Spectral Classification Method Research Articles

Accurate Coal Classification Using PAIPSO-ELM with Near-Infrared Reflectance Spectroscopy.

China has vast proven coal reserves, encompassing a wide variety of types. However, traditional coal classification methods have limitations, often leading to inaccurate classification and inefficient utilization of coal resources. To address this issue, this paper introduces the Extreme Learning Machine (ELM) as a novel coal classification method, based on the near-infrared reflectance spectroscopy (NIRS) of coal. Initially, we collected NIRS data from coal samples using the SVC-HR-1024 spectrometer. Given the high dimensionality and strong linear correlations in NIRS data, we conducted preprocessing to enhance the usefulness of the data. In experiments, the ELM model demonstrated good classification performance. However, due to the random generation of input layer weights and hidden layer biases in the ELM model, its performance can be unstable, preventing the model from fully realizing its potential. To overcome this shortcoming, we employed the Particle Swarm Optimization (PSO) algorithm to optimize the parameters of the ELM model. Simulation results showed that the PSO-ELM model achieved a 9.68% improvement in classification accuracy compared to the original ELM model. Furthermore, we optimized the PSO algorithm by introducing exponentially decaying inertia factors and position-variant particles to further reduce the risk of the algorithm falling into local optima. The improved Position-Adaptive Inertia PSO-ELM (PAIPSO-ELM) model achieved an additional 2% increase in classification accuracy over the PSO-ELM model, without a significant increase in training time. In summary, this paper proposes a coal spectral classification method based on the PAIPSO-ELM model, effectively overcoming the limitations of traditional classification methods while meeting industrial demands for classification accuracy and speed.

Geochemical characteristics and mapping of Reşadiye (Tokat-Türkiye) bentonite deposits using machine learning and sub-pixel mixture algorithms

Reşadiye bentonite deposits, which play a significant role in Türkiye's bentonite production, are situated in Central Anatolia. Geochemical, mineralogical, and remote sensing data have been integrated to map the spatial distribution of clay minerals in the bentonite deposits and argillic areas. It is hypothesized that the bentonite samples occurred by the in-situ diagenetic alteration of rhyolite-dacite, trachyte, and andesite/basaltic andesitic composition pyroclastic rocks (ash-flow tuff). Biotite, clinoptilolite, calcite, dolomite, K-feldspar, opal-CT, quartz, and clay minerals are detected in most bentonite samples. The clay patterns determined in the bentonite samples in the X-ray diffraction (XRD) diagrams were 12.3–12.6 Å and were interpreted as being rich in Na-smectites. Mineral mapping in these deposits is essential for mining operations since the high-grade bentonite deposits can be affected by the other clay, gang, and ore minerals they contain in addition to the smectite. The sample spectra measurements matched montmorillonite and kaolin/smectite spectra. This study tests support vector machine (SVM) and artificial neural network (ANN) machine learning and MTMF subpixel algorithms in lithology and mineral mapping in Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite data. It combines the power of subpixel unmixing algorithms to determine the distribution of clay and high-grade bentonites in argillic areas discriminated by machine learning. The results showed that the SVM algorithm can map better than ANN for argillic areas. Additionally, the distribution of high-grade bentonite and kaolin/smectite bearing sites in the study area is discriminated by the mixture-tuned matched filtered (MTMF) spectral classification method. As a result, this study shows that remote sensing studies can be utilized for the exploration and monitoring of high-grade bentonite sites during and/or post-mining operations.

Lyman-α at Cosmic Noon II: The relationship between kinematics and Lyman-α in z ∼ 2–3 Lyman break galaxies

Abstract We report for the first time a relationship between galaxy kinematics and net Lyman- $\alpha$ equivalent width (net Ly $\alpha$ EW) in star-forming galaxies during the epoch of peak cosmic star formation. Building on the previously reported broadband imaging segregation of Ly $\alpha$ -emitting and Ly $\alpha$ -absorbing Lyman break galaxies (LBGs) at $z\sim2$ (Paper I in this series) and previously at $z\sim3$ , we use the Ly $\alpha$ spectral type classification method to study the relationship between net Ly $\alpha$ EW and nebular emission-line kinematics in samples of $z\sim2$ and $z\sim3$ LBGs drawn from the literature for which matching rest-frame UV photometry, consistently measured net Ly $\alpha$ EWs, and kinematic classifications from integral field unit spectroscopy are available. We show that $z\sim2$ and $z\sim3$ LBGs segregate in colour-magnitude space according to their kinematic properties and Lyman- $\alpha$ spectral type and conclude that LBGs with Ly $\alpha$ dominant in absorption (aLBGs) are almost exclusively rotation-dominated (presumably disc-like) systems, and LBGs with Ly $\alpha$ dominant in emission (eLBGs) characteristically have dispersion-dominated kinematics. We quantify the relationship between the strength of rotational dynamic support (as measured using ${v}_{\mathrm{obs}}/2{\sigma }_{\mathrm{int}}$ and ${v}_{\mathrm{rot}}/{\sigma}_{\mathrm{0}}$ ) and net Ly $\alpha$ EW for subsets of our kinematic sample where these data are available, and demonstrate the consistency of our result with other properties that scale with net Ly $\alpha$ EW and kinematics. Based on these findings, we suggest a method by which large samples of rotation- and dispersion-dominated galaxies might be selected using broadband imaging in as few as three filters and/or net Ly $\alpha$ EW alone. If confirmed with larger samples, application of this method will enable an understanding of galaxy kinematic behaviour over large scales in datasets from current and future large-area and all-sky photometric surveys that will select hundreds of millions of LBGs in redshift ranges from $z\sim2-6$ across many hundreds to thousands of Mpc. Finally, we speculate that the combination of our result linking net Ly $\alpha$ EW and nebular emission-line kinematics with the known large-scale clustering behaviour of Ly $\alpha$ -absorbing and Ly $\alpha$ -emitting LBGs is evocative of an emergent bimodality of early galaxies that is consistent with a nascent morphology-density relation at $z\sim2-3$ .

Galaxy spectral classification and feature analysis based on convolutional neural network

ABSTRACT Emission-line galaxy classification plays an important role in comprehending the formation and evolution of galaxies. The widely used optical spectral classification method for galaxies is the BPT diagram, which classifies emission-line galaxies on the basis of precise spectral line measurements. Various classical machine learning methods have been utilized to classify galaxy spectra. Deep learning (DL) is more feasible for a huge amount of data, as it can learn patterns autonomously from the original data. This study aims to explore the possibility of applying DL to classify galaxy spectra and improve classification efficiency. A one-dimensional convolutional neural network model called GalSpecNet was constructed to classify emission-line galaxy spectra, which recognizes star-forming, composite, active galactic nucleus (AGN), and normal galaxies with an accuracy of over 93 per cent. This study employs the Gradient-weighted Class Activation Mapping to elucidate the decision-making process of the model by inspecting spectral features that the model prioritizes for each type of galaxy. The findings suggest that the model considers features highly consistent with the conventional BPT method. Subsequently, we applied the model to the cross-matched galaxies of Sloan Digital Sky Survey Data Release 16 (DR16) and Large Sky Area Multi-Object Fiber Spectroscopic Telescope DR8 and present a catalogue comprising of 41 699 star-forming candidates and 55 103 AGN candidates. The catalogue is publicly available.

Indian Film Songs Genre Wise Classification Based on Spectral Features

The classification of musical genres has received a lot of attention in recent years due to the several uses it can serve in music information retrieval. Additionally, since genres frequently co-occur in music tracks in Indian Film Songs, it is desirable to record and model the genre correlations in order to enhance the effectiveness of multi-label music genre classification. Present a novel multi-modal strategy that uses a music spectral feature-based classification method to address these problems. In addition, a genre correlations extraction module is offered to identify and model probable genre correlations based on the characteristics of the proposed approach.

Land Cover Classification from Hyperspectral Images via Weighted Spatial–Spectral Joint Kernel Collaborative Representation Classifier

The continuous changes in Land Use and Land Cover (LULC) produce a significant impact on environmental factors. Highly accurate monitoring and updating of land cover information is essential for environmental protection, sustainable development, and land resource planning and management. Recently, Collaborative Representation (CR)-based methods have been widely used in land cover classification from Hyperspectral Images (HSIs). However, most CR methods consider the spatial information of HSI by taking the average or weighted average of spatial neighboring pixels of each pixel to improve the land cover classification performance, but do not take the spatial structure information for pixels into account. To address this problem, a novel Weighted Spatial–Spectral Joint CR Classification (WSSJCRC) method is proposed in this paper. WSSJCRC only performs spatial filtering on HSI through a weighted spatial filtering operator to alleviate the spectral shift caused by adjacency effect, but also utilizes the labeled training pixels to simultaneously represent each test pixel and its spatial neighborhood pixels to consider the spatial structure information of each test pixel to assist the classification of the test pixel. On this basis, the kernel version of WSSJCRC (i.e., WSSJKCRC) is also proposed, which projects the hyperspectral data into the kernel-induced high-dimensional feature space to enhance the separability of nonlinear samples. The experimental results on three real hyperspectral scenes show that the proposed WSSJKCRC method achieves the best land cover classification performance among all the compared methods. Specifically, the Overall Accuracy (OA), Average Accuracy (AA), and Kappa statistic (Kappa) of WSSJKCRC reach 96.21%, 96.20%, and 0.9555 for the Indian Pines scene, 97.02%, 96.64%, and 0.9605 for the Pavia University scene, and 95.55%, 97.97%, and 0.9504 for the Salinas scene, respectively. Moreover, the proposed WSSJKCRC method obtains the promising accuracy with OA over 95% on the three hyperspectral scenes under the situation of small-scale labeled samples, thus effectively reducing the labeling cost for HSI.

A multi-kernel channel attention combined with convolutional neural network to identify spectral information for tracing the origins of rice samples.

Rice is a primary food consumed daily by many people, and different samples of rice often show disparate quality levels due to different production environments. In the rice market, it is common to sell low-quality rice with high-quality origin labels. As a nondestructive testing technology, spectral analysis has been widely used in food quality supervision. In this work, a deep learning method was developed and combined with a hyperspectral imaging system to achieve a quality-based identification of rice samples from different origins. First, the hyperspectral system was used to obtain spectral information of rice samples from five different origins. Then, a multi-kernel channel attention (MKCA) was proposed to focus on the deep features of the spectral information. Finally, based on the classical deep learning network, combined with MKCA, the spectral characteristics of rice samples from different origins were effectively identified. The results showed that MKCA combined with the LeNet-5 network structure achieved 97.40% accuracy, 97.63% precision, 97.78% recall, and 97.70% F1-score. It provides an effective technical method for tracing rice.

ŞAPHANE (ÇORUM) DAMAR TİPİ ALTIN CEVHERLEŞMESİNDE MULTİSPEKTRAL UYDU GÖRÜNTÜLERİ KULLANILARAK HİDROTERMAL ALTERASYON MİNERAL HARİTALAMASI VE ÇİZGİSELLİK ANALİZİ

Remote sensing techniques provide very useful data for mapping lithological features and minerals, and also revealing structural features. In particular, mapping the distributions of hydrothermal alteration minerals, which is one of the first exploration stages of metallic ore deposits, with less cost and in a short time reveals very important results. In the study area, Eocene volcanic rocks and alterations caused by hydrothermal fluids activities are intensely observed. These intense alterations are associated with gold-bearing hydrothermal veins in approximately NE-SW direction. This area, which has a high gold potential, has a steep topography and is difficult to access. Field studies in the region are carried out with difficulty and many areas can be overlooked during the studies. The mapping of distribution of hydrothermal alteration minerals was carried out by applying the spectral classification method with image enhancement techniques such as vegetation masking (NDVI), and band ratio in ASTER data in this study. In addition, automatic lineament analysis was applied to the 15 m resolution image obtained from the Landsat-8 OLI data for the detection of vein-type mineralizations. The results obtained have an overall accuracy of 84.8 and a kappa coefficient of 0.836, showing high accuracy with field data.It is suggested that the maps obtained in the study will provide useful and advanced data for new gold and/or other precious metallic mineralizations in the region.

A Neural Network-Based Spectral Approach for the Assignment of Individual Trees to Genetically Differentiated Subpopulations

Studying population structure has made an essential contribution to understanding evolutionary processes and demographic history in forest ecology research. This inference process basically involves the identification of common genetic variants among individuals, then grouping the similar individuals into subpopulations. In this study, a spectral-based classification of genetically differentiated groups was carried out using a provenance–progeny trial of Eucalyptus cladocalyx. First, the genetic structure was inferred through a Bayesian analysis using single-nucleotide polymorphisms (SNPs). Then, different machine learning models were trained with foliar spectral information to assign individual trees to subpopulations. The results revealed that spectral-based classification using the multilayer perceptron method was very successful at classifying individuals into their respective subpopulations (with an average of 87% of correct individual assignments), whereas 85% and 81% of individuals were assigned to their respective classes correctly by convolutional neural network and partial least squares discriminant analysis, respectively. Notably, 93% of individual trees were assigned correctly to the class with the smallest size using the spectral data-based multi-layer perceptron classification method. In conclusion, spectral data, along with neural network models, are able to discriminate and assign individuals to a given subpopulation, which could facilitate the implementation and application of population structure studies on a large scale.

Spectral Matching Classification Method of Multi-State Similar Pigments Based on Feature Differences

The properties of the same pigments in murals are affected by different concentrations and particle diameters, which cause the shape of the spectral reflectance data curve to vary, thus influencing the outcome of matching calculations. This paper proposes a spectral matching classification method of multi-state similar pigments based on feature differences. Fast principal component analysis (FPCA) was used to calculate the eigenvalue variance of pigment spectral reflectance, then applied to the original reflectance values for parameter characterization. We first projected the original spectral reflectance from the spectral space to the characteristic variance space to identify the spectral curve. Secondly, the relative distance between the eigenvalues in the eigen variance space is combined with the JS (Jensen-Shannon) divergence to express the difference between the two spectral distributions. The JS information divergence calculates the relative distance between the eigenvalues. Experimental results show that our classification method can be used to identify the spectral curves of the same pigment under different states. The value of the root means square error (RMSE) decreased by 12.0817, while the mean values of the mean absolute percentage error (MAPE) and R2 increased by 0.0965 and 0.2849, respectively. Compared with the traditional spectral matching algorithm, the recognition error was effectively reduced.

Stellar Spectral Classification Based on Capsule Network

The rapid development of large-scale sky survey project has produced a large amount of stellar spectral data, which make the automatic classification of stellar spectral data a challenging task. In this paper, we have proposed a stellar spectral classification method based on a capsule network. At first, by using the one-dimensional convolutional network and short-time Fourier transform (STFT), the one-dimensional spectra of the F5, G5, and K5 types selected from the LAMOST Data Release 5 (DR5) are converted into the two-dimensional Fourier spectrum images. Then, the two-dimensional Fourier spectrum images are classified automatically by the capsule network. Because the capsule network can preserve the hierarchical pose relationships among the entities in the image, and it does not need any pooling layers, the experimental results show that the capsule network has a better classification performance, for the classifications of the F5, G5, and K5-type stellar spectra, its classification accuracy is superior to other classification methods.

Rapid and accurate identification of colon cancer by Raman spectroscopy coupled with convolutional neural networks

Colonoscopy is regarded as the gold standard in colorectal tumor diagnosis, but it is costly and time-consuming. Raman spectroscopy has shown promise for differentiating cancerous from non-cancerous tissue and is expected to be a new tool for oncological diagnosis. However, traditional Raman spectroscopy analysis requires tedious preprocessing, and the classification accuracy needs to be improved. In this work, a novel Raman spectral qualitative classification method based on convolutional neural network (CNN) is proposed for the identification of three different colon tissue samples, including adenomatous polyp, adenocarcinoma and normal tissues. Experimental results show that this CNN model has superior feature extraction ability. For the spectral data of new individuals, the trained CNN model presents much better classification performance than traditional machine learning methods, such as the k-nearest neighbor, random forest, and support vector machine. Raman spectroscopy combined with CNN can be used as an effective auxiliary tool for the early diagnosis of colon cancer.

One case study shows an important phenomenon: Active fault can cause subtle spectral features change of soil

Buried fault identification in plains is one of the important and difficult problems in current active fault research. Seismic activity forces crustal fluid to migrate up, especially along faults, thereby altering the geochemical characteristics of faults. Hyperspectral remote sensing has an extremely high spectral resolution; data derived from hyperspectral remote sensing have the capacity to detect physicochemical characteristics for mining or the subtle recognition of different underground objects. It provides a new method to detect changes in the geochemical characteristics in the fault. This study is trying to locate the fault by spectrum feature. The Xiadian fault was chosen as the study area. Several samples across the fault were collected, and these samples were measured by an ASD Field Spec 3 spectrometer(Analytical Spectral Devices, Boulder, USA, produced in 2008). Continuum removal, the spectral classification method and the wavelet transform technique have been used for spectral analysis. It has been shown that the spectrum of soil in the fault zone has some special characteristics that are different from those of the spectrum away from fault. Four special spectral regions, 550–580 ​nm, 600–700 ​nm, 700–800 ​nm and 800–900 ​nm, have been extracted as the singular ranges. These individual ranges could be used for fault location in the plain area. There is weak absorption near 880 ​nm of the sample in the fault, indicating that Fe3+ content plays an important role in fault identification and that the iron valences can indicate the fault environment. This study also found that the spectral anomalies in space could reflect the intensity of active tectonics to a certain extent. Therefore, spectral features have the potential to identify a buried faults. This study is the first to use spectroscopic techniques to analyze active tectonics, and there is a new discovery. This new discovery provides the basis for the application of remote sensing in the study of active tectonics.

A New Method to Classify Type IIP/IIL Supernovae Based on Their Spectra

Abstract Type IIP and Type IIL supernovae are defined on the basis of their light curves, but the spectral criteria for distinguishing these two types of supernovae (SNe) remain unclear. We propose a spectral classification method. First, we subtract the principal components of different wavelength bands in the spectra based on the functional principal components analysis method. Then, we use support vector machine and artificial neural network to classify these two types of SNe. The best F1_Score of our classifier is 0.871 for SNe IIL, and 0.974 for SNe IIP. We found that by only using the H α line at 6150–6800 Å for classification, the F1_Score up to 0.961 for Type IIP, and 0.818 for Type IIL SNe can be obtained. These results indicate that the profile of the H α spectral line is the key to distinguishing the two types of SNe.

The young stellar content of the giant H II regions M 8, G333.6−0.2, and NGC 6357 with VLT/KMOS

Context.The identification and characterisation of populations of young massive stars in (giant) H IIregions provides important constraints on (i) the formation process of massive stars and their early feedback on the environment, and (ii) the initial conditions for population synthesis models predicting the evolution of ensembles of stars.Aims.We identify and characterise the stellar populations of the following young giant H IIregions: M 8, G333.6−0.2, and NGC 6357.Methods.We have acquiredH- andK-band spectra of around 200 stars using theK-band Multi Object Spectrograph on the ESO Very Large Telescope. The targets for M 8 and NGC 6357 were selected from the Massive Young Star-Forming Complex Study in Infrared and X-ray (MYStIX), which combines X-ray observations with near-infrared (NIR) and mid-infrared data. For G333.6−0.2, the sample selection is based on the NIR colours combined with X-ray data. We introduce an automatic spectral classification method in order to obtain temperatures and luminosities for the observed stars. We analysed the stellar populations using their photometric, astrometric, and spectroscopic properties and compared the position of the stars in the Hertzprung-Russell diagram with stellar evolution models to constrain their ages and mass ranges.Results.We confirm the presence of candidate ionising sources in the three regions and report new ones, including the first spectroscopically identified O stars in G333.6−0.2. In M 8 and NGC 6357, two populations are identified: (i) OB main-sequence stars (M > 5M⊙) and (ii) pre-main sequence stars (M ≈ 0.5 − 5 M⊙). The ages of the clusters are ∼1−3 Myr, < 3 Myr, and ∼0.5−3 Myr for M 8, G333.6−0.2, and NGC 6357, respectively. We show that MYStIX selected targets have > 90% probability of being members of the H IIregion, whereas a selection based on NIR colours leads to a membership probability of only ∼70%.

Local Linear Spatial–Spectral Probabilistic Distribution for Hyperspectral Image Classification

A key challenge in hyperspectral image (HSI) classification is how to effectively utilize the spectral and spatial information of limited labeled training samples in the data set. In this article, a new spatial–spectral combined classification method, termed local linear spatial-spectral probabilistic distribution (LSPD), has been proposed on the basis of local geometric structure and spatial consistency of HSI. LSPD extracts discriminating spatial–spectral information from limited labeled training samples and their spatial–spectral neighbors. Then, it constructs a multiclass probability map by exploiting the local linear representation and spatial information of HSI. Finally, the spatial–spectral weighted reconstruction has been performed on the probability map, and the class of test sample can be predicted by the maximum value of LSPD. LSPD not only exploits spectral information to discover more intrinsic properties of the labeled training data but also utilizes the spatial relationship between samples to effectively improve discriminating power for classification. Experimental results on the Indian Pines, PaviaU, and HoustonU hyperspectral data sets demonstrate that the proposed LSPD method possesses better classification performance by comparing with some state-of-the-art classifiers.

Influence of species richness, evenness, and composition on optical diversity: A simulation study

While remote sensing has increasingly been applied to estimate α biodiversity directly through optical diversity, there is a need to better understand the mechanisms behind the optical diversity-biodiversity relationship. Here, we examined the relative contributions of species richness, evenness, and composition to the spectral reflectance, and consider factors confounding the remote estimation of species diversity in a prairie ecosystem experiment at Cedar Creek Ecosystem Science Reserve, Minnesota. We collected hyperspectral reflectance of 16 prairie species using a tram-mounted imaging spectrometer, and a full-range field spectrometer with a leaf clip, and simulated plot-level images from both instruments with different species richness, evenness and composition. Two optical diversity metrics were explored: the coefficient of variation (CV) of spectral reflectance in space and classified species derived from Partial Least Squares Discriminant Analysis (PLS-DA), a spectral classification method. Both optical diversity metrics (CV and PLS-DA classified species) were affected by species richness and evenness. Diversity metrics that combined species richness and evenness together (e.g. Shannon's index) were more strongly correlated with optical diversity than either metric alone. Image-derived data were influenced by both leaf traits and canopy structure and showed larger spectral variability than leaf clip data, indicating that sampling methods influence optical diversity. Leaf and canopy traits both contributed to optical diversity, sometimes in complex or contradictory ways. Large within-species variation sometimes confounded biodiversity estimation from optical diversity, and a single species markedly altered the optical-biodiversity relationship. Biodiversity estimation from CV was strongly influenced by soil background, while estimation from PLS-DA classified species was not sensitive to soil background. These findings are consistent with recent empirical studies and demonstrate that modeling approaches can be used to explore effects of spatial scale and guide regional studies of biodiversity estimation using high spatial and spectral resolution remote sensing.

Detecting Clay Minerals in Hydrothermal Alteration Areas with Integration of ASTER Image and Spectral Data in Kösedag-Zara (Sivas), Turkey

ABSTRACT Remote sensing technology and its terrestrial components are more useful than classical geological investigation in mineral exploration and mapping the hydrothermal alteration areas and help to investigate larger areas in short time. Intrusive and volcanic rocks, namely Kösedag syenite and Karatas volcanics in Kösedag (Zara) area shows argillic alteration zones. Two different test area were chosen and sampled for mineralogical studies. XRD-CF investigations showed that kaolinite and illite are the dominant clay minerals in test areas of A and B respectively. The spectroradiometer measurements were carried out 5 times on different surfaces of clayey samples with self-illuminated contact-probe lens within the wavelength range of 350-2500 nm. The spectroradiometer measurements used as endmember were resampled to ASTER short wave infrared (SWIR) bandwidths. Band ratio, principal component and decorrelation stretching analysis were performed to visualize the distribution of clay minerals. In spectral classification method, matched filtering (MF) was used for integrating the satellite image and spectroradiometer measurement data. It was concluded that co-interpretations of the band ratio, principal component, decorrelation stretching analysis, MF results and geological map are very useful in determining, classifying and mapping of the argillic alteration zones related to hydrothermal processes on ASTER image and they seem to be very useful to identify the target areas for mineral exploration.

Spectral Feature-Based Classification of Wind Profiler Power Spectra

Wind profilers (WPs) are coherent pulsed Doppler radars operating in the ultrahigh frequency and very high frequency bands. These atmospheric radars receive backscattered signals from the atmospheric and nonatmospheric targets. Atmospheric targets include clear air turbulence, precipitation, mesospheric turbulence, ionospheric D, E, F regions, and meteors, whereas nonatmospheric targets are objects like airplanes, birds, insects, hills, and so on. Modern WPs operate for long hours and often change radar operating parameters. Echoes from one beam direction are observed for 8 to 32 s and a set of Doppler power spectra is generated. WPs generate more than a hundred sets of Doppler power spectra per hour. These Doppler spectra often contain echoes from more than one target. The studies for atmospheric modeling and prediction require analysis of the backscattered signal. In order to facilitate systematic study, the data need to be classified and archived according to the atmospheric target type. Considering the large data volume consisting of echoes from different atmospheric targets, there is a need of an automated classification technique that segregates the Doppler power spectral data according to the types of atmospheric targets. The technique is expected to operate in real time with the data generation. This paper presents a spectral feature-based classification (SFBC) method for the classification of Doppler power spectra. This method associates three to four descriptor spectral features with each type of atmospheric target. The SFBC method classifies the data into a particular type of the atmospheric target if concurrent occurrence of descriptor features corresponding to that target type is observed. This paper presents the results indicating that the SFBC classifies the data from different WP radars with good accuracy. It also shows that the SFBC method is computationally simpler compared with other established classification methods.

Massive Stars in the SDSS-IV/APOGEE SURVEY. I. OB Stars

Abstract In this work, we make use of DR14 APOGEE spectroscopic data to study a sample of 92 known OB stars. We developed a near-infrared semi-empirical spectral classification method that was successfully used in case of four new exemplars, previously classified as later B-type stars. Our results agree well with those determined independently from ECHELLE optical spectra, being in line with the spectral types derived from the “canonical” MK blue optical system. This confirms that the APOGEE spectrograph can also be used as a powerful tool in surveys aiming to unveil and study a large number of moderately and highly obscured OB stars still hidden in the Galaxy.