CASIA OpenIR  > 毕业生  > 博士学位论文
天体光谱数据自动处理和算法研究
邱波
学位类型工学博士
导师胡占义 ; 赵永恒
2002-05-01
学位授予单位中国科学院研究生院
学位授予地点中国科学院自动化研究所
学位专业模式识别与智能系统
关键词光谱 分类 红移 粗集 Spectrum Classification Red Shift Rough Set
摘要随着观测手段的不断提高,以LAMOST望远镜为代表的海量观测数据的处理问题日益尖 锐。LAMOST每个观测夜4,000个天体光谱的高获取率使得传统的人工或半人工的数据分析 方法无法满足天文学的需求,因此迫切需要高效的光谱自动处理系统。作为LAMOST数据处 理系统算法预研究的一部分,本论文的工作主要是围绕着求红移和自动分类这两个中心问题 进行的。论文的主要内容有以下三个方面: 一、伪三角法求红移 红移是河外天体最重要的参数,而红移和分类相耦合的现象使得红移的提取格外困难。 本论文在仔细分析天体光谱特点的基础上,提出了一种求红移的新方法一“伪三角法”。 该方法利用最强的三根谱线的波长信息构造“三角形”,通过将最大角的“余弦”与已知模 板的“余弦”表相匹配,反推得到相应的标准谱线波长,并进而得到红移值。实验表明,本 方法具有速度快,精度高,鲁棒性强的特点。 二、基于RANSAC方法的星表匹配 星表匹配从本质上来说是一个两个二维点集之间的匹配问题,与计算机视觉中两幅图像 之间自动寻找对应点的问题等价。我们把计算机视觉中的基于RANSAC方法的自动寻找匹 配点的鲁棒性估计技术成功地应用到星表匹配上来,取得了了令人满意的结果。实验表明, 我们的方法能够快速有效地完成星表匹配。在总共960个星表样本中,除了两个不完备的星 表之外,全部获得了正确的匹配结果。 三、光谱描述语言和基于粗集的光谱分类规则挖掘 我们首先引入一种新的“语言”对光谱进行描述,用一条光谱的特征谱线波长、相应的 谱线强度和宽度、以及谱线之间的关系来描述整条光谱。光谱描述语言的引入一则可以大大 压缩光谱数据库的存贮量,二则使得数据挖掘的技术之一——粗集的应用成为可能。在用光 谱描述语言表示光谱之后,我们使用粗集来构造自动提取光谱分类规则的算法。作为研究的 初步阶段,我们首先对400多条具有明确分类的恒星光谱进行了分析,并取得了令人比较满 意的分类结果。
其他摘要Now a large telescope called LAMOST (Large Sky Area Multi-Object Fiber Spectroscopic Telescope) is under construction in the National Observatory in Beijing, China. After its scheduled completion in 2004, it is expected to collect more than 4,000 spectra in a single observation night. Such voluminous data demand automatic spectrum processing, and the previous mainly manual or interactive tèchniques become unsuitable. The main obstacles for automatic spectrum classification and recognition in LAMOST are two-fold: firstly, the spectra of celestial bodies are extremely noisy; and secondly, the spectra to be recognized are voluminous. Hence any acceptable technique must be both insensitive to noise and computationally efficient. This thesis is concentrated on the following two key issues in spectrum classification and recognition: redshift identification and automatic classification of spectra. The main results can be summarized as follows: 1. Redshift Identification by the pseudo-triangle method. Redshift is the most important parameter of celestial bodies outside the galaxy. Due to the coupling effect of redshift determination and spectrum classification, redshift identification is a key and very difficult problem in practice. A pseudo-triangle technique is proposed to deal with the problem, which, consists of the following three major steps: in the first step, the 3 wavelengths corresponding to the 3 highest intensity values of an unknown spectrum are selected to construct a pseudo-triangle, and the largest angle, being of independent of the unknown redshift value, of this triangle is calculated, and used as the index. In the second step, 3 model wavelengths are retrieved via this index from a pre-calculated look-up-table, which contains all the combinations of all the feature wavelengths of the model spectra. And finally based on the 3 corresponding wavelengths, the corresponding redshift value is derived. The main characteristics of our technique are its simplicity, efficiency and good robustness, and are demonstrated by experiments on simulated data as well as on real celestial spectra. 2. Atlas matching based on an RANSAC algorithm. The problem of atlas matching is in essence a point-matching problem from two point sets. It is equivalent to the point correspondence problem across two images in computer vision literature. The RANSAC based algorithms are widely used in computer vision due to its extremely good robustness. We have successfully applied an RANSAC based technique to the atlas-matching problem. Among the 960 available images, all but two incomplete images are correctly matched. Furthermore, the algorithm is quite computationally efficient, It takes no more than a few seconds for each matching. 3. Spectrum description language and classifying rule mining in spectra datasets using rough set We introduced a kind of new 'language' to describe a spectrum, which uses feature wavelengths, the corr
馆藏号XWLW675
其他标识符675
语种中文
文献类型学位论文
条目标识符http://ir.ia.ac.cn/handle/173211/5737
专题毕业生_博士学位论文
推荐引用方式
GB/T 7714
邱波. 天体光谱数据自动处理和算法研究[D]. 中国科学院自动化研究所. 中国科学院研究生院,2002.
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