遥感技术与应用 2013, Vol. 28 Issue (4): 707-713 DOI: 10.11873/j.issn.1004-0323.2013.4.707 |
模型与反演 |
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基于Bhattacharyya距离的典型地物波谱特征差异性分析 |
程熙1,2,沈占锋1,周亚男1,2,夏列钢1,2,骆剑承1
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(1.中国科学院遥感与数字地球研究所,北京 100101;2.中国科学院大学,北京 100049) |
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The Spectral Characteristics Separability Analysis of Spectral Database of Typical Objects of Land Surface based on Bhattacharyya Distance |
Cheng Xi1,2,Shen Zhanfeng1,Zhou Ya nan1,2,Xia Liegang1,2,Luo Jiancheng1
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(1.Institute of Remote Sensing and Digtal Earth,Chinese Academy of Sciences,Beijing 100101,China;
2.University of Chinese Academy of Science,Beijing 100049,China;) |
引用本文:
程熙,沈占锋,周亚男,夏列钢,骆剑承. 基于Bhattacharyya距离的典型地物波谱特征差异性分析[J]. 遥感技术与应用, 2013, 28(4): 707-713.
Cheng Xi,Shen Zhanfeng,Zhou Ya nan,Xia Liegang,Luo Jiancheng. The Spectral Characteristics Separability Analysis of Spectral Database of Typical Objects of Land Surface based on Bhattacharyya Distance. Remote Sensing Technology and Application, 2013, 28(4): 707-713.
链接本文:
http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2013.4.707
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http://www.rsta.ac.cn/CN/Y2013/V28/I4/707
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[1]Ben-Dor E,Levin N,Saaroni H.A Spectral based Recognition of the Urban Environment Using the Visible and Near-infrared Spectral Region (0.4~1.1 μm):A Case Study over Tel-Aviv,Israel[J].International Journal of Remote Sensing,2001,22(11):2193-2218.[2]Green R O,Eastwood M L,Sarture C M,et al.Imaging Spectroscopy and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS)[J].Remote Sensing of Environment,1998,65(3):227-248.[3]Wang Jindi,Zhang Lixin,Liu Qinghuo,et al.The Spectral Database of Typical Objects of Land Surface[M].Beijing:Science Press,2009.[王锦地,张立新,柳钦火,等.中国典型地物波谱知识库[M].北京:科学出版社,2009.][4]Shi Jian,Liu Qinhuo,Wen Jianguang,et al.Design and Realization of the Service Platform for Typical Ground Objects Spectrum Data in China based on E-government[J].Remote Sensing Technology and Application,2011,26(4):520-526.[施建,柳钦火,闻建光,等.面向电子政务的全国典型地物波谱数据服务平台设计与实现[J].遥感技术与应用,2011,26(4):520-526.][5]Clark R N,Swayze G A,Wise R,et al.USGS Digital Spectral Library Splib06a:U.S.Geological Survey,Digital Data Series 231[EB/OL].http://speclab.cr.usgs.gov/spectral.lib06,2007,2012.[6]Baldridge A M,Hook S J,Grove C I,et al.The ASTER Spectral Library Version 2.0[J].Remote Sensing of Environment,2009,113(4):711-715.[7]Franke J,Roberts D A,Halligan K,et al.Hierarchical Multiple Endmember Spectral Mixture Analysis (MESMA) of Hyperspectral Imagery for Urban Environments[J].Remote Sensing of Environment,2009,113(8):1712-1723.[8]Ridd M K.Exploring a V-I-S (Vegetation-Impervious Surface-Soil) Model for Urban Ecosystem Analysis Through Remote Sensing:Comparative Anatomy for Cities[J].International Journal of Remote Sensing,1995,16(12):2165-2185.[9]Herold M,Roberts D A,Gardner M E,et al.Dennison,Spectrometry for Urban Area Remote Sensing—Development and Analysis of a Spectral Library from 350 to 2400 nm[J].Remote Sensing of Environment,2004,91(3-4):304-319.[10]Xuan G,Chai P,Wu M.Bhattacharyya Distance Feature Selection[C]//Proceedings of the 13th International Conference on Pattern Recognition,Vienna,Austria,1996.[11]Mausel P W,Kramber W J,Lee J K.Optimum Band Selection for Supervised Classification of Multispectral Data[J].Photogrammetric Engineering and Remote Sensing,1990,56:55-60.[12]Jimenez L O,Landgrebe D A.Hyperspectral Data analysis and Supervised Feature Reduction via Projection Pursuit[J].IEEE Transactions on Geoscience and Remote Sensing,1999,37(6):2653-2667.[13]Bruzzone L,Conese C,Maselli F,et al.Multisource Classification of Complex Rural Areas by Statistical and Neural-network Approaches[M].Bethesda,MD:ETATS-UNIS,American Society for Photogrammetry and Remote Sensing,1997.[14]Landgrebe D.Hyperspectral Image Data Analysis[J].IEEE Signal Processing Magazine,2002,19(1):17-28.[15]Biehl L,Landgrebe D.MultiSpec——A Tool for Multispectral-hyperspectral Image Data Analysis[J].Computers & Geosciences,2002,28(10):1153-1159.[16]Du Huaqiang,Jin Wei,Ge Hongli,et al.Using Fractal Dimensions of Hyperspeetral Curves to Analyze the Healthy Status of Vegetation[J].Spectroscopy and Spectral Analysis,2009,(8):2136-2140.[杜华强,金伟,葛宏立,等.用高光谱曲线分形维数分析植被健康状况[J].光谱学与光谱分析,2009,(8):2136-2140.] |
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