|
|
|
|
|
|
Preliminary Study in Spectral Mixing Model of Mineral Pigments on Chinese Ancient Paintings-Take Azurite and Malachite for Example |
LI Da-peng1, ZHAO Heng-qian1,2*, ZHANG Li-fu2*, ZHAO Xue-sheng1 |
1. College of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China
2. State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China |
|
|
Abstract Hyperspectral remote sensing technology is completely non-invasive for cultural relics, and suitable for identification and analysis of pigments of Chinese ancient paintings and other cultural relics, but the quantitative analysis of the mixed pigments composition in the ancient paintings is still a difficulty. For the mixed pigments phenomenon which often appears in Chinese ancient paintings, taking an example of azurite and malachite, two typical mineral pigments, we choose the two kinds of mineral pigment powder with the same size, precisely compound these two kinds of pigments to obtain pigment samples, and then obtain their spectra understrict control of experimental conditions . For mixed spectra, we use fully constrained least square method for spectral unmixing with full bands and use derivative of ratio spectroscopy for spectral unmixing with single band, then evaluate the unmixing accuracy, compare and analyze the unmixing results, and finally discuss the spectral mixing model of these two kinds of mineral pigments. Experimental results show that the spectral mixtures of azurite and malachite display strong nonlinear mixing characteristics overall, but are in accordance with linear mixing model in some strong linear bands. Using derivative of ratio method for spectral unmixing at these bands, we can achieve much higher unmixing accuracy than spectral unmixing with full bands.
|
Received: 2017-09-01
Accepted: 2018-01-15
|
|
Corresponding Authors:
ZHAO Heng-qian, ZHANG Li-fu
E-mail: zhaohq@cumtb.edu.cn;zhanglf@radi.ac.cn
|
|
[1] ZHOU Ping-ping, HOU Miao-le, ZHAO Xue-sheng, et al(周平平,侯妙乐,赵学胜,等). Geomatics World(地理信息世界), 2017, 24(3): 113.
[2] WANG Hong-mei(王红梅). China Cultural Heritage Scientific Research(中国文物科学研究), 2011, 2: 85.
[3] XU Wen-juan(徐文娟). Sciences of Conservation and Archaeology(文物保护与考古科学), 2012, 24(S1): 41.
[4] ZHANG Liang-pei, ZHANG Li-fu(张良培,张立福). Hyperspectral Remote Sensing(高光谱遥感). Beijing: Surveying and Mapping Press(北京:测绘出版社), 2011.
[5] Yin Qinli, Lv Shuqiang. IEEE International Workshop on Earth Observation and Remote Sensing Applications, 2016: 174.
[6] CHEN Jin, MA Lei, CHEN Xue-hong, et al(陈 晋,马 磊,陈学泓,等). Journal of Remote Sensing(遥感学报), 2016, 20(5): 1102.
[7] Heylen R, Parente M, Gader P. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(6): 1844.
[8] Heylen R, Gader P. IEEE Geoscience and Remote Sensing Letters, 2014, 11(7): 1195.
[9] WANG Run-sheng, GAN Fu-ping, YAN Bo-kun, et al(王润生,甘甫平,阎柏琨,等). Remote Sensing for Land and Resources(国土资源遥感), 2010, (1): 1.
[10] ZHAO Chun-hui, WANG Li-guo, QI Bin(赵春晖,王立国,齐 滨). Hyperspectral Remote Sensing Images Processing: Methods and Applications(高光谱遥感图像处理方法及应用). Beijing: Publishing House of Electronics Industry(北京:电子工业出版社), 2016.
[11] LIU Juan-juan, WANG Mao-zhi, GE Shi-guo, et al(刘娟娟,王茂芝,葛世国,等). Journal of Sichuan University of Science and Engineering·Natural Science Edition(四川理工学院学报·自然科学版), 2013, 26(3): 76.
[12] ZHAO Heng-qian, ZHANG Li-fu, CEN Yi, et al(赵恒谦,张立福,岑 奕,等). The Journal of Infrared and Millimeter Waves(红外与毫米波学报), 2013, 32(6): 563.
[13] HUANG Wen-xiang(黄文祥). Art Journal(美术学报), 2016, (4): 14. |
[1] |
LIU Hao-dong1, 2, JIANG Xi-quan1, 2, NIU Hao1, 2, LIU Yu-bo1, LI Hui2, LIU Yuan2, Wei Zhang2, LI Lu-yan1, CHEN Ting1,ZHAO Yan-jie1*,NI Jia-sheng2*. Quantitative Analysis of Ethanol Based on Laser Raman Spectroscopy Normalization Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3820-3825. |
[2] |
LIN Hong-jian1, ZHAI Juan1*, LAI Wan-chang1, ZENG Chen-hao1, 2, ZHAO Zi-qi1, SHI Jie1, ZHOU Jin-ge1. Determination of Mn, Co, Ni in Ternary Cathode Materials With
Homologous Correction EDXRF Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3436-3444. |
[3] |
HUANG Li, MA Rui-jun*, CHEN Yu*, CAI Xiang, YAN Zhen-feng, TANG Hao, LI Yan-fen. Experimental Study on Rapid Detection of Various Organophosphorus Pesticides in Water by UV-Vis Spectroscopy and Parallel Factor Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3452-3460. |
[4] |
LI Zhong-bing1, 2, JIANG Chuan-dong2, LIANG Hai-bo3, DUAN Hong-ming2, PANG Wei2. Rough and Fine Selection Strategy Binary Gray Wolf Optimization
Algorithm for Infrared Spectral Feature Selection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3067-3074. |
[5] |
LIU Shu1, JIN Yue1, 2, SU Piao1, 2, MIN Hong1, AN Ya-rui2, WU Xiao-hong1*. Determination of Calcium, Magnesium, Aluminium and Silicon Content in Iron Ore Using Laser-Induced Breakdown Spectroscopy Assisted by Variable Importance-Back Propagation Artificial Neural Networks[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3132-3142. |
[6] |
KONG De-ming1, LIU Ya-ru1, DU Ya-xin2, CUI Yao-yao2. Oil Film Thickness Detection Based on IRF-IVSO Wavelength Optimization Combined With LIF Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2811-2817. |
[7] |
ZHAO Yu-wen1, ZHANG Ze-shuai1, ZHU Xiao-ying1, WANG Hai-xia1, 2*, LI Zheng1, 2, LU Hong-wei3, XI Meng3. Application Strategies of Surface-Enhanced Raman Spectroscopy in Simultaneous Detection of Multiple Pathogens[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2012-2018. |
[8] |
CHENG Xiao-xiang1, WU Na2, LIU Wei2*, WANG Ke-qing2, LI Chen-yuan1, CHEN Kun-long1, LI Yan-xiang1*. Research on Quantitative Model of Corrosion Products of Iron Artefacts Based on Raman Spectroscopic Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2166-2173. |
[9] |
CHEN Rui1, WANG Xue1, 2*, WANG Zi-wen1, QU Hao1, MA Tie-min1, CHEN Zheng-guang1, GAO Rui3. Wavelength Selection Method of Near-Infrared Spectrum Based on
Random Forest Feature Importance and Interval Partial
Least Square Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 1043-1050. |
[10] |
DENG Xiao-jun1, 2, MA Jin-ge1, YANG Qiao-ling3, SHI Yi-yin1, HUO Yi-hui1, GU Shu-qing1, GUO De-hua1, DING Tao4, YU Yong-ai5, ZHANG Feng6. Visualized Fast Identification Method of Imported Olive Oil Quality Grade Based on Raman-UV-Visible Fusion Spectroscopy Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 1117-1125. |
[11] |
WANG Hai-ping1, 2, ZHANG Peng-fei1, XU Zhuo-pin1, CHENG Wei-min1, 3, LI Xiao-hong1, 3, ZHAN Yue1, WU Yue-jin1, WANG Qi1*. Quantitative Determination of Na and Fe in Sorghum by LIBS Combined With VDPSO-CMW Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(03): 823-829. |
[12] |
XU Wei-xin, XIA Jing-jing, WEI Yun, CHEN Yue-yao, MAO Xin-ran, MIN Shun-geng*, XIONG Yan-mei*. Rapid Determination of Oxytetracycline Hydrochloride Illegally Added in Cattle Premix by ATR-FTIR[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(03): 842-847. |
[13] |
ZHENG Li-na1, 2, XUAN Peng1, HUANG Jing1, LI Jia-lin1. Development and Application of Spark-Induced Breakdown Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(03): 665-673. |
[14] |
GAO Xi-ya1, 2, 3, ZHANG Zhu-shan-ying1, 2, 3*, LU Cui-cui1, 2, 3, MENG Yong-ji1, 2, 3, CAO Hui-min1, 2, 3, ZHENG Dong-yun1, 2, 3, ZHANG Li1, 2, 3, XIE Qin-lan1, 2, 3. Quantitative Analysis of Hemoglobin Based on SiPLS-SPA
Wavelength Optimization[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(01): 50-56. |
[15] |
LI Yuan1, ZHANG Wen-bo1, CHEN Xiao-lin2, 3, LI Han1, ZHANG Guan-jun1. Application of Gaussian Process Regression on the Quantitative Analysis of the Aging Condition of Insulating Paper by Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(10): 3073-3078. |
|
|
|
|