基于胶囊网络的恒星光谱分类研究

大型巡天项目的快速发展,产生大量的恒星光谱数据,也使得实现恒星光谱数据的自动分类成为一项具有挑战性的工作.提出一种新的基于胶囊网络的恒星光谱分类方法,首先利用1维卷积网络和短时傅里叶变换将来源于LAMOST(Large Sky Area Multi-Object Fiber Spectroscopy Telescope)Data Release 5(DR5)的F5、G5、K5型1维恒星光谱转化成2维傅里叶谱图像,再通过胶囊网络对2维谱图像进行自动分类.由于胶囊网络具有保留图像中实体之间的分层位姿关系和无需池化层的优点,实验结果表明:胶囊网络具有较好的分类性能,对于F5、G5、K5型恒星光谱的分类,准确率优于其他分类方法.

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 makes the automatic classification of stellar spectral data a challenging task. We select F5, G5, and K5 types of spectra data, which are derived from LAMOST Data Release 5 (DR5), one dimensional convolution network and short-time Fourier transform (STFT) are used to transform the one-dimensional spectra data into two-dimensional Fourier spectrum images. And then the two-dimensional Fourier spectrum images are classified automatically by the capsule network. Because the capsule network preserves the hierarchical pose relationships between the entities in the image and the advantages of removing the pooled layers, the experimental results show that for the classification accuracy of F5, G5, and K5 stellar spectra, capsule Network has better classification performance and is superior to other classification methods.