矿物光谱特征谱段识别方法与应用
|
摘要
矿物光谱识别所使用的光谱参量在不同影响因素下的稳定性对矿物识别效果影响很大。基于特征谱段峰谷相关系数法,提出了高光谱矿物识别的新算法,建立了提取稳定光谱参量(峰谷位置)的数学模型和操作流程。该算法通过提取矿物参考光谱峰谷的位置,计算矿物特征谱段的峰和谷与待测矿物光谱相应谱段的相关系数,并以此作为比较矿物光谱相似程度的主要依据。以甘肃北山方山口地区拾金坡金矿床为例,研究并比较新算法与典型算法的蚀变矿物识别。结果表明,本文算法的正确识别率为85%,较典型算法有更好的识别效果。
The stability of the spectrum parameters(width, depth and shape) in a mineral spectrum identification under differentinfluencing factors is greatly influenced by the identification effects. It is shown that the positions of the peak and the valley of differentminerals in the characteristic spectrum are more stable, and they are relatively stable characteristic parameters of the spectrum. This paperproposes a hyperspectral mineral identification algorithm based on the characteristic spectrum peak-valley correlation coefficient method,and the mathematical model and the operation flowchart of extracting the spectral stability parameters(the locations of the peak and thevalley) are established. The algorithm is based on the extraction of the reference spectra peak-valley positions, and the calculations of thepeaks and the valleys of minerals characteristic spectrum and the correlation coefficient of the corresponding measured mineral spectrum, todetermine whether they exceed the thresholds, as the main basis of comparison of the similarity degree of mineral spectra. Gansu BeishanShijinpo gold mining is taken as the study area, using the CAIS/SASI airborne hyperspectral data, and the algorithm is used to identify theregions of alteration minerals, and the results are compared with those obtained with the existing typical algorithms(SFF、SID、SAM). It isshown that the correct recognition rate of the algorithm is higher, and the accuracy of the algorithm can reach 85%.
The stability of the spectrum parameters(width, depth and shape) in a mineral spectrum identification under differentinfluencing factors is greatly influenced by the identification effects. It is shown that the positions of the peak and the valley of differentminerals in the characteristic spectrum are more stable, and they are relatively stable characteristic parameters of the spectrum. This paperproposes a hyperspectral mineral identification algorithm based on the characteristic spectrum peak-valley correlation coefficient method,and the mathematical model and the operation flowchart of extracting the spectral stability parameters(the locations of the peak and thevalley) are established. The algorithm is based on the extraction of the reference spectra peak-valley positions, and the calculations of thepeaks and the valleys of minerals characteristic spectrum and the correlation coefficient of the corresponding measured mineral spectrum, todetermine whether they exceed the thresholds, as the main basis of comparison of the similarity degree of mineral spectra. Gansu BeishanShijinpo gold mining is taken as the study area, using the CAIS/SASI airborne hyperspectral data, and the algorithm is used to identify theregions of alteration minerals, and the results are compared with those obtained with the existing typical algorithms(SFF、SID、SAM). It isshown that the correct recognition rate of the algorithm is higher, and the accuracy of the algorithm can reach 85%.
引文
[1]王润生,甘甫平,闫柏琨,等.高光谱矿物填图技术与应用研究[J].国土资源遥感,2010(1):1-13.Wang Runsheng,Gan Fuping,Yan Bokun,et al.Hyperspectral mineral mapping and its application[J].Remote Sensing for Land&Resources,2010(1):1-13.
[2]李浩杰,常晓珂.敦煌白山地区遥感蚀变信息提取与分析[J].资源环境与工程,2016,30(1):114-118.Li Haojie,Chang Xiaoke.Extraction and analysis of information of remote sensing alteration of Dunhuang Baishan area[J].Resources Environment&Engineering,2016,30(1):114-118.
[3]唐攀科,李永丽,李国斌,等.成像光谱遥感技术及其在地质中的应用[J].矿产与地质,2006,20(2):160-165.Tang Panke,Li Yongli,Li Guobin,et al.Imaging spectrometry remote sensing technology and its applications in geology[J].Mineral Resources and Geology,2006,20(2):160-165.
[4]田丰.全波段(0.35~25μm)高光谱遥感矿物识别和定量化反演技术研究[D].北京:中国地质大学,2010.Tian Feng.Identification and quantitative retrival of minerals information integrating VIS-NIR-MIR-TIR(0.35~25μm)hyspectral data[D].Beijing:China University of Geosciences,2010.
[5]李楠,肖克炎,陈析缪,等.基于Hyperion高光谱数据的矿物蚀变提取-以内蒙古西部狼山地区炭窑口矿床为例[J].地质通报,2010,29(10):1558-1563.Li Na,Xiao Keyan,Chen Xiqiu,et al.Mineralizing alteration extraction based on hyperion hyper-spectral data-taking Tanyaokou deposit,Inner Mongolia,China as an example[J].Geological Bulletin of China,2010,29(10):1558-1563.
[6]Bo D,Yu X Z,Liang P Z,et al.A hypothess independent subpixel target detector for hyperspectral images[J].Signal Processing,2015,110:244-249.
[7]吴浩,徐元进,高冉.基于光谱相关角和光谱信息散度的高光谱蚀变信息提取[J].地理与地理信息科学,2016,32(1):44-48.Wu Hao,Xu Yuanjin,Gao Ran.Extraction of alteration information from hyperspectral imagery based on SCA and SID[J].Geography and Geo-Information Science,2016,32(1):44-48.
[8]杨燕杰,赵英俊.航空成像光谱的蚀变信息提取技术[J].科技导报,2011,29(23):57-61.Yang Yanjie,Zhao Yingjun.Extraction of alteration information based on airborne hyperspectral image[J].Science&Technology Review,2011,29(23):57-61.
[9]周强,甘甫平,王润生,等.高光谱遥感影像矿物自动识别与应用[J].国土资源遥感,2005(4):28-31.Zhou Qiang,Gan Fuping,Wang Runsheng,et al.Mineral auto-identification based on hyperspectral imaging data and its application[J].Remote Sensing for Land&Resources,2005(4):28-31.
[10]牛志宇,赵慧洁.基于光谱知识的高光谱图像自动识别方法[J].北京航空航天大学学报,2012,38(2):280-284.Niu Zhiyu,Zhao Huijie.Automatic recognition of hyperspectral image based on spertral knowledge[J].Journal of Beijing University of Aeronautics and Astronautics,2012,38(2):280-284.
[11]Van der Meer F.The effectiveness of spectral similarity measures for the analysis of hyperspectral imagery[J].International Gournal of Applied Rarth Observation and Geoinformation,2006,8(1):3-17.
[12]Baugh W M,Kruse F A,Atkinson W W.Quantitative geochemical mapping of ammonium minerals in the southern cedar mountains,Nevada,using the airborne visible/infrared imaging spectrometer(AVIRIS)[J].Remote Sensing of Environment,1998,65(3):292-308.
[13]甘甫平,王润生,马蔼乃.基于特征谱带的高光谱遥感矿物谱系识别[J].地学前缘,2003,10(2):445-454.Gan Fuping,Wang Runsheng,Ma Ainai.Spectral identification tree(sit)for mineral extraction based on spectral characteristics of minerals[J].Earth Science Frontiers,2003,10(2):445-454.
[14]余先川,熊利平,徐金东,等.基于二次散射非线性混合模型的矿物填图方法[J].国土资源遥感,2014,26(2):60-68.Yu Xianchuan,Xiong Liping,Xu Jindong,et al.Mineral mapping based on secondary scattering mixture model[J].Remote Sensing for Land&Resources,2014,26(2):60-68.
[15]王润生,杨苏明,阎柏琨.成像光谱矿物识别方法与识别模型评述[J].国土资源遥感,2007(1):1-9.Wang Runsheng,Yang Suming,Yan Bokun.A review of mineral spectral identification methods and models with imaging spectrometer[J].Remote Sensing for Land&Resources,2007(1):1-9.
[16]甘甫平,王润生,江思宏,等.基于完全谱形特征的成像光谱遥感岩矿识别技术及其应用[J].地质科学,2000,35(3):376-384.Gan Fuping,Wang Runsheng,Jiang Sihong,et al.Discrimination technique for rocks or ore deposits based on the feature of full spectral shape using hyperspectral remote sensing and its application[J].Science Geologica,2000,35(3):376-384.
[2]李浩杰,常晓珂.敦煌白山地区遥感蚀变信息提取与分析[J].资源环境与工程,2016,30(1):114-118.Li Haojie,Chang Xiaoke.Extraction and analysis of information of remote sensing alteration of Dunhuang Baishan area[J].Resources Environment&Engineering,2016,30(1):114-118.
[3]唐攀科,李永丽,李国斌,等.成像光谱遥感技术及其在地质中的应用[J].矿产与地质,2006,20(2):160-165.Tang Panke,Li Yongli,Li Guobin,et al.Imaging spectrometry remote sensing technology and its applications in geology[J].Mineral Resources and Geology,2006,20(2):160-165.
[4]田丰.全波段(0.35~25μm)高光谱遥感矿物识别和定量化反演技术研究[D].北京:中国地质大学,2010.Tian Feng.Identification and quantitative retrival of minerals information integrating VIS-NIR-MIR-TIR(0.35~25μm)hyspectral data[D].Beijing:China University of Geosciences,2010.
[5]李楠,肖克炎,陈析缪,等.基于Hyperion高光谱数据的矿物蚀变提取-以内蒙古西部狼山地区炭窑口矿床为例[J].地质通报,2010,29(10):1558-1563.Li Na,Xiao Keyan,Chen Xiqiu,et al.Mineralizing alteration extraction based on hyperion hyper-spectral data-taking Tanyaokou deposit,Inner Mongolia,China as an example[J].Geological Bulletin of China,2010,29(10):1558-1563.
[6]Bo D,Yu X Z,Liang P Z,et al.A hypothess independent subpixel target detector for hyperspectral images[J].Signal Processing,2015,110:244-249.
[7]吴浩,徐元进,高冉.基于光谱相关角和光谱信息散度的高光谱蚀变信息提取[J].地理与地理信息科学,2016,32(1):44-48.Wu Hao,Xu Yuanjin,Gao Ran.Extraction of alteration information from hyperspectral imagery based on SCA and SID[J].Geography and Geo-Information Science,2016,32(1):44-48.
[8]杨燕杰,赵英俊.航空成像光谱的蚀变信息提取技术[J].科技导报,2011,29(23):57-61.Yang Yanjie,Zhao Yingjun.Extraction of alteration information based on airborne hyperspectral image[J].Science&Technology Review,2011,29(23):57-61.
[9]周强,甘甫平,王润生,等.高光谱遥感影像矿物自动识别与应用[J].国土资源遥感,2005(4):28-31.Zhou Qiang,Gan Fuping,Wang Runsheng,et al.Mineral auto-identification based on hyperspectral imaging data and its application[J].Remote Sensing for Land&Resources,2005(4):28-31.
[10]牛志宇,赵慧洁.基于光谱知识的高光谱图像自动识别方法[J].北京航空航天大学学报,2012,38(2):280-284.Niu Zhiyu,Zhao Huijie.Automatic recognition of hyperspectral image based on spertral knowledge[J].Journal of Beijing University of Aeronautics and Astronautics,2012,38(2):280-284.
[11]Van der Meer F.The effectiveness of spectral similarity measures for the analysis of hyperspectral imagery[J].International Gournal of Applied Rarth Observation and Geoinformation,2006,8(1):3-17.
[12]Baugh W M,Kruse F A,Atkinson W W.Quantitative geochemical mapping of ammonium minerals in the southern cedar mountains,Nevada,using the airborne visible/infrared imaging spectrometer(AVIRIS)[J].Remote Sensing of Environment,1998,65(3):292-308.
[13]甘甫平,王润生,马蔼乃.基于特征谱带的高光谱遥感矿物谱系识别[J].地学前缘,2003,10(2):445-454.Gan Fuping,Wang Runsheng,Ma Ainai.Spectral identification tree(sit)for mineral extraction based on spectral characteristics of minerals[J].Earth Science Frontiers,2003,10(2):445-454.
[14]余先川,熊利平,徐金东,等.基于二次散射非线性混合模型的矿物填图方法[J].国土资源遥感,2014,26(2):60-68.Yu Xianchuan,Xiong Liping,Xu Jindong,et al.Mineral mapping based on secondary scattering mixture model[J].Remote Sensing for Land&Resources,2014,26(2):60-68.
[15]王润生,杨苏明,阎柏琨.成像光谱矿物识别方法与识别模型评述[J].国土资源遥感,2007(1):1-9.Wang Runsheng,Yang Suming,Yan Bokun.A review of mineral spectral identification methods and models with imaging spectrometer[J].Remote Sensing for Land&Resources,2007(1):1-9.
[16]甘甫平,王润生,江思宏,等.基于完全谱形特征的成像光谱遥感岩矿识别技术及其应用[J].地质科学,2000,35(3):376-384.Gan Fuping,Wang Runsheng,Jiang Sihong,et al.Discrimination technique for rocks or ore deposits based on the feature of full spectral shape using hyperspectral remote sensing and its application[J].Science Geologica,2000,35(3):376-384.