改进的信息散度法在蚀变矿物分类中的研究
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摘要
为了有效获取待测光谱类型,采用信息散度匹配法对其进行分类处理。根据不同矿物具有不同的光谱特征曲线,建立不同矿物光谱曲线的标准光谱库,再采用信息散度法对待测矿物光谱与标准库中的光谱进行匹配,进而得出一种标准光谱库下的信息散度匹配法。采用该方法对实际光谱曲线进行分类处理,结果表明提出的方法能够有效对光谱曲线进行匹配,判别待测光谱所蕴含矿物类型,所得出的蚀变矿物类型与岩矿鉴定结果具有较高的吻合度,有利于提取蚀变矿物信息。
In order to identify the types of the spectrum under measurement effectively,the information divergence method is applied to classification of the measured spectra. The standard spectral library of different mineral spectral curves is set up according to different spectral characteristic curves of different minerals. The information divergence method is used to match the curves of the altered spectrum under measurement and the spectrum in the standard spectral library,so a new spectral information divergence method based on standard spectrum library is obtained. The new method is used to conduct the classification processing of the actual spectral curves. The results show that the proposed method can match the spectral curves effectively and identify the mineral types implied in the spectra under measurement. The altered mineral types got by the proposed method are consistent with the results of actual mineral identification,which is beneficial to the extraction of altered mineral information.
In order to identify the types of the spectrum under measurement effectively,the information divergence method is applied to classification of the measured spectra. The standard spectral library of different mineral spectral curves is set up according to different spectral characteristic curves of different minerals. The information divergence method is used to match the curves of the altered spectrum under measurement and the spectrum in the standard spectral library,so a new spectral information divergence method based on standard spectrum library is obtained. The new method is used to conduct the classification processing of the actual spectral curves. The results show that the proposed method can match the spectral curves effectively and identify the mineral types implied in the spectra under measurement. The altered mineral types got by the proposed method are consistent with the results of actual mineral identification,which is beneficial to the extraction of altered mineral information.
引文
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[13]申金媛,李航,刘润杰,等.基于相关系数的有效特征光谱筛选方法[J].郑州大学学报(理学版),2017,49(3):28-31.SHEN Jinyuan,LI Hang,LIU Runjie,et al. Screening the effective spectrum features based on correlation coefficient[J].Journal of Zhengzhou University(Natural science edition),2017,49(3):28-31.
[14]张正勇,沙敏,桂冬冬,等.基于移动窗口相关系数光谱法的白酒品牌快速判别技术研究[J].光谱学与光谱分析,2017,37(10):3122-3126.ZHANG Zhengyong,SHA Min,GUI Dongdong,et al. Research on the rapid discrimination technology of the Chinese liquor brands based on the moving window correlation coefficient spectral method[J]. Spectroscopy and spectral analysis,2017,37(10):3122-3126.
[2]秦飞龙.原生晕地球化学和岩心高光谱粗糙集耦合建模与深部矿产预测研究:以湖北鸡冠咀铜金矿床为例[D].成都:成都理工大学,2017.QIN Feilong. Coupling modeling of the primary halo geochemistry and the core hyperspectral with the rough set model and its studying in the prediction of deep mineral resources—take the Jiguanzui Cu-Au mineral deposit in Hubei as an example[D]. Chengdu:Chengdu University of Technology,2017.
[3] LI Y S,XU J J,XIA R J,et al. Extreme-constrained spatialspectral corner detector for image-level hyperspectral image classification[J]. Pattern recognition letters,2018,109(15):110-119.
[4]张成业,秦其明,陈理,等.高光谱遥感岩矿识别的研究进展[J].光学精密工程,2015,23(8):2407-2417.ZHANG Chengye,QIN Qiming,CHEN Li,et al. Research and development of mineral identification utilizing hyperspectral remote sensing[J]. Optics and precision engineering,2015,23(8):2407-2417.
[5]卢燕.高光谱矿物填图技术在金属矿产和油气勘查中的应用研究[D].北京:中国地质大学,2018.LU Yan. Applications of hyperspectral mineral mapping in mineral and petroleum exploration[D]. Beijing:China University of Geosciences,2018.
[6]秦飞龙,郭科,柳炳利.高光谱遥感影像分类算法并行处理设计与实现[J].成都理工大学学报(自然科学版),2016,43(1):119-123.QIN Feilong,GUO Ke,LIU Bingli. Parallel design and realization of classification algorithm of hyperspectral remote sensing image[J]. Journal of Chengdu University of Technology(Science&technology edition),2016,43(1):119-123.
[7]孙娅琴,田淑芳,王兴振,等.基于光谱匹配的热红外高光谱数据岩性分类研究[J].现代地质,2016,30(1):239-246.SUN Yaqin,TIAN Shufang,WANG Xingzhen,et al. An improved lithological classification method for thermal infrared hyperspectral data based on spectral matching[J]. Geoscience,2016,30(1):239-246.
[8]王春阳,张合兵,许志方,等.矩阵因子和信息散度融合的高光谱波段选择方法[J].遥感信息,2018,33(2):26-32.WANG Chunyang, ZHANG Hebing, XU Zhifang, et al. A new band selection method for herperspectral images based on matrix mode and spectral information divergence[J]. Remote sensing information,2018,33(2):26-32.
[9]彭建,徐飞雄,邓凯,等.琅琊山景区不同叶绿素条件下树种叶片光谱差异分析[J].光谱学与光谱分析,2018,38(6):1839-1849.PENG Jian,XU Feixiong,DENG Kai,et al. Spectral differences of tree leaves at different chlorophyll relative content in Langya mountain[J]. Spectroscopy and spectral analysis, 2018, 38(6):1839-1849.
[10]刘万军,杨秀红,曲海成.基于光谱信息散度与光谱角匹配的高光谱解混算法[J].计算机应用,2015,35(3):844-848.LIU Wanjun,YANG Xiuhong,QU Haicheng. Hyperspectral unmixing algorithm based on spectral information divergence and spectral angle mapping[J]. Journal of computer applications,2015,35(3):844-848.
[11] CHENG W W,SUN D W,PU H B,et al. Characterization of myofibrils cold structural deformation degrees of frozen pork using hyperspectral imaging coupled with spectral angle mapping algorithm[J]. Food chemistry,2018,23(15):1001-1008.
[12]王泓鹏,万雄.基于植物油可见吸收光谱的相关系数鉴别特级初榨橄榄油[J].光谱学与光谱分析,2018,38(9):2814-2819.WANG Hongpeng,WAN Xiong. Distinguishing extra virgin olive oil based on the correlation coefficient of visible absorption spectrum of vegetable oil[J]. Spectroscopy and spectral analysis,2018,38(9):2814-2819.
[13]申金媛,李航,刘润杰,等.基于相关系数的有效特征光谱筛选方法[J].郑州大学学报(理学版),2017,49(3):28-31.SHEN Jinyuan,LI Hang,LIU Runjie,et al. Screening the effective spectrum features based on correlation coefficient[J].Journal of Zhengzhou University(Natural science edition),2017,49(3):28-31.
[14]张正勇,沙敏,桂冬冬,等.基于移动窗口相关系数光谱法的白酒品牌快速判别技术研究[J].光谱学与光谱分析,2017,37(10):3122-3126.ZHANG Zhengyong,SHA Min,GUI Dongdong,et al. Research on the rapid discrimination technology of the Chinese liquor brands based on the moving window correlation coefficient spectral method[J]. Spectroscopy and spectral analysis,2017,37(10):3122-3126.