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遥感技术与应用  2020, Vol. 35 Issue (2): 267-286    DOI: 10.11873/j.issn.1004-0323.2020.2.0267
综述     
地物波谱数据库应用方法及遥感应用现状
程娟1,2(),肖青1,2(),闻建光1,2,唐勇1,游冬琴1,卞尊健1,吴胜标1,2,郝大磊1,2,钟守熠1,2
1.中国科学院遥感与数字地球研究所 遥感科学国家重点实验室,北京 100101
2.中国科学院大学,北京 100049
Review of Methods and Remote Sensing Cases Using Spectral Library
Juan Cheng1,2(),Qing Xiao1,2(),Jianguang Wen1,2,Yong Tang1,Dongqin You1,Zunjian Bian1,Dalei Hao1,2,Shouyi Zhong1,2
1.State Key Laboratory of Remote Sensing, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
2.University of Chinese Academy of Sciences, Beijing 100049, China
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摘要:

地物波谱数据库在遥感信息提取中具有重要的应用价值,本文归纳和总结了常用的国内外通用型地物波谱数据库与专业型地物波谱数据库的发展现状。在对有关波谱库遥感应用文献进行计量分析的基础上,综述了地物波谱数据库遥感应用的四种主要

方法

波谱特征分析、光谱匹配识别、混合像元分解以及参数提取建模,阐述了地物波谱数据库在地物分类、目标识别及参数反演中的应用。从当前所处的遥感“大数据”时代背景出发,亦对地物波谱数据库的建设趋势与应用潜力进行了展望。

关键词: 遥感波谱数据库分类识别反演    
Abstract:

Ground object spectral libraries play a significant role in remote sensing information extraction. This paper investigates the domestic and foreign spectral libraries frequently-used, including the general spectral libraries and the professional spectral libraries. Based on the biliometric analysis of the literatures about remote sensing applications based on spectral libraries, four kinds of methods are summarized, including spectral feature analysis, spectral matching, spectral mixture analysis and quantitative remote sensing modeling. Some remote sensing applications based on spectral libraries, such as ground object classification, target identification and land surface parameters inversion, are also summarized. From the background of remote sensing big data, the developing trends and application potential of the ground object spectral library are prospected at the end.

Key words: Remote sensing    Spectra    Database    Classification    Identification    Inversion
收稿日期: 2018-12-27 出版日期: 2020-07-10
ZTFLH:  TP701  
基金资助: 国家科技性基础专项(2014FYZ10800)
通讯作者: 肖青     E-mail: chengjuan@radi.ac.cn;xiaoqing@radi.ac.cn
作者简介: 程娟(1993-),女,陕西大荔人,硕士研究生,主要从事地物波谱特性及多角度特性研究。E?mail:chengjuan@radi.ac.cn
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程娟
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闻建光
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卞尊健
吴胜标
郝大磊
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引用本文:

程娟,肖青,闻建光,唐勇,游冬琴,卞尊健,吴胜标,郝大磊,钟守熠. 地物波谱数据库应用方法及遥感应用现状[J]. 遥感技术与应用, 2020, 35(2): 267-286.

Juan Cheng,Qing Xiao,Jianguang Wen,Yong Tang,Dongqin You,Zunjian Bian,Dalei Hao,Shouyi Zhong. Review of Methods and Remote Sensing Cases Using Spectral Library. Remote Sensing Technology and Application, 2020, 35(2): 267-286.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2020.2.0267        http://www.rsta.ac.cn/CN/Y2020/V35/I2/267

图1  USGS和ASTER波谱数据库被引用频次年际变化(Google Scholar检索,时间:20180613 )
表1  典型波谱库地物类型及波谱范围
图2  波谱库遥感应用文献关键词总体特征(Web of Science检索)
图3  明矾石光谱匹配识别图示[48]
方法优点缺点代表性算法
确定型光谱距离匹配计算简单;对光谱的细微差异敏感对噪声敏感,匹配前需进行严格的光谱去噪处理ED, LSM, ZSD, MD等[52,53,54,55]
光谱角度匹配计算简单,速度快;夹角大小与光谱的绝对数值无关复杂的光谱曲线计算效率低;难以区分开材质相似的波谱SAM, MSAM, SCA, ESAM等[56,57,58,63]
光谱相关性匹配性能较好,可用于光谱极其微小的识别匹配结果受非诊断性噪声影响CCSM, CR-CCSM, d-CICR CCSM等[59,64,65]
二值编码匹配速度快,效率高易丢失细节信息,限用于粗略的分类和识别BE, BMIBM, NBE等[66,67,68]
光谱特征拟合考虑了光谱内在的物理意义;识别效率较高对噪声敏感,需进行严格的去噪处理SFF, MRSFF, SCF, SCM等[69,70,71,72]
随机型光谱信息分布法适用于图像识别对图像噪声敏感;识别小目标困难CEM, ACE, SID等[51,73,74]
组合型任意两种光谱匹配算法的组合组合算法比单一算法匹配精度高,光谱识别能力强计算复杂,尤其是形状较复杂的光谱SSV, SAM-SID, SID-SCA, NS3, JM-SAM等[60,61,62,75,76]
表2  主要的光谱匹配方法
图4  基于光谱库的稀疏解混原理[113]
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