干涉成像光谱仪光谱应用技术研究
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摘要
针对西安光机所研制的星载高光谱成像仪和月球干涉成像光谱仪光谱应用技术展开研究。首先讨论了数据预处理技术，其次针对星载高光谱成像仪进行了地物覆盖光谱分类技术和目标探测技术研究。最后针对月球干涉成像光谱仪进行了矿物质混合光谱分解技术研究。
讨论了数据预处理技术，主要是辐射校正和反射率反演方法。利用此方法处理星载高光谱成像仪飞行实验数据，得到了可信的反射率光谱图像。
利用新的基于端元的地物识别分类技术处理了飞行实验数据，得到了较好的分类结果。
利用BandMax和SAM相结合的技术处理飞行实验数据进行目标探测，假定目标(汽车)大部分被探测到。
针对月球干涉成像光谱仪进行混合光谱分解技术研究，讨论了高光谱解混合的三个步骤，重点介绍解混合(inversion逆问题求解)这一步骤。最后对AVIRIS获取的Cuprite地区的矿物质进行混合光谱分解，结果可靠。为即将获取的月球高光谱影像分析提供了一种有效途径。
Hyperspectral data application for land cover and mineral analysis is discussed in this paper. Four main parts are included: Hyperspectral data preprocessing, Land cover classification using an endmember-based method, Target detection and mineral mapping using spectral unmixing.
Hyperspectral data preprocessing is discussed, including radiation correction and atmospheric correction. The flying data is processed using this preprocessing method, and they get a credible reflectance image.
A new method for land cover mapping, endmember-based identification and classification, is used to process the flying data. They get a satisfying classification result.
The interesting targets (cars) are detected using BandMax and SAM. They get a satisfying detection result.
Mineral mapping for the lunar interference imaging spectrometer is studied. Three steps for spectral unmixing are discussed. They process the hyperspectral image of the Cuprite derived by AVIRIS using the spectral unmixing method for mineral mapping. This method can be used in lunar hyperspectral image processing future.
讨论了数据预处理技术，主要是辐射校正和反射率反演方法。利用此方法处理星载高光谱成像仪飞行实验数据，得到了可信的反射率光谱图像。
利用新的基于端元的地物识别分类技术处理了飞行实验数据，得到了较好的分类结果。
利用BandMax和SAM相结合的技术处理飞行实验数据进行目标探测，假定目标(汽车)大部分被探测到。
针对月球干涉成像光谱仪进行混合光谱分解技术研究，讨论了高光谱解混合的三个步骤，重点介绍解混合(inversion逆问题求解)这一步骤。最后对AVIRIS获取的Cuprite地区的矿物质进行混合光谱分解，结果可靠。为即将获取的月球高光谱影像分析提供了一种有效途径。
Hyperspectral data application for land cover and mineral analysis is discussed in this paper. Four main parts are included: Hyperspectral data preprocessing, Land cover classification using an endmember-based method, Target detection and mineral mapping using spectral unmixing.
Hyperspectral data preprocessing is discussed, including radiation correction and atmospheric correction. The flying data is processed using this preprocessing method, and they get a credible reflectance image.
A new method for land cover mapping, endmember-based identification and classification, is used to process the flying data. They get a satisfying classification result.
The interesting targets (cars) are detected using BandMax and SAM. They get a satisfying detection result.
Mineral mapping for the lunar interference imaging spectrometer is studied. Three steps for spectral unmixing are discussed. They process the hyperspectral image of the Cuprite derived by AVIRIS using the spectral unmixing method for mineral mapping. This method can be used in lunar hyperspectral image processing future.
引文
[1] 薛彬，“CE-1干涉成像光谱仪信息处理及应用研究”，博士论文，中国科学院西安光学精密机械研究所，2006
[2] 陈琦，“大孔径静态干涉成像光谱仪定标技术研究”，硕士论文，中国科学院西安光学精密机械研究所，2006
[3] John R．Jensen著，陈晓玲等译．遥感数字影像处理导论．北京：机械工业出版社．2007
[4] 北京星图环宇科技公司编著．ENVI遥感影像处理专题与实践．2005
[5] 王晋年，张兵等．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，1999，11(4)：957～964
[6] Peg Shippert, Introduction to Hyperspectral Image Analysis. Research Systems, Inc.
[7] D.Manolakis, et al. Hyperspectral Image Processing for Automatic Target Detection Applications. In: Proceedings, Lincoln Laboratory Journal, Volume 14, Number 1, 2003
[8] 姜景山等著．空间科学与应用．北京：科学出版社．2001．39～40
[9] 张兵著．高光谱遥感课程．中科院遥感所遥感科学国家重点实验室．2006
[10] D.Manolakis, et al. Hyperspectral Subpixel Target Detection Using the Linear Mixing Model. IEEE Transactions on Geoscience and Remote Sensing, Vol.39, No.7, July 2001.
[11] Michael K. Griffin, et al. Compensation of Hyperspectral Data for Atmospheric Effects. In: Proceedings, Lincoln Laboratory Journal, Volume 14, Number 1, 2003
[12] Uttam Kumar. Comparative Evaluation of the Algorithms for Land Cover Mapping using Hyperspectral Data. Jan.2006.
[13] 许卫东．高光谱遥感分类与提取技术．《红外》月刊，May 2004：28～34
[14] 杨哲海，冯猛，等．高光谱影像处理方法的改进．海洋测绘，2004年7月， 第24卷第4期：13～15
[15] Wendy Zhang and Shobha Sriharan. Using hyperspectral remote sensing for land cover classification. Proc. of SPIE Vol. 5655:261～270
[16] Fred A. Kruse, Joseph W. Boardman, and Adam B. Lefkoff. Extraction of Compositional Information for Trafficability Mapping from Hyperspectral Data. In: Proceedings, SPIE International Symposium on Aerosense[C]. 24-28 April 2000C, Orlando.
[17] ENVI4.1 Online Help. Research Systems, Inc., 2002.
[18] Nirmal Keshava. A Survey of Spectral Unmixing Algorithms. In: Proceedings, Lincoln Laboratory Journal, Volume 14, Number 1, 2003
[19] 施介岚等．以光谱混合分析法进行台湾地区Master影像之研究．第二十三届测量学术及应用研讨会．2004．
[20] 薛绮，“基于线性混合模型的高光谱图像端元提取方法研究”，硕士论文，国防科学技术大学，2004
[21] 罗红霞，龚健雅．UFCLS线性光谱混合分析法在遥感图像分类识别中的效果分析．武汉大学学报信息科学版，2004年7月，第29卷第7期：615～618
[22] Nirmal Keshava and John F. Mustard. Spectral Unmixing. In: Proceedings, IEEE Signal Processing Magazine, January 2002
[23] Berhanu Kefale Alemie. Spectral unmixing of hyperspectral and multispectral images for predictive mapping of surface soil organic matter. March 2005
[24] ENVI4.1 Online Help. Research Systems, Inc., 2002.
[25] 薛彬，赵葆常，杨建峰，吕群波．改进的线性混合模型用于高光谱分离实验模拟．光子学报，2004，33(6)：689～692
[26] Dimitris Manolakis, et al. Detection Algorithms for Hyperspectral Imaging Data Exploitation. ASAP 2002, 12-14 March 2002.
[27] Dimitris Manolakis, et al. Comparative Analysis of Hyperspectral Adaptive Matched Filter Detectors. In: Proceedings, SPIE Vol.4049, 2000
[28] Dimitris Manolakis. Overview of Algorithms for Hyperspectral Target Detection: Theory and Practice. In: Proceedings, SPIE Vol.4725, 2002
[29] Gary A. Shaw and Hsiao-hua K. Burke. Spectral Imaging for Remote Sensing. In: Proceedings, Lincoln Laboratory Journal, Volume 14, Number 1, 2003
[30] A. Schaum. Hyperspectral detection algorithms: From old ideas to operational concepts to next generation. In: Proceedings, SPIE Vol.6233, 2006
[31] 耿修瑞，“高光谱遥感目标探测与分类技术研究”，博士论文，中国科学院遥感应用研究所，2005
[32] 贺霖，潘泉，赵永强，等．基于波段子集特征融合的高光谱图像异常检测．光子学报，2005，33(11)：1752～1755
[33] 张兵，“时空信息辅助下的高光谱数据挖掘”，博士论文，中国科学院遥感应用研究所，2003
[34] Michael D. Farrell Jr. Analysis of Modeling, Training, and Dimension Reduction Approaches for Target Detection in Hyperspectral Imagery. December 2005
[35] 浦瑞良，宫鹏．高光谱遥感及其应用．北京：高等教育出版社．2000．
[36] ZHAO Yong-Chao, TONG Qing-Xi, ZHENG Lan-Fen, et al. A Kernel Adaptive Filter (SRSSHF) and Quality; Improvement Method for Hyperspectral Image on the Base of Spectral Dimension Recognition and Spatial Dimension Smoothing According to CSAM [C], SPIE: 2~(nd) International Symposium on Multispectral Image Processing and Pattern Recognition, 2001, 4552, 230-236
[2] 陈琦，“大孔径静态干涉成像光谱仪定标技术研究”，硕士论文，中国科学院西安光学精密机械研究所，2006
[3] John R．Jensen著，陈晓玲等译．遥感数字影像处理导论．北京：机械工业出版社．2007
[4] 北京星图环宇科技公司编著．ENVI遥感影像处理专题与实践．2005
[5] 王晋年，张兵等．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，1999，11(4)：957～964
[6] Peg Shippert, Introduction to Hyperspectral Image Analysis. Research Systems, Inc.
[7] D.Manolakis, et al. Hyperspectral Image Processing for Automatic Target Detection Applications. In: Proceedings, Lincoln Laboratory Journal, Volume 14, Number 1, 2003
[8] 姜景山等著．空间科学与应用．北京：科学出版社．2001．39～40
[9] 张兵著．高光谱遥感课程．中科院遥感所遥感科学国家重点实验室．2006
[10] D.Manolakis, et al. Hyperspectral Subpixel Target Detection Using the Linear Mixing Model. IEEE Transactions on Geoscience and Remote Sensing, Vol.39, No.7, July 2001.
[11] Michael K. Griffin, et al. Compensation of Hyperspectral Data for Atmospheric Effects. In: Proceedings, Lincoln Laboratory Journal, Volume 14, Number 1, 2003
[12] Uttam Kumar. Comparative Evaluation of the Algorithms for Land Cover Mapping using Hyperspectral Data. Jan.2006.
[13] 许卫东．高光谱遥感分类与提取技术．《红外》月刊，May 2004：28～34
[14] 杨哲海，冯猛，等．高光谱影像处理方法的改进．海洋测绘，2004年7月， 第24卷第4期：13～15
[15] Wendy Zhang and Shobha Sriharan. Using hyperspectral remote sensing for land cover classification. Proc. of SPIE Vol. 5655:261～270
[16] Fred A. Kruse, Joseph W. Boardman, and Adam B. Lefkoff. Extraction of Compositional Information for Trafficability Mapping from Hyperspectral Data. In: Proceedings, SPIE International Symposium on Aerosense[C]. 24-28 April 2000C, Orlando.
[17] ENVI4.1 Online Help. Research Systems, Inc., 2002.
[18] Nirmal Keshava. A Survey of Spectral Unmixing Algorithms. In: Proceedings, Lincoln Laboratory Journal, Volume 14, Number 1, 2003
[19] 施介岚等．以光谱混合分析法进行台湾地区Master影像之研究．第二十三届测量学术及应用研讨会．2004．
[20] 薛绮，“基于线性混合模型的高光谱图像端元提取方法研究”，硕士论文，国防科学技术大学，2004
[21] 罗红霞，龚健雅．UFCLS线性光谱混合分析法在遥感图像分类识别中的效果分析．武汉大学学报信息科学版，2004年7月，第29卷第7期：615～618
[22] Nirmal Keshava and John F. Mustard. Spectral Unmixing. In: Proceedings, IEEE Signal Processing Magazine, January 2002
[23] Berhanu Kefale Alemie. Spectral unmixing of hyperspectral and multispectral images for predictive mapping of surface soil organic matter. March 2005
[24] ENVI4.1 Online Help. Research Systems, Inc., 2002.
[25] 薛彬，赵葆常，杨建峰，吕群波．改进的线性混合模型用于高光谱分离实验模拟．光子学报，2004，33(6)：689～692
[26] Dimitris Manolakis, et al. Detection Algorithms for Hyperspectral Imaging Data Exploitation. ASAP 2002, 12-14 March 2002.
[27] Dimitris Manolakis, et al. Comparative Analysis of Hyperspectral Adaptive Matched Filter Detectors. In: Proceedings, SPIE Vol.4049, 2000
[28] Dimitris Manolakis. Overview of Algorithms for Hyperspectral Target Detection: Theory and Practice. In: Proceedings, SPIE Vol.4725, 2002
[29] Gary A. Shaw and Hsiao-hua K. Burke. Spectral Imaging for Remote Sensing. In: Proceedings, Lincoln Laboratory Journal, Volume 14, Number 1, 2003
[30] A. Schaum. Hyperspectral detection algorithms: From old ideas to operational concepts to next generation. In: Proceedings, SPIE Vol.6233, 2006
[31] 耿修瑞，“高光谱遥感目标探测与分类技术研究”，博士论文，中国科学院遥感应用研究所，2005
[32] 贺霖，潘泉，赵永强，等．基于波段子集特征融合的高光谱图像异常检测．光子学报，2005，33(11)：1752～1755
[33] 张兵，“时空信息辅助下的高光谱数据挖掘”，博士论文，中国科学院遥感应用研究所，2003
[34] Michael D. Farrell Jr. Analysis of Modeling, Training, and Dimension Reduction Approaches for Target Detection in Hyperspectral Imagery. December 2005
[35] 浦瑞良，宫鹏．高光谱遥感及其应用．北京：高等教育出版社．2000．
[36] ZHAO Yong-Chao, TONG Qing-Xi, ZHENG Lan-Fen, et al. A Kernel Adaptive Filter (SRSSHF) and Quality; Improvement Method for Hyperspectral Image on the Base of Spectral Dimension Recognition and Spatial Dimension Smoothing According to CSAM [C], SPIE: 2~(nd) International Symposium on Multispectral Image Processing and Pattern Recognition, 2001, 4552, 230-236