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It is shown that the positions of the peak and the valley of different minerals in the characteristic spectrum are more stable, and they are relatively stable characteristic parameters of the spectrum. This paper proposes 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 the valley) are established. The algorithm is based on the extraction of the reference spectra peak-valley positions, and the calculations of the peaks and the valleys of minerals characteristic spectrum and the correlation coefficient of the corresponding measured mineral spectrum, to determine whether they exceed the thresholds, as the main basis of comparison of the similarity degree of mineral spectra. Gansu Beishan Shijinpo gold mining is taken as the study area, using the CAIS/SASI airborne hyperspectral data, and the algorithm is used to identify the regions of alteration minerals, and the results are compared with those obtained with the existing typical algorithms (SFF、SID、SAM). It is shown that the correct recognition rate of the algorithm is higher, and the accuracy of the algorithm can reach 85%., authors=CHE Yongfei, ZHAO Yingjun, authorsList=CHE Yongfei, ZHAO Yingjun, authorCompany=National Key Laboratory of Remote Sensing Information and Imagery Analyzing Technology;Beijing Research Institute of Uranium Geology, Beijing 100029, China, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=95ciV0MCTz9OCkWeq+iXCQ==, pdfFileSize=1272908, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1242135785084887329, articleId=1242135782094352745, tenantId=1146029695717560320, journalId=1146031591421210625, language=CN, 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矿物光谱特征谱段识别方法与应用
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矿物光谱特征谱段识别方法与应用
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车永飞, 赵英俊
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    核工业北京地质研究院;遥感信息与图像分析技术国家级重点实验室, 北京 100029
Study on hyperspectral mineral identification based on characteristic spectrum peak-valley correlation coefficient method
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出版时间: 2017-02-28 doi: 10.3981/j.issn.1000-7857.2017.04.016
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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。
高光谱遥感  /  特征谱段  /  峰谷相关系数  /  矿物识别
The stability of the spectrum parameters (width, depth and shape) in a mineral spectrum identification under different influencing factors is greatly influenced by the identification effects. It is shown that the positions of the peak and the valley of different minerals in the characteristic spectrum are more stable, and they are relatively stable characteristic parameters of the spectrum. This paper proposes 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 the valley) are established. The algorithm is based on the extraction of the reference spectra peak-valley positions, and the calculations of the peaks and the valleys of minerals characteristic spectrum and the correlation coefficient of the corresponding measured mineral spectrum, to determine whether they exceed the thresholds, as the main basis of comparison of the similarity degree of mineral spectra. Gansu Beishan Shijinpo gold mining is taken as the study area, using the CAIS/SASI airborne hyperspectral data, and the algorithm is used to identify the regions of alteration minerals, and the results are compared with those obtained with the existing typical algorithms (SFF、SID、SAM). It is shown that the correct recognition rate of the algorithm is higher, and the accuracy of the algorithm can reach 85%.
hyperspectral remote sensing  /  characteristic spectrum  /  peak-valley correlation coefficient method  /  mineral information identification
车永飞, 赵英俊. 矿物光谱特征谱段识别方法与应用. 科技导报, 2017 , 35 (4) : 90 -93 . DOI: 10.3981/j.issn.1000-7857.2017.04.016
CHE Yongfei, ZHAO Yingjun. Study on hyperspectral mineral identification based on characteristic spectrum peak-valley correlation coefficient method[J]. Science & Technology Review, 2017 , 35 (4) : 90 -93 . DOI: 10.3981/j.issn.1000-7857.2017.04.016
2017年第35卷第4期
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doi: 10.3981/j.issn.1000-7857.2017.04.016
  • 接收时间:2016-08-24
  • 首发时间:2017-02-28
  • 出版时间:2017-02-28
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  • 收稿日期:2016-08-24
  • 修回日期:2016-11-20
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2种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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