| 光谱学与光谱分析 |
|
|
|
|
|
| A Method of Hyperspectral Quantificational Identification of Minerals Based on Infrared Spectral Artificial Immune Calculation |
| LIU Qing-jie1, 2, JING Lin-hai1, 2, LI Xin-wu1, 2, BI Jian-tao1, 2, WANG Meng-fei3, LIN Qi-zhong1, 2 |
1. Center for Earth Observation and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
2. Key Laboratory of Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
3. China Aero Geophysical Survey and Remote Sensing Center for Land and Resources, Beijing 100083, China |
|
|
|
|
Abstract Rapid identification of minerals based on near infrared (NIR) and shortwave infrared (SWIR) hyperspectra is vital to remote sensing mine exploration, remote sensing minerals mapping and field geological documentation of drill core, and have leaded to many identification methods including spectral angle mapping (SAM), spectral distance mapping(SDM), spectral feature fitting(SFF), linear spectral mixture model(LSMM), mathematical combination feature spectral linear inversion model(CFSLIM) etc. However, limitations of these methods affect their actual applications. The present paper firstly gives a unified minerals components spectral inversion (MCSI) model based on target sample spectrum and standard endmember spectral library evaluated by spectral similarity indexes. Then taking LSMM and SAM evaluation index for example, a specific formulation of unified MCSI model is presented in the form of a kind of combinatorial optimization. And then, an artificial immune colonial selection algorithm is used for solving minerals feature spectral linear inversion model optimization problem, which is named ICSFSLIM. Finally, an experiment was performed to use ICSFSLIM and CFSLIM to identify the contained minerals of 22 rock samples selected in Baogutu in Xinjiang China. The mean value of correctness and validness identification of ICSFSLIM are 34.22% and 54.08% respectively, which is better than that of CFSLIM 31.97% and 37.38%; the correctness and validness variance of ICSFSLIM are 0.11 and 0.13 smaller than that of CFSLIM, 0.15 and 0.25, indicating better identification stability.
|
|
Received: 2012-09-17
Accepted: 2012-11-20
|
|
|
|
Corresponding Authors:
LIU Qing-jie
E-mail: qjliu@ceode.ac.cn
|
|
[1] WANG De-zi, XIE Lei(王德滋, 谢 磊). Optical Mineralogy，3rd ed(光性矿物学，第3版). Beijing: Science Press(北京: 科学出版社), 2008.
[2] XIU Lian-cun, ZHENG Zhi-zhong, YU Zheng-kui，et al(修连存, 郑志忠, 愈正奎，等). Acta Geologica Sinica(地质学报), 2007, 81(11): 1584.
[3] LI Shuai, LIN Qi-zhong, LIU Qing-jie，et al(李 帅, 蔺启忠, 刘庆杰，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2010, 30(5): 1315.
[4] Horn R A，Johuson C R. Matrix Analysis(矩阵分析). Translated by YANG Qi(杨 奇，译). Beijing: China Machine Press(北京：机械工业出版社)，2005.
[5] Marcus B，William S H，Russel W. Hyperspectral Remote Sensing Principles and Applications. Taylor & Francis Group: CRC Press, 2007.
[6] MO Hong-wei(莫宏伟). Artificial Immune System(人工免疫系统). Beijing: Science Press(北京: 科学出版社), 2009.
[7] ZHENG Hong, HU Xue-min(郑 宏, 胡学敏). Journal of Remote Sensing(遥感学报), 2009, 13(5): 913.
[8] De Castro L N, Von Zuben F J. The Clonal Selection Algorithm with Engineering Application. Proceeding of the Twelfth European Symposium on Artificial Neural Networks (ESANN2004), 2004. 519. |
| [1] |
YE Wen-chao1, LUO Shui-yang1, LI Jin-hao1, LI Zhao-rong1, FAN Zhi-wen1, XU Hai-tao1, ZHAO Jing1, LAN Yu-bin1, 2, DENG Hai-dong1*, LONG Yong-bing1, 2, 3*. Research on Classification Method of Hybrid Rice Seeds Based on the Fusion of Near-Infrared Spectra and Images[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2935-2941. |
| [2] |
ZHANG Fu-jie, SHI Lei, LI Li-xia*, ZHAO Hao-ran, ZHU Yin-long. Study on Nondestructive Identification of Panax Notoginseng Powder Quality Grade Based on Hyperspectral Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(07): 2255-2261. |
| [3] |
LI Peng-cheng1, 2, LIU Han1, 2, ZHAO Long-lian1, 2, LI Jun-hui1, 2*. Key Parameters for Maize Leaf Moisture Measurement Using NIR Camera With Filters Based on Hyperspectral Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(10): 3184-3188. |
| [4] |
YANG Tian-hang 1, 2, 3, GU Ming-jian1, 2*, HU Xiu-qing4, WU Chun-qiang4, QI Cheng-li4，SHAO Chun-yuan1, 2. Tropospheric 3D Winds Measurement Based on Cross-Platform Infrared Hyperspectral Observation[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(04): 1131-1137. |
| [5] |
FAN Nai-yun, LIU Gui-shan*, ZHANG Jing-jing, ZHANG Chong, YUAN Rui-rui, BAN Jing-jing. Hyperspectral Model Optimization for Protein of Tan Mutton Based on Box-Behnken[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(03): 918-923. |
| [6] |
WANG Cheng1, YU Hang1, YAO Wei-rong1, CHENG Yu-liang1, GUO Ya-hui1, QIAN He1, TAN Zhi-qiang2, XIE Yun-fei1*. A Study on the Screening of Anti-Inflammatory Drug Diclofenac Sodium in Dietary Supplements by Near Infrared Hyperspectral Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(02): 592-598. |
| [7] |
LÜ Meng-qi1, SONG Yu-jie4, WENG Hai-yong1, 3, SUN Da-wei1, 3, DONG Xiao-ya2, FANG Hui1, 3, CEN Hai-yan1, 3*. Effect of Near Infrared Hyperspectral Imaging Scanning Speed on Prediction of Water Content in Arabidopsis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(11): 3508-3514. |
| [8] |
SHAO Yuan-yuan1, 2, WANG Yong-xian1, XUAN Guan-tao1, 3*, GAO Zong-mei4, LIU Yi1, HAN Xiang1, HU Zhi-chao2*. Hyperspectral Imaging Technique for Estimating the Shelf-Life of Kiwifruits[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(06): 1940-1946. |
| [9] |
ZHANG Jing-jing1, LIU Gui-shan1*, REN Ying-chun1, SU Wen-hao1, KANG Ning-bo2, MA Chao3. Rapid Detection of Freshness in Tan-Lamb Mutton Based on Hyperspectral Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(06): 1909-1914. |
| [10] |
YANG Yu-qing1, 2, ZHANG Tian-tian1,2, LI Jun-hui1, 2*, LU Meng-yao1, 2, LIU Hui1, 2, ZHAO Long-lian1, 2, ZHANG Ye-hui1, 2. Water Imaging of Living Corn Leaves Based on Near-Infrared Hysperspectral Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(12): 3743-3747. |
| [11] |
LI Da-peng1, ZHAO Heng-qian1,2*, ZHANG Li-fu2*, ZHAO Xue-sheng1. Preliminary Study in Spectral Mixing Model of Mineral Pigments on Chinese Ancient Paintings-Take Azurite and Malachite for Example[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(08): 2612-2616. |
| [12] |
YE Hui, LI Xiao-li, YU Ke-qiang, XIA Yi-hua, ZHANG Chu, HE Yong*. Application of Hyperspectral Technology for the Determination of the Solid Concentration of the Anaerobic Digestion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2017, 37(12): 3833-3838. |
| [13] |
FAN Yang-yang, QIU Zheng-jun*, CHEN Jian, WU Xiang, HE Yong . Identification of Varieties of Dried Red Jujubes with Near-Infrared Hyperspectral Imaging [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2017, 37(03): 836-840. |
| [14] |
LIU Lei*, SUN Xue-jin, GAO Tai-chang . Research on Cloud Phase Detemination Using Infrared Emissivity Spectrum Data (1): Cloud Phase Determination [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2016, 36(12): 3885-3894. |
| [15] |
LIU Lei*, SUN Xue-jin, GAO Tai-chang . Research on Cloud Phase Detemination Using Infrared Emissivity Spectrum Data (2): Retrieval of Cloud Effective Radius and Water Path[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2016, 36(12): 3895-3906. |
|
|
|
|
