高光谱遥感资料用于海岸带分类与沙滩表面承载特性的研究
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摘要
高光谱遥感数据具有图像—光谱合一的特点,与传统的多光谱遥感数据相比,能够更好的分类与识别各类地物,本文主要利用高光谱遥感数据,实现海岸带分类,并且在此基础上实现沙滩表面承载特性信息的提取,主要内容分为以下三个部分:
第一部分概要介绍了高光谱遥感技术。首先回顾了高光谱遥感的基本概念、原理和发展历史,然后介绍了高光谱遥感数据的预处理,包括大气校正,几何校正,以及高光谱遥感技术的主要分析方法,最后对试验高光谱数据做了预处理。
第二部分研究高光谱遥感海岸带分类技术。分析了海岸各类地物的光谱特性,并介绍了基本的遥感图像分类算法,在基本的分类算法的基础上,根据海岸带地物光谱特性,设计了海岸带混合决策分类算法,实现海岸带地表覆盖信息的初步分类。
第三部分利用高光谱遥感资料研究海岸带沙滩表面承载特性。对海岸沙滩的光谱影响因素以及沙滩表面承载特性进行了实验分析,并根据实验分析的结论利用高分辨高光谱遥感数据制作了海岸带沙滩表面特性的专题图,最后探讨了高光谱数据在海岸带探测方面应用。
Hyperspectral remote sensing data has the characteristic of combining both image data and spectral data into one data set, each pixel of a hyperspectral image an entire spectrum (consisting of numerous contiguous, narrow spectral bands) is present. Compared with conventional multi-spectral remote sensing data, hyperspectral remote sensing data can be more useful for the identification and classification of surface Materials. In this paper, the author classified the coastal area using hyperspectral remote sensing data, and further more, succeeded in extraction of sand beach surface information related to bearing capacity characteristic. This thesis consists of the following three parts.In the first part, the author firstly reviewed hyperspectral technology, include its basic conception, theories and its developing history, then introduced preprocess of hyperspectral data concerning atmospheric correction, geometric correction and its basic analysis techniques. Finally the write preprocessed the hyperspectral data used in this thesis.In the second part, mainly concern with coastal area classification techniques using hyperspectral data. Based on analysis of spectrum features of coastal surface materials and basic classification method, the author proposed a hybrid decision tree classification algorithm and classified the coastal area.In the third part, mainly study on sand beach surface characteristic of bearing capacity by hyperspectral remote sensing data. Based on analysis of field spectrum of sand beach and its surface characteristic, the author made a thematic map, which indicated sand beach surface characteristic of bearing capacity. Final, the author discussed hyperspectral and its applications detecting coastal zone.
第一部分概要介绍了高光谱遥感技术。首先回顾了高光谱遥感的基本概念、原理和发展历史,然后介绍了高光谱遥感数据的预处理,包括大气校正,几何校正,以及高光谱遥感技术的主要分析方法,最后对试验高光谱数据做了预处理。
第二部分研究高光谱遥感海岸带分类技术。分析了海岸各类地物的光谱特性,并介绍了基本的遥感图像分类算法,在基本的分类算法的基础上,根据海岸带地物光谱特性,设计了海岸带混合决策分类算法,实现海岸带地表覆盖信息的初步分类。
第三部分利用高光谱遥感资料研究海岸带沙滩表面承载特性。对海岸沙滩的光谱影响因素以及沙滩表面承载特性进行了实验分析,并根据实验分析的结论利用高分辨高光谱遥感数据制作了海岸带沙滩表面特性的专题图,最后探讨了高光谱数据在海岸带探测方面应用。
Hyperspectral remote sensing data has the characteristic of combining both image data and spectral data into one data set, each pixel of a hyperspectral image an entire spectrum (consisting of numerous contiguous, narrow spectral bands) is present. Compared with conventional multi-spectral remote sensing data, hyperspectral remote sensing data can be more useful for the identification and classification of surface Materials. In this paper, the author classified the coastal area using hyperspectral remote sensing data, and further more, succeeded in extraction of sand beach surface information related to bearing capacity characteristic. This thesis consists of the following three parts.In the first part, the author firstly reviewed hyperspectral technology, include its basic conception, theories and its developing history, then introduced preprocess of hyperspectral data concerning atmospheric correction, geometric correction and its basic analysis techniques. Finally the write preprocessed the hyperspectral data used in this thesis.In the second part, mainly concern with coastal area classification techniques using hyperspectral data. Based on analysis of spectrum features of coastal surface materials and basic classification method, the author proposed a hybrid decision tree classification algorithm and classified the coastal area.In the third part, mainly study on sand beach surface characteristic of bearing capacity by hyperspectral remote sensing data. Based on analysis of field spectrum of sand beach and its surface characteristic, the author made a thematic map, which indicated sand beach surface characteristic of bearing capacity. Final, the author discussed hyperspectral and its applications detecting coastal zone.
引文
[1] 林敏基,海洋与海岸带遥感应用[M],北京,海洋出版社,1991
[2] 肖永茂,江苏北部海岸带卫星图像处理方法研究[J],遥感信息,1995,
[3] A. P. CRACKNELL. Remote sensing techniques in estuaries and coastal zones -an update Int J. Remote Sensing, 1999, 19(3)
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[7] G. Smith, A. Thomson, et al. Potential of hyperspectral imaging to assess the stability of mudflat surfaces by mapping sediment characteristics. Proceedings of SPIE,2003,4886.
[8] Shu Gao, Michael Collins Sinking Depths of Sand surface over an intertidal area within a tidal inlet channel, Journal of coastal research, 1996, 1000-1004
[9] Green Robert O et al, Imageing spectroscopy and the airborne visible/infrared spectrometer (AVIRIS), Remote Sens. Environ, 1998,65,227-248
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[11] www.space.gc.ca
[12] 刘建贵,PHI成像光谱图像反射率转换,遥感学报,1999
[13] David Landgrebe, Information Extraction principles and Methods for Multispectral and Hyperspectral Image Data, Information Processing for Remote Sensing,1998
[14] 王晋年,张兵,以地物识别和分类为目标的高光谱数据挖掘,中国图象图形学报,1999,957-964
[15] 张兵,时空信息辅助下的高光谱数据挖掘,中国科学院博士学位论文
[16] 白继伟,基于高光谱数据库的光谱匹配技术研究,中国科学院硕士学位论文
[17] Miyatake S, Lee K. Mapping alteration minerals in northern Cuprite and Goldfieled, Nevada, with JERS-1 OPS data, Proceedings of the Twelfth International Conference and Workshops on Applied Geologic Remote Sensing, Denver, Colorado, 17-19 November 1997, Volume Ⅱ,473-484
[18] Yang H, et al. A back-propagation neural network for raineralogical mapping from AVIRIS data, Proceedings of the Third International Airborne Remote Sensing Conference and Exhibition,7-10 July, Copenhagen, Denmark, Volume, 1997, Ⅰ ,256-272
[19] Baugh W M, Kruse F A, Quantitative geochemical mapping of ammonoum minerals in the southern Ceder Mountains, Nevada, using airborne visible/infrared imagingspectrometer (AVIRIS), Remote Sensing of Environment, 1998, 65,292-308
[20] 童庆禧,郑兰芬,王晋年等,湿地植被成像光谱遥感研究,遥感学报,1997,50-57
[21] Boardrnan J W, Inversion of high spectral resolution data, Image Spectroscopy of the Terrestrial Environment,Proc SPIE,1990,1298,222-233
[22] 王晋年,郑兰芬,童庆禧,成像光谱图像吸收鉴别模型与矿物填图研究,环境遥感,1996
[23] 白继伟,赵永超,基于包络线消除的高光谱图像分类方法研究,计算机工程与应用,2003
[24] Cloutis E A. Hyperspectral geological remote sensing: evaluation of analytical techniques. Int J. Remote Sensing, 1996,20(1),37～46
[25] 吕长春,王忠武,混合像元分解模型综述,遥感信息,2003,55～60
[26] Charles Ichoku, Arnon Karnieli, A review of mixture modeling techniques for sub2pixel 1and cover estimation, Remote Sensing Reviews , Vol.13: 161～186.
[27] Yi Ma, Jie Zhang, Preliminary research on dominant species identification of red tide organism by airborne hyperspectral technique, Proceedings of SPIE, 4892,278-286
[28] 韩震,恽才兴,悬浮泥沙反射光谱特性实验研究,水利学报,2003,12,118-122
[29] 吕国楷,遥感概论,北京,高等教育出版社,1995
[30] 陈述彭,童庆禧,高光谱分辨率遥感信息机理与地物识别,遥感信息机理研究,北京,科学出版社,1998,139-231
[31] 孙家抦,遥感原理与应用,武汉,武汉大学出版社,2003,p204
[32] 万建,王继成,基于算法的彩色图像分割ISODATA,计算机工程,2002(5),135
[33] Hughes G.F., On the mean accuracy of statistical pattern recognizers, IEEE Trans. Information Theory, Vol. IT-14,1968,55-63
[34] James Norman Sweet, The Spectral Similarity Scale and its application to the classification of hyperspectral remote sensing data, Ph.D thesis of State University of New York
[35] 张丰,熊桢,高光谱遥感数据用于水稻精细分类研究,武汉理工大学学报,2002,36-39
[36] Friedl MarkA, Brodley C E. Decision Tree Classification of Land Cover from Remotely Sensed Data. Remote Sensing of Environment, 1997, 61,399-409
[37] HansenM, DubayahR, DefriesR. Classification Trees: An Alternative to Traditional Land Cover Classifiers, Int Remote sensing, 1996,17(5), 1075-1081
[38] 刘建贵,张兵,基于光谱特征的城市人工地物分级分类方法研究,遥感技术与应用,1999,Vol 14
[39] 徐彬彬,等.土壤光谱反射特性与理化性状的相关分析,宁芜土壤遥感研究文集,科学出版社,1987
[40] 刘伟东,高光谱遥感土壤信息提取与挖掘研究,中科院博士学位研究生学位论文
[41] 王昌佐,等,裸土表层含水量高光谱遥感的最佳波段选择.遥感信息,2003
[2] 肖永茂,江苏北部海岸带卫星图像处理方法研究[J],遥感信息,1995,
[3] A. P. CRACKNELL. Remote sensing techniques in estuaries and coastal zones -an update Int J. Remote Sensing, 1999, 19(3)
[4] Barf Deronde, ect, Sand dynamics along the Belgian coast based on airborne hyperspectral data and lidar data, EARSeL eProceedings 3,1/2004
[5] 浦瑞良,宫鹏,高光谱遥感及其应用[M],北京,高等教育出版社,2000
[6] 郑兰芬,王晋年,成像光谱技术及其图像光谱信息提取分析研究,环境遥感,1992,7(1):49～58
[7] G. Smith, A. Thomson, et al. Potential of hyperspectral imaging to assess the stability of mudflat surfaces by mapping sediment characteristics. Proceedings of SPIE,2003,4886.
[8] Shu Gao, Michael Collins Sinking Depths of Sand surface over an intertidal area within a tidal inlet channel, Journal of coastal research, 1996, 1000-1004
[9] Green Robert O et al, Imageing spectroscopy and the airborne visible/infrared spectrometer (AVIRIS), Remote Sens. Environ, 1998,65,227-248
[10] Hunt G R, Electromagnetic radiation :the communication link in remote sensing, In: Remote Sensing in Geology, Siegal B, Gilespia A, Wiley New York,1980,702
[11] www.space.gc.ca
[12] 刘建贵,PHI成像光谱图像反射率转换,遥感学报,1999
[13] David Landgrebe, Information Extraction principles and Methods for Multispectral and Hyperspectral Image Data, Information Processing for Remote Sensing,1998
[14] 王晋年,张兵,以地物识别和分类为目标的高光谱数据挖掘,中国图象图形学报,1999,957-964
[15] 张兵,时空信息辅助下的高光谱数据挖掘,中国科学院博士学位论文
[16] 白继伟,基于高光谱数据库的光谱匹配技术研究,中国科学院硕士学位论文
[17] Miyatake S, Lee K. Mapping alteration minerals in northern Cuprite and Goldfieled, Nevada, with JERS-1 OPS data, Proceedings of the Twelfth International Conference and Workshops on Applied Geologic Remote Sensing, Denver, Colorado, 17-19 November 1997, Volume Ⅱ,473-484
[18] Yang H, et al. A back-propagation neural network for raineralogical mapping from AVIRIS data, Proceedings of the Third International Airborne Remote Sensing Conference and Exhibition,7-10 July, Copenhagen, Denmark, Volume, 1997, Ⅰ ,256-272
[19] Baugh W M, Kruse F A, Quantitative geochemical mapping of ammonoum minerals in the southern Ceder Mountains, Nevada, using airborne visible/infrared imagingspectrometer (AVIRIS), Remote Sensing of Environment, 1998, 65,292-308
[20] 童庆禧,郑兰芬,王晋年等,湿地植被成像光谱遥感研究,遥感学报,1997,50-57
[21] Boardrnan J W, Inversion of high spectral resolution data, Image Spectroscopy of the Terrestrial Environment,Proc SPIE,1990,1298,222-233
[22] 王晋年,郑兰芬,童庆禧,成像光谱图像吸收鉴别模型与矿物填图研究,环境遥感,1996
[23] 白继伟,赵永超,基于包络线消除的高光谱图像分类方法研究,计算机工程与应用,2003
[24] Cloutis E A. Hyperspectral geological remote sensing: evaluation of analytical techniques. Int J. Remote Sensing, 1996,20(1),37～46
[25] 吕长春,王忠武,混合像元分解模型综述,遥感信息,2003,55～60
[26] Charles Ichoku, Arnon Karnieli, A review of mixture modeling techniques for sub2pixel 1and cover estimation, Remote Sensing Reviews , Vol.13: 161～186.
[27] Yi Ma, Jie Zhang, Preliminary research on dominant species identification of red tide organism by airborne hyperspectral technique, Proceedings of SPIE, 4892,278-286
[28] 韩震,恽才兴,悬浮泥沙反射光谱特性实验研究,水利学报,2003,12,118-122
[29] 吕国楷,遥感概论,北京,高等教育出版社,1995
[30] 陈述彭,童庆禧,高光谱分辨率遥感信息机理与地物识别,遥感信息机理研究,北京,科学出版社,1998,139-231
[31] 孙家抦,遥感原理与应用,武汉,武汉大学出版社,2003,p204
[32] 万建,王继成,基于算法的彩色图像分割ISODATA,计算机工程,2002(5),135
[33] Hughes G.F., On the mean accuracy of statistical pattern recognizers, IEEE Trans. Information Theory, Vol. IT-14,1968,55-63
[34] James Norman Sweet, The Spectral Similarity Scale and its application to the classification of hyperspectral remote sensing data, Ph.D thesis of State University of New York
[35] 张丰,熊桢,高光谱遥感数据用于水稻精细分类研究,武汉理工大学学报,2002,36-39
[36] Friedl MarkA, Brodley C E. Decision Tree Classification of Land Cover from Remotely Sensed Data. Remote Sensing of Environment, 1997, 61,399-409
[37] HansenM, DubayahR, DefriesR. Classification Trees: An Alternative to Traditional Land Cover Classifiers, Int Remote sensing, 1996,17(5), 1075-1081
[38] 刘建贵,张兵,基于光谱特征的城市人工地物分级分类方法研究,遥感技术与应用,1999,Vol 14
[39] 徐彬彬,等.土壤光谱反射特性与理化性状的相关分析,宁芜土壤遥感研究文集,科学出版社,1987
[40] 刘伟东,高光谱遥感土壤信息提取与挖掘研究,中科院博士学位研究生学位论文
[41] 王昌佐,等,裸土表层含水量高光谱遥感的最佳波段选择.遥感信息,2003