Classification of Spectra of Emission Line Stars Using Machine Learning Techniques

Bromová, Pavla; Škoda, Petr; Vážný, Jaroslav

doi:10.1007/s11633-014-0789-2

Cited by 11 publications

(4 citation statements)

References 14 publications

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“…According to the comparison results, SVMs obtained a relatively high accuracy among all testing classifers. From the literature review [13][14][15] , SVMs is also quite effective in coping classification problem with a small dataset. In addition, SVM is one non-parametric statistical learning algorithm, where no particular assumption should be made on data distribution [26] .…”

Section: Classifier Selection: Svmsmentioning

confidence: 99%

“…For example, the support vector machines (SVMs) has been applied to solve remote sensing applications regarding unmanned aerial vehicles hyperspectral image (HSI) classification and satellite image analysis. In comparison with many existing classifiers such as neural network, SVMs classifier can achieve a competitive performance even with small training samples [13][14][15][16] . This property is extremely attractive for precision agriculture applications, since getting ground truth data is expensive, labour and time-consuming, involving filed survey and lab experiment test.…”

Section: Introductionmentioning

confidence: 99%

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Potential Bands of Sentinel-2A Satellite for Classification Problems in Precision Agriculture

Zhang

Liu

2018

Int. J. Autom. Comput.

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Various indices are used for assessing vegetation and soil properties in satellite remote sensing applications. Some indices, such as normalized difference vegetation index (NDVI) and normalized difference water index (NDWI), are capable of simply differentiating crop vitality and water stress. Nowadays, remote sensing capabilities with high spectral, spatial and temporal resolution are available to analyse classification problems in precision agriculture. Many challenges in precision agriculture can be addressed by supervised classification, such as crop type classification, disease and stress (e.g., grass, water and nitrogen) monitoring. Instead of performing classification based on designated indices, this paper explores direct classification using different bands information as features. Land cover classification by using the recently launched Sentinel-2A image is adopted as a case study to validate our method. Four approaches of featured band selection are compared to classify five classes (crop, tree, soil, water and road) with the support vector machines (SVMs) algorithm, where the first approach utilizes traditional empirical indices as features and the latter three approaches adopt specific bands (red, near infrared and short wave infrared) related to indices, specific bands after ranking by mutual information (MI), and full bands of on-board sensors as features, respectively. It is shown that a better classification performance can be achieved by directly using the selected bands after MI ranking compared with the one using empirical indices and specific bands related to indices, while the use of all 13 bands can marginally improve the classification accuracy than MI based one. Therefore, it is recommended that this approach can be applied for specific Sentinel-2A image classification problems in precision agriculture.

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Section: Classifier Selection: Svmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Potential Bands of Sentinel-2A Satellite for Classification Problems in Precision Agriculture

Zhang

Liu

2018

Int. J. Autom. Comput.

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“…With the assistance of SVMs, one can perform both linear as well as non-linear classification (Goudjil et al, 2018;Liu et al, 2012). SVM has become very widespread in research and has been incorporated into several fields including medical (Bromová et al, 2014;Mark Chang, 2020), military (Mohril et al, 2020;Rozek et al, 2020), industry (Zermane & Kasmi, 2020), and so forth; and a variety of applications, including image classification (Elaziz et al, 2020), text mining (Chatterjee et al, 2021;Court & Cole, 2020), video recommendation (Bălan et al, 2020;Massiris Fernández et al, 2020), and multimedia concept retrieval (Aslam & Curry, 2021;Moreno-Schneider et al, 2017).…”

Section: Methodsmentioning

confidence: 99%

Intelligent Control System of a Real Industrial Process

ZERMANE¹,

Hassina²

2022

EPSTEM

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Industrial systems are difficult to control and supervise efficiently because of the complexity of the production process. The aim is to automatically control in real-time as an alternative for operators as possible and highlight the importance of machine learning in the field of industry. Integrating SVM into the industrial supervision system in the cement factory (SCIMAT) permits the classification of different measurements coming from sensors to the Programmable Logic Controller (PLC) that indicates when the process is in good functioning or bad indicating that a default has occurred. These measurements are classified after training in three classes of level (low, medium, and high) that are classified in their turn into two classes (good and bad functioning). The three classes present the inputs of the fuzzy controllers. Based on this classification, the PLC makes orders for industrial equipment. Then a regression of variation of measurements in real-time is carried out to predict the good or the bad functioning of the production line. In conclusion, the proposed approach innovates the complex supervision system to learn how to control and preserve the habitual linguistic language used by operators, react in the right way, and prevent critical situations.

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“…For the sake of simplicity, we limit ourselves to the vicinity of the Hα line. The early attempts on a small sample of good spectra (Škoda & Vážný 2012;Bromová et al 2014) have already justified this method, and its application to the LAMOST DR1 (Škoda et al 2015, 2016) has resulted in the discovery of unknown emission-line candidates. This article describes the first systematic investigation of the LAMOST DR2 using a deep convolutional neural network (CNN) in combination with active learning.…”

Section: Introductionmentioning

confidence: 99%

Active deep learning method for the discovery of objects of interest in large spectroscopic surveys

Škoda

Podsztavek

Tvrdík

2020

A&A

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Context. Current archives of the LAMOST telescope contain millions of pipeline-processed spectra that have probably never been seen by human eyes. Most of the rare objects with interesting physical properties, however, can only be identified by visual analysis of their characteristic spectral features. A proper combination of interactive visualisation with modern machine learning techniques opens new ways to discover such objects. Aims. We apply active learning classification methods supported by deep convolutional neural networks to automatically identify complex emission-line shapes in multi-million spectra archives. Methods. We used the pool-based uncertainty sampling active learning method driven by a custom-designed deep convolutional neural network with 12 layers. The architecture of the network was inspired by VGGNet, AlexNet, and ZFNet, but it was adapted for operating on one-dimensional feature vectors. The unlabelled pool set is represented by 4.1 million spectra from the LAMOST data release 2 survey. The initial training of the network was performed on a labelled set of about 13 000 spectra obtained in the 400 Å wide region around Hα by the 2 m Perek telescope of the Ondřejov observatory, which mostly contains spectra of Be and related early-type stars. The differences between the Ondřejov intermediate-resolution and the LAMOST low-resolution spectrographs were compensated for by Gaussian blurring and wavelength conversion. Results. After several iterations, the network was able to successfully identify emission-line stars with an error smaller than 6.5%. Using the technology of the Virtual Observatory to visualise the results, we discovered 1 013 spectra of 948 new candidates of emission-line objects in addition to 664 spectra of 549 objects that are listed in SIMBAD and 2 644 spectra of 2 291 objects identified in an earlier paper of a Chinese group led by Wen Hou. The most interesting objects with unusual spectral properties are discussed in detail.

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Classification of Spectra of Emission Line Stars Using Machine Learning Techniques

Cited by 11 publications

References 14 publications

Potential Bands of Sentinel-2A Satellite for Classification Problems in Precision Agriculture

Potential Bands of Sentinel-2A Satellite for Classification Problems in Precision Agriculture

Intelligent Control System of a Real Industrial Process

Active deep learning method for the discovery of objects of interest in large spectroscopic surveys

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