一种新的基于2维傅里叶谱图像的恒星光谱特征提取方法和深度网络分类应用

A New Stellar Spectral Feature Extraction Method Based on Two-dimensional Fourier Spectrum Image and Its Application in the Stellar Spectral Classification Based on Deep Network

天体光谱分类是天文学研究的重要内容之一,其关键是从光谱数据中选择和提取对分类识别最有效的特征构建特征空间.提出一种新的基于2维傅里叶谱图像的特征提取方法,并应用于LAMOST (the Large Sky Area Multi-Object Fiber Spectroscopic Telescope)恒星光谱数据的分类研究中.光谱数据来源于LAMOST Data Release 5(DR5),选取30000条F、 G和K型星光谱数据,利用短时傅里叶变换(Short-Time Fourier Transform, STFT)将1维光谱数据变换成2维傅里叶谱图像,对得到的2维傅里叶谱图像采用深度卷积网络模型进行分类,得到的分类准确率是92.90%.实验结果表明通过对LAMOST恒星光谱数据进行STFT可得到光谱的2维傅里叶谱图像,谱图像构成了新的光谱数据特征和特征空间,新的特征对于光谱数据分类是有效的.此方法是对光谱分类的一种全新尝试,对海量天体光谱的分类和挖掘处理有一定的开创意义.

The classification of celestial spectra is one of the important contents of astronomical research. The key is to select and extract the most effective feature for classification from spectra data. In this paper, we propose a new feature extraction method for astronomical spectra based on two-dimensional Fourier spectrum image, and apply the method to the classification study of LAMOST(the Large Sky Area MultiObject Fiber Spectroscopic Telescope) stellar spectral data. The spectra data are from LAMOST Data Release 5(DR5). We select 30000 F, G, and K types of spectra data.The short-time Fourier transform(STFT) is used to transform the one-dimensional spectra data into two-dimensional Fourier spectrum images. We classify and test these two-dimensional Fourier spectrum images with a module based on deep convolutional network, and the classification accuracy rate is 92.90%. The experimental result shows that the LAMOST stellar spectra data can be transformed into the two-dimensional Fourier spectrum images by the STFT. These spectral images inform new features, and build a new feature space, which is effective for classification. The method is a fully new attempt in spectra classification, which has certain pioneering significance for the classification and mining of massive celestial spectra.