Abstract
This paper presents a novel spectral analysis and classification technique, which is based on multi-scale feature extraction and neural networks. We propose two feature extraction methods in wavelet domain to implement de-noising process and construct feature spectra. Then a radial basis function network is employed for classifying spectral lines. The input of the neural network is the feature spectra, which is produced by the proposed methods. Real world data experimental results show that our technique is robust and efficient. The classification results are much better than the best results obtained by principle component analysis feature extraction method.
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References
Odewahn, S.C., Stockwell, E.B., Pennington, R.L., Humphreys, R.M., Zumach, W.A.: Automated Star/Galaxy Discrimination with Neural Networks. Astronomical Journal 103, 318–331 (1992)
Bai, L., Li, Z.B., Guo, P.: Classification of Stellar Spectral Data Based on Kalman Filter and RBF Neural Networks. In: Proceedings of IEEE Inter. Conf. on Sys. Man & Cyber., pp. 274–279. IEEE Press, Piscataway (2003)
Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, London (1995)
Jacoby, G.H., Hunter, D.A., Christian, C.A.: A Library of Stellar Spectra. The Astrophysical Journal Supplement Series 56, 257–281 (1984)
Pickles, A.J.: A Stellar Spectral Flux Library: 1150–25000 Å. The Publications of the Astronomical Society of the Pacific 110, 863–878 (1998)
Kurtz, M.J.: The MK Process and Stellar Classification. David Dunlap Observatory, Toronto (1984)
Mallat, S.G.: A Theory for Multiresolution Signal Decomposition: The Wavelet Representation. IEEE Trans. on Pattern Analysis and Machine Intelligence 11, 674–693 (1989)
Donoho, D.L.: De-noising by Soft-Thresholding. IEEE Trans. on Information Theory 41, 613–627 (1995)
Zombeck, M.V.: Handbook of Space Astronomy and Astrophysics. Cambridge University Press, London (1990)
Jolliffe, I.T.: Principal Component Analysis. Springer, New York (1986)
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© 2004 Springer-Verlag Berlin Heidelberg
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Jiang, Y., Guo, P. (2004). Spectral Analysis and Recognition Using Multi-scale Features and Neural Networks. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks - ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28648-6_58
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DOI: https://doi.org/10.1007/978-3-540-28648-6_58
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22843-1
Online ISBN: 978-3-540-28648-6
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