天文光谱特征提取及其应用研究

项目名称： 天文光谱特征提取及其应用研究

项目编号： No.61273248

项目类型： 面上项目

立项/批准年度： 2013

项目学科： 自动化技术、计算机技术

项目作者： 李乡儒

作者单位： 华南师范大学

项目金额： 81万元

中文摘要： 随着SDSS、2dF和LAMOST等大型天文观测项目的实施，天体光谱的获取速度和数据量急剧增加。如何快速、高质量地对光谱进行参数估计、分类归档、检索和物理规律探索成为有效利用这些海量、高维数据所急需解决的问题。其关键环节是光谱特征的提取，而传统方法对噪声、定标质量、光谱复杂度、红移及其变化范围等非常敏感，导致基于它的分析结果精度不稳定、并影响已标注光谱的跨望远镜使用。特征点思想已成功应用于计算机视觉领域，我们提出了特征点分析模型Correspondence Function(CF)，初步研究表明它可用于光谱自动分析。本项目旨在基于特征点思想探索一种新的光谱特征提取方法，通过对特征的局部化、自定位、自适应尺度估计，及其表征的离散化和统计量化克服上述因素的不良影响。内容包括：光谱特征的检测、定位及其稳健表征方法研究；基于该特征和大规模跨望远镜历史标注光谱，研究CF模型在类星体识别中的应用。

中文关键词： 天文光谱；特征提取；特征选择；科学大数据；特征检测

英文摘要： With the recent implementation of some large-scale astronomical sky survey proposals (e.g. SDSS, 2dF and LAMOST), celestial spectra are becoming very abundant and rich. Therefore, the following problems are urgent to be solved: how to quickly estimate the parameters of the high-dimentional spectra and file them, how to discover the patterns under the enormous spectra (pattern exploring), how to retrieve them based on some specified contents in spectra, etc. One of the key procedures in solving the above problems is to extract the spectrum features and represent the spectra adequately and compactly. However, the traditional spectrum features are vulnerable to noise, calibration-distortion, spectrum complexity, redshift and its range, which result that the accuracy of processed result is unstable and that the labeled celestial spectra can not be effectively used in automatic spectra between different telescopes. Furthermore, the idea based on interest point has been applified sussesfully in computer vision community, and we proposed a novel model correspondence function(CF) for analyzing image interest point features.To deal with the above limitations of the traditional methods, this project focus on designing a novel feature extraction method for astronomical spectra based on the idea of image interest point by

英文关键词： astronomical spectrum；feature extraction；feature selection；scientific big data；feature detection