperion hyperspectral image datasets show that the proposed AN-GCN can significantly improve classification accuracy. For example, the overall accuracy for Houston University data increases from 81.71% (MiniGCN) to 97.88% (AN-GCN). Furthermore, the AN-GCN can classify hyperspectral images of rice seeds exposed to high day and night temperatures, proving its efficacy in discriminating the seeds under increased ambient temperature treatments. Full article

As we know, 3DPC is printed layer by layer compared with mold-casting conventional concrete. Pore structure and layer-to-layer interface are two main aspects of the internal structure for 3DPC, which decide 3DPC’s mechanical performance. The layer-to-layer interface caused by printing is specific to 3DPC. The emphasis of this study lies in the layer-to-layer interfaces of 3DPC. The first aim of this study is to quantify the characteristics of the layer-to-layer interface and therefore characterize different aspects of the interfaces. The second aim of this study is to explore how the internal structure of printed concrete influences the mechanical performance of 3DPC. This research set out to design a series of experimental comparisons between 3DPC and casted concrete with the same compositions. Mechanical tests, i.e., compressive stress, ultrasonic Pulse Velocity test, flexural tension, and tension splitting, as well as the Ultrasonic Pulse Velocity test, were performed to check the mechanical performance of 3DPC. Contrary to what has often been expected, the mechanical test results showed the printed concrete has a quality not worse than casted concrete with the same recipe. Meanwhile, the X-ray computed tomography (X-CT) is used to characterize the internal structure, pore shapes, and interfaces of 3DPC. First, the investigation revealed that the lower total porosity and fewer big voids could be the fundamental causes meaning 3DPC has a better mechanical performance than casted concrete. Second, the statistics based on aspect ratio show that the distribution curves follow similar trends, regardless of the printed or casted concrete. Third, this study quantified the depth of the different interfaces for 3DPC. The results suggest that the porosity in an interface varies in a range. The author’s pioneer work has contributed to our present understanding of the interfaces of 3DPC. Full article

Monitoring the Earth’s surface and objects is important for many applications, such as managing natural resources, crop yield predictions, and natural hazard analysis. Remote sensing is one of the most efficient and cost-effective solutions for analyzing land-use and land-cover (LULC) changes over the Earth’s surface through advanced computer algorithms, such as classification and change detection. In the past literature, various developments were made to change detection algorithms to detect LULC multitemporal changes using optical or microwave imagery. The optical-based hyperspectral highlights the critical information, but sometimes it is difficult to analyze the dataset due to the presence of atmospheric distortion, radiometric errors, and misregistration. In this work, an artificial neural network-based post-classification comparison (ANPC) as change detection has been utilized to detect the muti-temporal LULC changes over a part of Uttar Pradesh, India, using the Hyperion EO-1 dataset. The experimental outcomes confirmed the effectiveness of ANPC (92.6%) as compared to the existing models, such as a spectral angle mapper (SAM) based post-classification comparison (SAMPC) (89.7%) and k-nearest neighbor (KNN) based post-classification comparison (KNNPC) (91.2%). The study will be beneficial in extracting critical information about the Earth’s surface, analysis of crop diseases, crop diversity, agriculture, weather forecasting, and forest monitoring. Full article

This study investigates how compatibility and perceived enjoyment affect the link between intention to use and actual technology use in Romanian preschool education, building on earlier studies. Methods: 300 participants were invited to participate in this research from 15 Romanian counties. 182 preschool teachers’ questionnaires were utilized for data analysis after the return and screening of responses. A valid and accurate scale evaluating preschool teachers’ behavior towards technology adoption was included in the questionnaire, along with self-reported demographic data, professional identification, and other information. Data was analyzed using SPSS V.16. Results: (1) Intention to use, compatibility, perceived enjoyment, and actual use were positively associated. (2) The effect of compatibility and perceived enjoyment on the link between intention to use and actual technology use was carried out in the following way: Intention to use → Compatibility with technology → Perceived enjoyment → Actual use. We hypothesize that intention to use affects compatibility, compatibility affects perceived enjoyment, and, lastly, perceived enjoyment affects actual use. For a more robust validation of results, we have also modelled this relationship with the Radial Basis Function (RBF) neural network. Conclusion: Compatibility and perceived enjoyment partially mediate the relationship between intention to use and actual technology use in class by Romanian preschool teachers. According to the theory of planned behavior, this study brought to light the intricacy of the relationship between preschool teachers’ intention to utilize technology in the classroom and their actual usage of it. Limitations and implications are discussed. Full article

Following the success of the first hyperspectral sensor, the evaluation of hyperspectral image capability became a challenge in research, which mainly focused on improving image pre-processing and processing steps to minimize their errors, whereas in this study, the focus was on the weight of hyperspectral sensor characteristics on image capability in order to distinguish this effect from errors caused by image pre-processing and processing steps and improve our knowledge of errors. For these purposes, two satellite hyperspectral sensors with similar spatial and spectral characteristics (Hyperion and PRISMA) were compared with corresponding synthetic images, and the city of Venice was selected as the study area. After creating the synthetic images, the errors in the simulation of Hyperion and PRISMA images were evaluated (1.6 and 1.1%, respectively). The same spectral unmixing procedure was performed using real and synthetic images, and their accuracies were compared. The spectral accuracies in root mean square error were equal to 0.017 and 0.016, respectively. In addition, 72.3 and 77.4% of these values were related to sensor characteristics. The spatial accuracies in the mean absolute error were equal to 3.93 and 3.68, respectively. A total of 55.6 and 59.0% of these values were related to sensor characteristics, and 22.6 and 22.3% were related to co-localization and spatial resampling errors. The difference between the radiometric precision values of the sensors was 6.81 and 5.91% regarding the spectral and spatial accuracies of Hyperion image. In conclusion, the results of this study showed that the combined use of two or more real hyperspectral images with similar characteristics and their synthetic images quantifies the weight of hyperspectral sensor characteristics on their image capability and improves our knowledge regarding processing errors, and thus image capability. Full article

The purpose of this study is to analyze the factors that are contributing to the remarkable growth of the m-commerce sector. The article examines eight variables, including socioeconomic (Internet access, mobile users, mobile Internet penetration rates, card payment transactions, consumer confidence, Internet use: selling goods or services) and macroeconomic (GDP and wage), that are considered to influence global m-commerce growth. The Generalized Method of Moments (DPD/GMM) was used to analyze a panel of data that covers the years 2011 through 2020, on a sample of 42 developed and developing countries. The empirical findings show that wages, GDP, consumer confidence index, card payment transactions, mobile users, Internet access and Internet use (selling goods and services) have a positive impact on m-commerce, whereas the mobile Internet penetration rate has a negative impact. Using a sizable and representative panel data set on socioeconomic and macroeconomic indicators, this research advances the state of the art in customer comprehension of these topics. The study’s novel contribution is the incorporation of under-researched m-commerce drivers into empirical analysis. Detailed policy recommendations are provided, with an emphasis on practical, implementable measures to enhance the m-commerce industry. Full article