Facing with the requirement of the automated spectral recognition and analysis in LAMOST, This thesis focuses on automated processing methods of spectra, which include preprocessing, spectral line extraction, spectral classification and redshift measurement and so on. The main points of our work are as follows: 1. We present a non-linear scale space filtering method based on mean shift. The filter is controlled only by the kernel bandwidth in spatial domain, and the kernel bandwidth in range domain at every sample point is chosen adaptively by the local characteristic of the signal. 2. We present a method of spectral line extraction based on feature constraints. Two feature constraints are used in spectral line recognition. One is the intensity constraint; the other is the shape constraint. The two feature constraints play a key role in improving quality of spectral line extraction. 3. We present a density estimation based method for redshift determination and spectral line identification, in which the problem of redshift determination is translated into the problem of searching for the point of maximum density within a data set. We present a novel cross-correlation method for redshift determination of quasars. Compared with the existing methods based on spectral line matching, the proposed method has a lower dependence on the quality of spectral line extraction.4. We present a new template matching technique for redshift determination and spectral classification based on similarity measure. The similarity measure adopted, similar to evidence accumulation, is the weighted sum of several similarity evidences. Different from existing methods, the approach can deal with the spectral classification and redshift determination simultaneously, and thus avoid the error multiplication caused by the classification following redshift determination or the reverse.Finally, we construct an automated recognition and analysis system of astronomical spectra using our work, which can realize a rough classification and redshift determination.
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