Spectral Information Research Articles

Feasibility Study of Scanning Spectral Imaging Based on a Birefringence Flat Plate

Hyper-spectral imaging (HSI) systems can be divided into two main types as follows: a group of systems that includes a dedicated dispersion/filtering component whose role is to physically separate the different wavelengths and a group of systems that sample all wavelengths in parallel, so that the separation into wavelengths is performed by signal processing (interferometric method). There is a significant advantage to systems of the second type in terms of the integration time required to obtain a signal with a high signal-to-noise ratio since the signal-to-noise ratio of methods based on scanning interferometry (Windowing method) is better compared to methods based on dispersion. The current research deals with the feasibility study of a new concept for an HSI system that is based on scanning interferometry using the “push-broom” method. In this study, we investigated the viability of incorporating a simple birefringent plate into a scanning optical system. By exploiting the motion of the platform on which the system is mounted, we extracted the spectral information of the scanned region. This approach combines the benefits of scanning interferometry with the simplicity of the setup. According to the theory, a chirped cosine-shaped interferogram is obtained for each wavelength due to the nonlinear behavior of the optical path difference of light in the birefringent plate as a function of the angle. An algorithm converts the signal from a superposition of chirped cosine signals to a scaled interferogram such that Fourier transforming (FT) the interferogram retrieves the spectral information. This innovative idea can turn a simple monochrome camera into a hyperspectral camera by adding a relief lens and a birefringent plate.

Combining spectrum, thermal, and texture features using machine learning algorithms for wheat nitrogen nutrient index estimation and model transferability analysis

The nitrogen nutrition index (NNI) has been extensively applied for the diagnosis of crop nitrogen status, providing insights into efficient nitrogen utilization and plant growth. In this study, we utilized a low-altitude unmanned aerial vehicle (UAV) platform, equipped with multispectral (MS), red–green–blue (RGB), and thermal infrared (TIR) cameras, to comprehensively capture wheat spectral information. The analysis of the relationship between NNI and relative yield revealed an initially linear relationship, which saturated for high NNI values. To enhance accuracy and minimize complexity, we employed a random forest (RF) – recursive feature elimination (RFE) method to select features as inputs for four machine learning (ML) models: back propagation neural network (BPNN), extreme learning machine (ELM), support vector regression (SVR), and Gaussian process regression (GPR). After feature selection, the prediction accuracies of single-sensor models were ranked as: MS > RGB > TIR. The R2 values for the four ML models were in the range of 0.54–0.75. Among multi-sensor combinations, the GPR with MS + RGB + TIR input features achieved the best results with R2 = 0.89 and RPD = 2.52. Further, the dataset was partitioned into six subsets based on location and cultivar variety to evaluate model transferability. The results showed that the transferability largely suffered during the bivariate conditions of different varieties at different locations; the transferability of the model was average improved by 11 % when GPR was combined with transfer component analysis (TCA). The accuracy and transferability of the NNI estimation models significantly improved, offering valuable guidance and methodological support for diagnosing the nitrogen nutrient status of wheat.

Research on Sugarcane Recognition based on Joint Spatial and Spectral Information from Satellite-Based Hyperspectral Imagery

Abstract. The total amount of sugarcane planting in China ranks third in the world, and Guangxi is the largest sugarcane planting base in China. In order to help the Guangxi government and industry better understand the annual planting distribution and scale of sugarcane in the whole region, it is convenient to optimize the planting structure and improve the efficiency of resource utilization, so as to ensure the stable development of Guangxi sugar industry. Based on the satellite-based hyperspectral image data of ' Zhuhai No.1 ', this paper carries out sugarcane recognition and area extraction, which fills the application gap of satellite-based hyperspectral image data in extracting sugarcane area in China. A globally optimized extended random walk model was used to identify and extract sugarcane by combining the spatial-spectral information of hyperspectral imagery. Firstly, the Initial probability estimation is performed using the LOR classifier, and then use the data after MNF dimensionality reduction to construct a weighted graph, and finally the probability optimization and recognition are carried out based on the extended random walk model. The results show that the spatial-spectral joint method based on extended random walk can effectively identify sugarcane, and the sugarcane recognition accuracy of 2021 year image can reach 91.4%, the misrecognition rate is 2.8%; the sugarcane recognition accuracy of 2020 year image can reach 90.5 %, the misrecognition rate is 3.1%.

Soybean Saponin Content Detection Based on Spectral and Image Information Combination

Soybean saponin is a natural antioxidant and is anti-inflammatory. Hyperspectral analysis technology was applied to detect soybean saponin content rapidly and nondestructively in this paper. Firstly, spectral preprocessing methods were studied, and standard normal variable (SNV) was used to remove noise information. Secondly, a two-step hybrid variable selection approach based on synergy interval partial least squares (SiPLS) and iteratively retains informative variables (IRIV) was proposed to extract characteristic variables. Then, the ensemble learning model was constructed by back propagation neural network (BPNN), deep forest (DF), partial least squares regression (PLSR), and extreme gradient boosting (EXG). Finally, image information was combined into spectral data to improve model accuracy. The prediction coefficient of determination (R2) of the final model reached 0.9216. It can provide rapid, nondestructive, and accurate detection technology of soybean saponin content. A combination of spectral and image information will provide a new idea for application of hyperspectral.

A Deep Neural Network for Road Extraction with the Capability to Remove Foreign Objects with Similar Spectra

Abstract. Existing road extraction methods based on deep learning often struggle with distinguishing ground objects that share similar spectral information, such as roads and buildings. Consequently, this study proposes a dual encoder-decoder deep neural network to address road extraction in complex backgrounds. In the feature extraction stage, the first encoder-decoder designed for extracting road features. The second encoder-decoder utilized for extracting building features. During the feature fusion stage, road features and building features are integrated using a subtraction method. The resultant road features, constrained by building features, enhance the preservation of accurate road feature information. Within the feature fusion stage, road feature maps and building feature maps designated for fusion are input into the convolutional block attention module. This step aims to amplify the features of different channels and extract key information from diverse spatial positions. Subsequently, feature fusion is executed using the element-by-element subtraction method. The outcome is road features constrained by building features, thus preserving more precise road feature information. Experimental results demonstrate that the model successfully learns both road and building features concurrently. It effectively distinguishes between easily confused roads and buildings with similar spectral information, ultimately enhancing the accuracy of road extraction.

Spectral Spatial Neighborhood Attention Transformer for Hyperspectral Image Classification

Hyperspectral image (HSI) can provide rich spectral information, which can be helpful for accurate classification in many applications. However, the hyperspectral image classification task has challenges, including limited labeled data, data redundancy, data sparsity, and imbalanced class samples. Over time, various methods have been proposed to solve the above-mentioned problems. To mitigate the issue mentioned above in this paper, we present the neighborhood attention transformer with a channel-wise shift technique for hyperspectral image classification. The neighborhood attention transformer leverages the power of the attention mechanism to capture the spatial relationship between neighboring pixels and extract discriminative features. The channel-wise shift techniques empower the model to adjust the spectral characteristics of each channel adaptively, enhancing its ability to handle the spectral variations present in hyperspectral data. To validate the effectiveness of the proposed model, we conduct comprehensive experiments on the publically available dataset. The results demonstrate that our model consistently outperforms other state-of-the-art methods. The overall accuracy of the proposed model reached 99.41 % , 99.93 % , 98.35 % 99.59 % four datasets.

National-scale nighttime high-temperature anomalies from Landsat-8 OLI images

Identifying and characterizing the distribution, abundance, and structural composition of national high-temperature anomalies (HTAs) are essential for mitigating natural disasters, regulating industrial emissions, and assessing societal impacts. The Landsat-8 Operational Land Imager (OLI) provides a unique capability for monitoring nighttime HTAs (N-HTAs) at high spatial resolutions without contamination from reflected solar radiation. However, the extraction of N-HTAs from nighttime OLI imagery poses significant challenges due to the interference of bright up-light, especially for smaller and cooler hotspots, and the absence of spectral information on backgrounds complicates the classification of N-HTAs at night. Here, we explored the background noise characteristics of Bands 2–7 in nighttime OLI imagery and developed an object-oriented approach for the enhanced detection of small and cool N-HTAs. Furthermore, we introduced a deep learning model that leverages high-resolution aerial imagery and land cover data to categorize N-HTAs into industrial sources, living sources, vegetation fires, and others. Applying these methodologies to nighttime OLI imagery from 2013 to 2020 allowed us to map and classify N-HTAs across the contiguous United States (CONUS), facilitating the creation of a detailed spatiotemporal inventory of industrial N-HTAs. Our analysis revealed that N-HTAs from living sources dominate the total N-HTAs, with a pronounced holiday effect, and gas flaring activities in oil and gas development areas account for >85 % of industrial N-HTAs. The outcomes of this national-scale investigation underscore the significant potential and practical utility of nighttime OLI imagery for N-HTA monitoring and advocate for expanding the spatial and temporal coverage of nighttime OLI observations globally.

Monitoring of Chlorophyll Content of Potato in Northern Shaanxi Based on Different Spectral Parameters

Leaf chlorophyll content (LCC) is an important physiological index to evaluate the photosynthetic capacity and growth health of crops. In this investigation, the focus was placed on the chlorophyll content per unit of leaf area (LCCA) and the chlorophyll content per unit of fresh weight (LCCW) during the tuber formation phase of potatoes in Northern Shaanxi. Ground-based hyperspectral data were acquired for this purpose to formulate the vegetation index. The correlation coefficient method was used to obtain the “trilateral” parameters with the best correlation between potato LCCA and LCCW, empirical vegetation index, any two-band vegetation index constructed after 0–2 fractional differential transformation (step size 0.5), and the parameters with the highest correlation among the three spectral parameters, which were divided into four combinations as model inputs. The prediction models of potato LCCA and LCCW were constructed using the support vector machine (SVM), random forest (RF) and back propagation neural network (BPNN) algorithms. The results showed that, compared with the “trilateral” parameter and the empirical vegetation index, the spectral index constructed by the hyperspectral reflectance after differential transformation had a stronger correlation with potato LCCA and LCCW. Compared with no treatment, the correlation between spectral index and potato LCC and the prediction accuracy of the model showed a trend of decreasing after initial growth with the increase in differential order. The highest correlation index after 0–2 order differential treatment is DI, and the maximum correlation coefficients are 0.787, 0.798, 0.792, 0.788 and 0.756, respectively. The maximum value of the spectral index correlation coefficient after each order differential treatment corresponds to the red edge or near-infrared band. A comprehensive comparison shows that in the LCCA and LCCW estimation models, the RF model has the highest accuracy when combination 3 is used as the input variable. Therefore, it is more recommended to use the LCCA to estimate the chlorophyll content of crop leaves in the agricultural practices of the potato industry. The results of this study can enhance the scientific understanding and accurate simulation of potato canopy spectral information, provide a theoretical basis for the remote sensing inversion of crop growth, and promote the development of modern precision agriculture.

Validation of spectral sleep scoring with polysomnography using forehead EEG device

IntroductionVisual scoring of sleep electroencephalography (EEG) has long been considered the gold standard for sleep staging. However, it has several drawbacks, including high cost, time-intensiveness, vulnerability to human variability, discomfort to patients, lack of visualization to validate the hypnogram, and no acknowledgment of differences between delta and slow oscillation deep sleep. This report highlights a spectral scoring approach that addresses all these shortcomings of visual scoring. Past algorithms have used spectral information to help classify traditional visual stages. The current method used the clearly visible spectral patterns to develop new spectral stages, which are similar to but not the same as visual stages. Importantly, spectral scoring delivers both a hypnogram and a whole-night spectrogram, which can be visually inspected to ensure accurate scoring.MethodsThis study compared traditional visual scoring of 32-channel polysomnography with forehead-only spectral scoring from an EEG patch worn concurrently. The PSG was visually scored by trained technicians and the forehead patch was scored spectrally. Because non-rapid eye movement (NREM) stage divisions in spectral scoring are not based on visual NREM stages, the agreements are not expected to be as high as other automated sleep scoring algorithms. Rather, they are a guide to understanding spectral stages as they relate to the more widely understood visual stages and to emphasize reasons for the differences.ResultsThe results showed that visual REM was highly recognized as spectral REM (89%). Visual wake was only scored as spectral Wake 47% of the time, partly because of excessive visual scoring of wake during Light and REM sleep. The majority of spectral Light (predominance of spindle power) was scored as N2 (74%), while less N2 was scored as Light (65%), mostly because of incorrect visual staging of Lo Deep sleep due to high-pass filtering. N3 was scored as both Hi Deep (13 Hz power, 42%) and Lo Deep (0–1 Hz power, 39%), constituting a total of 81% of N3.DiscussionThe results show that spectral scoring better identifies clinically relevant physiology at a substantially lower cost and in a more reproducible fashion than visual scoring, supporting further work exploring its use in clinical and research settings.

Spectral Superresolution Using Transformer with Convolutional Spectral Self-Attention

Hyperspectral images (HSI) find extensive application across numerous domains of study. Spectral superresolution (SSR) refers to reconstructing HSIs from readily available RGB images using the mapping relationships between RGB images and HSIs. In recent years, convolutional neural networks (CNNs) have become widely adopted in SSR research, primarily because of their exceptional ability to extract features. However, most current CNN-based algorithms are weak in terms of extracting the spectral features of HSIs. While certain algorithms can reconstruct HSIs through the fusion of spectral and spatial data, their practical effectiveness is hindered by their substantial computational complexity. In light of these challenges, we propose a lightweight network, Transformer with convolutional spectral self-attention (TCSSA), for SSR. TCSSA comprises a CNN-Transformer encoder and a CNN-Transformer decoder, in which the convolutional spectral self-attention blocks (CSSABs) are the basic modules. Multiple cascaded encoding and decoding modules within TCSSA facilitate the efficient extraction of spatial and spectral contextual information from HSIs. The convolutional spectral self-attention (CSSA) as the basic unit of CSSAB combines CNN with self-attention in the transformer, effectively extracting both spatial local features and global spectral features from HSIs. Experimental validation of TCSSA’s effectiveness is performed on three distinct datasets: GF5 for remote sensing images along with CAVE and NTIRE2022 for natural images. The experimental results demonstrate that the proposed method achieves a harmonious balance between reconstruction performance and computational complexity.

Hyperspectral image classification based on superpixel merging and broad learning system

AbstractMost spectral–spatial classification methods for hyperspectral images (HSIs) can achieve satisfactory classification results. However, the common problem faced with these approaches is the need for a long training time and sufficient training samples. To address this issue, this study proposes an effective spectral–spatial HSI classification method based on superpixel merging, superpixel smoothing and broad learning system (SMS‐BLS). The newly introduced parameter‐free superpixel merging technique based on local modularity not only enhances the role of local spatial information in classification, but also maintains class boundary information as much as possible. In addition, the spectral and spatial information of HSIs is further fused during the superpixel smoothing process. As a result, with limited training samples, using merged and smoothed superpixels instead of pixels as input to the broad learning system significantly improves its classification performance. Moreover, the merged superpixels weaken the dependence of the classification results on the superpixel segmentation scale. The effectiveness of the proposed method was validated on three HSI benchmarks, namely Indian Pines, Pavia University and Salinas. Experimental and comparative results show the superiority of the method to other state‐of‐the‐art approaches in terms of overall accuracy and running time.

GACDNet:Mapping winter wheat by generative adversarial cross-domain networks with transformer integration for zero-sample extraction

Accurate extraction of winter wheat area is essential for wheat yield estimation. Remotely sensed images have limited coverage, are taken at different times, from different angles and in different geographical areas, and the spectral information of the same feature varies from image to image. Machine learning methods are very popular in extracting winter wheat planting area, there are differences between different images, namely the distribution differences between source domain data and target domain data, the results obtained by these methods directly applied to other areas are not satisfactory. To achieve cross regional extraction of winter wheat area, we propose generative adversarial cross-domain networks for image classification under the same image type. The cross-domain network proposed comprises a generative network and a feature extractor. The generative network creates diverse and acceptable samples from the initial input data, constrained by the contrast loss. The feature extractor converts the initial input data and the generated data into a high-level representation. Two time series datasets were constructed and a series of experiments were conducted on these datasets, obtaining high-precision and optimal classification results. Sample training was performed using 70%, 50%, 30% and 10% of the data on both datasets. The study results demonstrated the high performance and cross-domain capability of our network. Furthermore, the winter wheat cultivation area of the entire Zhoukou city was extracted, yielding results with an accuracy of 94.23%. Extensive experiments and applications on the dataset demonstrate that our method is feasible and reliable.

Full-scale semantic segmentation of hyperspectral imaging based on spatial spatial-spectral joint network

Abstract. Hyperspectral images contain dozens or even hundreds of spectral bands, which contain rich spectral information and help distinguish different ground objects. Hyperspectral images have a wide range of applications in urban planning, environmental monitoring, and other fields. The semantic segmentation of hyperspectral images is one of the current research hotspots. The difficulty lies in the rich spectral information and strong correlation of hyperspectral images. Traditional semantic segmentation methods cannot fully extract information, which affects the accuracy of classification. This article utilizes an encoding decoding structure to simultaneously extract deep and shallow features of images. A REGCS convolution module was constructed using the idea of group convolution to extract spectral and spatial features of images. We compared the Salinas Valley dataset and MUUFL dataset with various classification algorithms. The experimental results show that compared with other classification models, the RESSU model has achieved stable and excellent results in hyperspectral image classification experiments. Among them, in the classification experiment of the Salinas Valley dataset, the accuracy of single class classification reached over 92%. In the effectiveness analysis experiment, we calculated different model parameter quantities to verify the performance of our method, and ultimately achieved good results.

The Molecular Mechanism of FABP4 Inhibition Effects of GAS and 4-HBA in Gastrodia elata Blume Was Discussed Based on NMR and Molecular Docking

To isolate gastrodin (GAS), 4-hydroxybenzyl alcohol (4-HBA), and phenolic compounds from Chinese medicine Gastrodia elata Blume, and to explore the binding mode of fatty acid binding protein 4 (FABP4/aP2) that is closely related to macrophage inflammation, we study their anti-inflammatory targets. After the ultrasonic extraction of the main active components with 70% ethanol, three resins and three eluents were selected, and eight phenolic monomers with similar structures, such as gastrodin and 4-hydroxybenzyl alcohol, were isolated from Gastrodia elata by AB-8 macroporous resin and silica gel column chromatography and eluted with the CHCl3-MeOH gradient. Their structures were identified by HPLC and nuclear magnetic resonance (NMR). The FABP4 protein was added to GAS and 4-HBA, and the NMR experiment was performed to observe ligand binding. Finally, according to the spectral information of STD-NMR and molecular docking technology, the interaction between ligands and protein was studied. The fluorescence competition experiment confirmed that both GAS and 4-HBA were in the binding cavity of FABP4. Moreover, 3-phenoxy-2-phenylbenzoic acid (PPA) is a possible inhibitor of FABP4, reducing macrophage-related inflammation and endoplasmic reticulum stress. This work provides a new basis for the anti-inflammatory mechanism of Gastrodia elata, paving the way for the research and development of FABP4 inhibitor drugs.

Classification model for chlorophyll content using CNN and aerial images

Chlorophyll content is usually used as a quantitative measurement of plant health. The chlorophyll content is also a continuous number of data type, leading to a regression approach when developing the deep learning model. The regression model will predict the chlorophyll content in number format, which requires experts to analyse the outcome. Nevertheless, the analysis of the outcome could possibly lead to human error in diagnosing the plant’s health condition. Therefore, this study proposed a classification approach in developing a deep learning model to analyse the plant’s health condition without human intervention. The classification approach requires a discrete group for dependent variables instead of continuous numbers. When forming the chlorophyll content index groups in this study, which are low, optimum and high levels, two research studies were combined to form the groups, which were (1) the product of the standard range of nitrogen value in mango plant and (2) the correlation analysis between nitrogen value and chlorophyll content index. The classification model in this study used transfer learning algorithms, which were InceptionV3, DenseNet121 and ResNet50, with the canopy-scale level of mango plant RGB images with complex leaf structures in an uncontrolled and open area. Based on the findings, the classification model could classify the chlorophyll content index levels on both mango plant images, which were infected and not infected with black sooty mould. The finding also shows that a clearer distribution pattern of spectral information extracted from the mango plant images can influence the performance result of the classification model. Besides that, the starting point of the Digitization Footprint for this study site across the development stages of the classification model was 308.5756 MB/ha. Finally, the overall accuracy performances for the classification models that used the transfer learning algorithms, which were InceptionV3, DenseNet121, and ResNet50, and trained using the images of the mango plant infected with pest were 96.49 %, 92.98 %, and 89.47 %, respectively, and for using the images of the mango plant not infected with pest were 88.10 %, 78.57 %, and 69.05 %, respectively.

Extreme-ultraviolet (EUV) observables of simulated plasmoid-mediated reconnection in the solar corona

Understanding the role of magnetic reconnection in the heating and dynamics of the solar atmosphere requires detailed observational data of any observable aspect of the reconnection process, including small-scale features such as plasmoids. Here, we examine the capability of active and upcoming instruments to detect plasmoids generated by reconnection in the corona including low-density regimes. We used the Bifrost code to perform simulations of plasmoid-mediated reconnection in the corona with a 2D idealized setup: a fan-spine topology with uniform density including thermal conduction. Through a forward-modeling of extreme-ultraviolet (EUV) observables, we checked whether our simulated plasmoids could be detected with the instruments of Solar Dynamics Observatory (SDO) and Solar Orbiter (SO), as well as the upcoming Multi-Slit Solar Explorer (MUSE) and Solar-C missions. Short-lived ($ 10-20$ s) small-scale ($ 0.2-0.5$ Mm) coronal plasmoids are not resolvable with the Atmospheric Imaging Assembly (AIA) on board SDO. In contrast, they could be captured with the EUV High-Resolution Imager at the Extreme Ultraviolet Imager (EUI-HRI$_ EUV $) of SO. The spatial and temporal high-resolution planned for the MUSE spectrograph (SG) is adequate to obtain full spectral information of these plasmoids. To achieve a sufficient signal-to-noise ratio (S/N) for sim 0.8 MK plasmoids in the MUSE/SG 171 channel, full-raster images are attainable for regions with electron densities above $10^9\ cm^ $, while sit-and-stare observations are recommended for lower-density regions. The future Solar-C mission could also capture these coronal plasmoids using the EUV High-Throughput Spectroscopic Telescope (EUVST), considering rapid changes in Doppler shift and line widths in different EUV lines caused by plasmoid motions along the current sheet. With the combined spectra of MUSE/SG and Solar-C/EUVST in multiple emission lines, along with high-resolution images from SO/EUI-HRI$_ EUV $ and MUSE/CI, it should be possible to gain new insights about plasmoid formation in the corona.

A joint model based on graph and deep learning for hyperspectral anomaly detection

Through the years, graph theory has gradually been applied in hyperspectral image (HSI) processing. The graph theory method does not need to consider the structural characteristics of the dataset and has achieved satisfactory application results. However, the current graph theory methods applied in HSI processing mainly focus on spectral characteristics. This paper proposes a joint model based on graph and deep learning (JGD) for hyperspectral anomaly detection (AD). The proposed JGD consists of a spectral sub-model using graph and a spatial sub-model using deep learning. In the spectral sub-model, graph Fourier transform (GFT) is used, and the reconstruction errors between the original HSI and the HSI after GFT are mapped to fractional Fourier domain (FrFD) for AD results, which mainly makes use of the global spectral information. In the spatial sub-model, stacked autoencoders (SAEs) and an adaptive algorithm are combined to obtain AD results, which mainly use local spatial information and spectral information. These two sub-models are combined through the differential fusion method to form the JGD, which fully utilizes spectral and spatial information of hyperspectral images (HSIs). Compared with seven other algorithms on four real HSIs, the proposed JGD shows superior AD performance.

MOD-YOLO: Multispectral object detection based on transformer dual-stream YOLO

Multispectral object detection can effectively improve the precision of object detection in low-visibility scenes, which increases the reliability and stability of the object detection application in the open environment. Cross-Modality Fusion Transformer (CFT) can effectively fuse different spectral information, but this method relies on large models and expensive computing resources. In this paper, we propose multispectral object detection dual-stream YOLO (MOD-YOLO), based on Cross Stage Partial CFT (CSP-CFT), to address the issue that prior studies need heavy inference calculations from the recurrent fusing of multispectral features. This network can divide the fused feature map into two parts, respectively for cross stage output and combined with the next stage feature, to achieve the correct speed/memory/precision balance. To further improve the accuracy, SIoU was selected as the loss function. Ultimately, extensive experiments on multiple publicly available datasets demonstrate that our model, which achieves the smallest model size and excellent performance, produces better tradeoffs between accuracy and model size than other popular models.

Enhancements in Immediate Speech Emotion Detection: Harnessing Prosodic and Spectral Characteristics

Speech is essential to human communication for expressing and understanding feelings. Emotional speech processing has challenges with expert data sampling, dataset organization, and computational complexity in large-scale analysis. This study aims to reduce data redundancy and high dimensionality by introducing a new speech emotion recognition system. The system employs Diffusion Map to reduce dimensionality and includes Decision Trees and K-Nearest Neighbors(KNN)ensemble classifiers. These strategies are suggested to increase voice emotion recognition accuracy. Speech emotion recognition is gaining popularity in affective computing for usage in medical, industry, and academics. This project aims to provide an efficient and robust real-time emotion identification framework. In order to identify emotions using supervised machine learning models, this work makes use of paralinguistic factors such as intensity, pitch, and MFCC. In order to classify data, experimental analysis integrates prosodic and spectral information utilizing methods like Random Forest, Multilayer Perceptron, SVM, KNN, and Gaussian Naïve Bayes. Fast training times make these machine learning models excellent for real-time applications. SVM and MLP have the highest accuracy at 70.86% and 79.52%, respectively. Comparisons to benchmarks show significant improvements over earlier models.

Estimating turbidity concentrations in highly dynamic rivers using Sentinel-2 imagery in Google Earth Engine: Case study of the Godavari River, India.

Turbidity is an essential biogeochemical parameter for water quality management because it shapes the physical landscape and regulates ecological systems. It varies spatially and temporally across large water bodies, but monitoring based on point-source field observations remains a difficult task in developing countries due to the need for logistics and costs. In this study, we present a novel semi-analytical approach for estimating turbidity from remote sensing reflectance in moderate to highly turbid waters in the lower part of the Godavari River (i.e., locations near Rajahmundry). The proposed method includes two sub-algorithms-Normalized Difference Turbidity Index (NDTI) and semi-empirical single-band turbidity ( ) algorithm-to retrieve spectral reflectance information corresponding to the study locations for turbidity modeling. Sentinel-2 Multi-Spectral Imager data have been used to quantify the turbidity in the Google Earth Engine (GEE) platform. The correlation analysis was observed between spectral reflectance values and in situ turbidity data using cubic polynomial regression equations. The results indicated that the , which uses the only red-edge wavelength, identified turbidity as the most accurate across all locations (highest R2 = 0.91, lowest RMSE = 0.003), followed by NDTI (highest R2 = 0.85, lowest RMSE = 0.05), respectively. The remote sensing data application provides a better way to monitor turbidity at large spatio-temporal scales in attaining the water quality standards of the Godavari River.