基于小波特征的星系光谱分类

提出了一种新的星系光谱分类方法.首先,对原始光谱进行四级小波分解,选择主要包含谱线信息的第四级小波系数作为光谱的小波特征;然后,利用主分量分析对光谱的小波特征进行特征压缩,得到光谱的识别特征;最后,利用Fisher线性判别分析实现分类.该方法能够在红移值未知的情况下,对流量未定标的星系光谱进行识别.通过实验与其他几种分类方法进行了比较.实验结果表明,本文方法具有较强的鲁棒性,在流量未定标情况下的识别效果优于其他几种分类方法.

Spectral Classification of Galaxy Based on Wavelet Feature

A technique for classification of galaxy spectra is proposed. At first, a four-level wavelet decomposition of the original spectrum is performed, and the wavelet coefficient at the fourth level, which mainly includes the information of spectral lines, is chosen as the wavelet feature of the spectrum. Secondly, principal components analysis is used to compress the wavelet feature and to get the recognition feature of the spectrum. Finally, Fisher linear discriminant analysis is employed for classification. This approach can recognize the galaxy spectrum whose flux is uncalibrated and redshift is unknown. Comparisons with several other classification techniques were made by experiments. Experiment results show the proposed method is robust and superior to other methods under the condition that flux is uncalibrated.