Flux Extraction Based on General Regression Neural Network for Two-Dimensional Spectral Image

  • Zhen Wang
  • Qian YinEmail author
  • Ping Guo
  • Xin Zheng
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 850)


In this paper, one novel method to extract flux from two dimensional spectral images which we observed through LAMOST (Large Area Multi-Object Fiber Spectroscopic Telescope) is proposed. First of all, the spectral images are preprocessed. Then, in the flux extraction algorithm, the GRNN (General Regression Neural Network) and double Gaussian function are employed to simulate the profile of each spectrum in spatial orientation. We perform our experiment, with same radial basis function, by GRNN and RBFNN (Radial Basis Function Neural Network) method. The experimental results show that our method performs higher SNR (Signal Noise Ration) and lower time-consuming that is more applicable in such massive spectral data.


LAMOST GRNN Flux extraction Spectral data 



The research work described in this paper was fully supported by the grants from the National Key Research and Development Program (Project No. 2017YFC1502505), the National Natural Science Foundation of China (Project No. 61472043), the Joint Research Fund in Astronomy (U1531242) under cooperative agreement between the NSFC and CAS. Prof. Qian Yin is the author to whom all correspondence should be addressed.


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Image Processing and Pattern Recognition LaboratoryBeijing Normal UniversityBeijingChina

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