|
|
|
|
|
|
| Automatic Classification Method of Star Spectra Based on
Semi-Supervised Mode |
| JIANG Xia*, QIU Bo, WANG Lin-qian, GUO Xiao-yu |
School of Electronics and Information Engineering，Hebei University of Technology，Tianjin 300401，China
|
|
|
|
|
Abstract With the implementation of more and more large-scale spectral surveys, many star spectral data are generated, which is of great significance to the study of star evolution theory, but also brings great challenges to the traditional spectral classification and processing. In the LAMOST DR7 (v2.0 version) spectral data set released in 2021, the total spectrum of stars has reached the order of millions, but the number of O-type is only 129, and the data set is seriously unbalanced. Traditional machine learning classification methods cannot achieve good results for such a large amount of data and a serious imbalance of data sets. Therefore, they are mostly used to classify the spectra of adjacent stars of two types, partial types or subclasses. An automatic star spectrum classification method based on semi-supervised mode of one dimensional convolutional neural network (CNN) and one dimensional generative adversarial network (GAN) is used to solve this problem. Firstly, each spectrum is tailored and denoised, and the wavelength range of the spectrum is 370.00~867.16 nm. Then, uniform sampling and normalization are carried out to generate 1×3 700 data set samples, which are sent to one-dimensional CNN for training. In order to avoid overfitting and improve model’s prediction ability for unknown data, dropout is applied between the full connection layer and the pooling layer. This one-dimensional CNN network is used to classify six kinds of spectra except for O-type, and the average classification accuracy is 98.08%. This experiment uses one-dimensional GAN network to solve the problem that the number of O-type is seriously small. The input of GAN is a noise signal whose size is 1×900.Through the fully connected three-layer convolution operation in the generator, the output size is 1×3 700. The separate alternating iterative training of the generator and discriminator finally outputs the required number of O-type star samples to expand the data set. Compared with oversampling, using data sets expanded by GAN to classify the star spectra can improve the classification accuracy of O-type stars from 72.92% to 97.92%, and the accuracy of the classifier can reach 96.28%. The experimental results show that the automatic star spectra classification method using this semi-supervised mode can realize the rapid and accurate classification of seven types of star spectra and can also be used to mine the unclassified spectra marked as “unknown”.
|
|
Received: 2022-03-18
Accepted: 2022-06-06
|
|
|
|
Corresponding Authors:
JIANG Xia
E-mail: xunjiang@hebut.edu.cn
|
|
[1] Li Xiangru，Pan Ruyang，Duan Fuqing. Research in Astronomy and Astrophysics，2017，17（4）：36.
[2] Morgan W, Keenan P. Annual Review of Astronomy and Astrophysics, 1973, 11: 29.
[3] Du Bing，Luo A-li，Zhang Shuo，et al. Research in Astronomy and Astrophysics，2021，21（8）：202.
[4] Fan J，Han F，Liu H. Nationanl Science Review，2014，1（2）：293.
[5] Schierscher F，Paunzen E. Astronomische Nachrichten，2011，332（6）：597.
[6] Liu C，Cui W Y，Zhang B，et al. Research in Astronomy and Astrophysics，2015，15（8）：1137.
[7] Kheirdastan S，Bazarghan M. Astrophysics and Space Science，2016，361（9）：304.
[8] SHI Chao-jun，QIU Bo，ZHOU Ya-tong，et al（石超君，邱 波，周亚同，等）. Spectroscopy and Spectral Analysis （光谱学与光谱分析），2019，39（4）：1312.
[9] Wen Xiaoqing，Yang Jinmeng. Chinese Journal of Physics，2021，69：303.
[10] XU Ting-ting，MA Chen-ye，ZHANG Jing-min，et al（许婷婷，马晨晔，张静敏，等）. Acta Astronomica Sinica（天文学报），2019，60（2）：11.
[11] Du Changde，Luo Ali, Yang Haifeng. New Astronomy，2017，51：51.
[12] Zheng Zipeng，Qiu Bo，Luo A-li，et al. Publications of the Astronomical Society of the Pacific，2020，132：024504.
[13] Si Jianmin，Li Yinbi，Luo A-li，et al. Research in Astronomy and Astrophysics，2015，15（10）：1671.
|
| [1] |
LI Xin-ting, ZHANG Feng, FENG Jie*. Convolutional Neural Network Combined With Improved Spectral
Processing Method for Potato Disease Detection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 215-224. |
| [2] |
LAN Yan1，WANG Wu1，XU Wen2，CHAI Qin-qin1*，LI Yu-rong1，ZHANG Xun2. Discrimination of Planting and Tissue-Cultured Anoectochilus Roxburghii Based on SMOTE and Inception-CNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 158-163. |
| [3] |
WANG Qi-biao1, HE Yu-kai1, LUO Yu-shi1, WANG Shu-jun1, XIE Bo2, DENG Chao2*, LIU Yong3, TUO Xian-guo3. Study on Analysis Method of Distiller's Grains Acidity Based on
Convolutional Neural Network and Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3726-3731. |
| [4] |
HUANG Meng-qiang1, KUANG Wen-jian2, 3*, LIU Xiang1, HE Liang4. Quantitative Analysis of Cotton/Polyester/Wool Blended Fiber Content by Near-Infrared Spectroscopy Based on 1D-CNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3565-3570. |
| [5] |
CAI Jian-rong1, 2, HUANG Chu-jun1, MA Li-xin1, ZHAI Li-xiang1, GUO Zhi-ming1, 3*. Hand-Held Visible/Near Infrared Nondestructive Detection System for Soluble Solid Content in Mandarin by 1D-CNN Model[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2792-2798. |
| [6] |
PU Shan-shan, ZHENG En-rang*, CHEN Bei. Research on A Classification Algorithm of Near-Infrared Spectroscopy Based on 1D-CNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2446-2451. |
| [7] |
FAN Ya-wen, LIU Yan-ping*, QIU Bo, JIANG Xia, WANG Lin-qian, WANG Kun. Research on Spectral Classification of Stellar Subtypes Based on
SSTransformer[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2523-2528. |
| [8] |
TANG Ting, PAN Xin*, LUO Xiao-ling, GAO Xiao-jing. Fusion of ConvLSTM and Multi-Attention Mechanism Network for
Hyperspectral Image Classification[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2608-2616. |
| [9] |
LI Wen-xia1, DU Yu-jun2, WANG Yue1, LIU Zheng-dong3*, ZHENG Jia-hui1, DU Wen-qian1, WANG Hua-ping4. Research on On-Line Efficient Near-Infrared Spectral Recognition and Automatic Sorting Technology of Waste Textiles Based on Convolutional Neural Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2139-2145. |
| [10] |
LIANG Wan-jie1, FENG Hui2, JIANG Dong3, ZHANG Wen-yu1, 4, CAO Jing1, CAO Hong-xin1*. Early Recognition of Sclerotinia Stem Rot on Oilseed Rape by Hyperspectral Imaging Combined With Deep Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2220-2225. |
| [11] |
ZHENG Zhi-jie1, LIN Zhen-heng1, 2*, XIE Hai-he2, NIE Yong-zhong3. The Method of Terahertz Spectral Classification and Identification for Engineering Plastics Based on Convolutional Neural Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1387-1393. |
| [12] |
CHEN Dong-ying1, 2, ZHANG Hao1, 2*, ZHANG Zi-long1, YU Mu-xin1, CHEN Lu3. Research on the Origin Traceability of Honeysuckle Based on Improved 1D-VD-CNN and Near-Infrared Spectral Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1471-1477. |
| [13] |
LI Kai-yu1, ZHANG Hui2, MA Jun-cheng3, ZHANG Ling-xian1*. Segmentation Method for Crop Leaf Spot Based on Semantic Segmentation and Visible Spectral Images[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 1248-1253. |
| [14] |
CHEN Qing1, TANG Bin1, 2*, LONG Zou-rong1, 2, MIAO Jun-feng1, HUANG Zi-heng1, DAI Ruo-chen1, SHI Sheng-hui1, ZHAO Ming-fu1, ZHONG Nian-bing1. Water Quality Classification Using Convolution Neural Network Based on UV-Vis Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(03): 731-736. |
| [15] |
CHU Zhi-hong1, 2, ZHANG Yi-zhu2, QU Qiu-hong3, ZHAO Jin-wu1, 2, HE Ming-xia1, 2*. Terahertz Spectral Imaging With High Spatial Resolution and High
Visibility[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(02): 356-362. |
|
|
|
|
