基于Bayes决策的光谱分类  

Spectral Classification Based on Bayes Decision

作　　者：刘蓉[1] 靳红梅[2] 段福庆[3] 

机构地区：[1]北京服装学院基础部,北京100029 [2]西安科技大学计算机系,陕西西安710054 [3]北京师范大学信息科学与技术学院,北京100875

出　　处：《光谱学与光谱分析》2010年第3期838-841,共4页Spectroscopy and Spectral Analysis

基　　金：国家自然科学基金项目(60773040);北京市属市高等学校人才强教计划项目PHR(201008282)资助

摘　　要：天文观测技术的迅速发展推动了大规模的星系光谱巡天计划如SDSS、LAMOST等,面对这些巡天项目所观测到的海量光谱数据,研究自动的光谱分析方法已成为必然的选择。研究了基于Bayes决策的光谱分类方法,将光谱分为恒星,星系和类星体三类。首先采用主分量分析来进行特征提取,将光谱投影到由三个主分量构成的特征空间中;然后,采用非参数密度估计Parzen窗法来估计类条件概率密度函数;最后利用基于最小错误率的Bayes决策进行分类。在Parzen窗法中,核宽很大程度上影响着估计效果,从而影响着分类效果。通过详尽的实验分析了核宽和分类效果的关系,发现当核宽接近某个阈值时,识别率将会增加,但小于这个阈值时,识别率反而下降。The rapid development of astronomical observation has led to many large sky surveys such as SDSS （Sloan digital sky survey） and LAMOST （large sky area multi-object spectroscopic telescope）. Since these surveys have produced very large numbers of spectra, automated spectral analysis becomes desirable and necessary. The present paper studies the spectral classification method based on Bayes decision theory, which divides spectra into three types： star, galaxy and quasar. Firstly, principal component analysis （PCA） is used in feature extraction, and spectra are projected into the 3D PCA feature space; secondly, the class conditional probability density functions are estimated using the non-parametric density estimation technique, Parzen window approach; finally, the minimum error Bayes decision rule is used for classification. In Parzen window approach, the kernel width affects the density estimation, and then affects the classification effect. Extensive experiments have been performed to analyze the relationship between the kernel widths and the correct classification rates. The authors found that the correct rate increases with the kernel width being close to some threshold, while it decreases with the kernel width being less than this threshold.

关 键 词：光谱分类 星系 Bayes决策 

分 类 号：TN911.7[电子电信—通信与信息系统]