| 引用本文： |
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刘超群,马祖陆,莫源富.遥感岩性识别研究进展与展望[J].广西科学院学报,2007,23(2):120-124,128. [点击复制]
- LIU Chao-qun,MA Zu-lu,MO Yuan-fu.Progress and Prospect of Study on Remote Sensing Lithologic Identification[J].Journal of Guangxi Academy of Sciences,2007,23(2):120-124,128. [点击复制]
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| 摘要: |
| 综述遥感岩性识别的岩石光谱学机理与岩石光谱特征,总结目前常用的遥感岩性信息提取方法。认为深入研究岩石矿物的光谱学机理,不仅要测量单个矿物的光谱,分析其诊断性特征,建立和完善矿物的光谱库,更重要的是测量和研究多种矿物、岩石组成的岩性地层单位的成分与光谱的相关性特征、规律及其诊断性特征。建议我国南方遥感岩性识别研究应开展不同岩性地层单位的岩石地球化学特征与其上部覆盖土壤化学特征和植被生物化学特征之间的相关分析,寻找其对应的遥感光谱信息变化规律;加强遥感信息与非遥感信息的综合分析,探索对新的高空间分辨率、高光谱分辨率的其它遥感信息的岩性识别方法。 |
| 关键词: 岩性识别 光谱特征 纹理 多光谱 高光谱 |
| DOI： |
| 投稿时间：2006-11-10修订日期：2006-12-15 |
| 基金项目:中国地质调查局工作项目《西南岩溶区地下水与环境地质调查综合研究》(编号(水)[2005]011-01)资助 |
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| Progress and Prospect of Study on Remote Sensing Lithologic Identification |
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LIU Chao-qun1, MA Zu-lu1,2, MO Yuan-fu1,3
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| (1.Institute of Karst Geology, CAGS, Guilin, Guangxi, 541004, China;2.China University of Geosciences, Beijing, 100083, China;3.Central South University, Changsha, Hunan, 410083, China) |
| Abstract: |
| The paper summarizes rock spectroscopy mechanism and rock spectral features,and sums up the common methods of obtaining lithologic information from Remote Sensing data at present.Considering for lucubrating the spectroscopy mechanism of rocks and minerals,the paper intends to measure single mineral's spectrum,analyze the diagnosable features,establish and consummate the spectral database of minerals.And the most important thing is to measure and study the spectral correlative features,rules and diagnosable features of the lithologic stratum units which are composed of several minerals and rocks.The Remote Sensing lithologic identification study in South China should search the change rules of corresponding spectral information by analysing the correlation between rock geochemical features of different lithologic stratum units and chemic features of covered soils or biochemical features of covered vegetation.The authors suggest enhancing integrative analysis on RS and non-RS information,and exploring the remote sensing lithologic identification methods with new high spatial and hyperspectral remote sensing data. |
| Key words: lithologic identification spectral feature texture multispectral hyperspectral |
