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REMOTE SENSING FOR LAND & RESOURCES    2017, Vol. 29 Issue (1) : 186-191     DOI: 10.6046/gtzyyg.2017.01.28
Technology Application |
Simulation of bi-directional reflectance on mixed minerals based on Hapke photometric model
WANG Zhe1, ZHAO Zhe2, YAN Bokun1, YANG Suming1
1. China Aero Geophysical Survey and Remote Sensing Center for Land and Resources, Beijing 100083, China;
2. Hebei Bureau of Coal Geological Exploration, Shijiazhuang 050085, China
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Abstract  

Hapke photometric model is a useful tool for studying the spectra of mixed minerals. However, there are still some improvable things, and domestic research still lags far behind that of foreign countries. This paper focuses on the characteristics of surface minerals through 4 groups of spectroscopic tests in laboratory, and then discusses and points out the accuracy of the Hapke photometric model when simulating the spectra of mixed minerals. The mean of root mean square errors (RMSE) of the 4 groups by using IMSA model is 0.014 4, and the mean of correlation coefficients (R) is 0.994 7. The mean of RMSE of the 4 groups by using AMSA model is 0.008 4, and the mean of R is 0.994 4. These data suggest that IMSA model and AMSA model have a very high precision and can be a good means to simulate spectral mixture of mixed minerals. Nevertheless, the experiment results show that, when the mixed minerals contain biotite, the accuracy is not satisfactory, but the accuracy of simulation can be improved by adjusting the weight of biotite. Spectral shape of mixed minerals needs a specific analysis of compositions of the mixed mineral, for instance, a particular mineral which possesses a higher mass fraction in the mixed minerals may not play the leading role in the spectral shape, while the mineral of low reflectivity may play a more important role.

Keywords object-based image analysis (OBIA)      J48 algorithm      decision tree      land cover classification     
:  TP79  
Issue Date: 23 January 2017
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SUN Yuyi
ZHAO Junli
WANG Miaomiao
LIU Yong
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SUN Yuyi,ZHAO Junli,WANG Miaomiao, et al. Simulation of bi-directional reflectance on mixed minerals based on Hapke photometric model[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(1): 186-191.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2017.01.28     OR     https://www.gtzyyg.com/EN/Y2017/V29/I1/186

[1] 代晶晶,李庆亭.基于Hapke和Shkuratov模型的斑岩铜矿蚀变带混合波谱研究[J].地质与勘探,2013,49(3):505-510. Dai J J,Li Q T.Study on mixed spectra of alteration zones in porphyry copper deposits based on the Hapke and Shkuratov models[J].Geology and Exploration,2013,49(3):505-510.
[2] Foody G M,Cox D P.Sub-pixel land cover composition estimation using a linear mixture model and fuzzy membership functions[J].International Journal of Remote Sensing,1994,15(3):619-631.
[3] Clark R N,Swayze G A,Livo K E,et al.Imaging spectroscopy:Earth and planetary remote sensing with the USGS Tetracorder and expert systems[J].Journal of Geophysical Research,2003,108(E12):5131.
[4] Hapke B.Bidirectional reflectance spectroscopy:1.Theory[J].Journal of Geophysical Research,1981,86(B4):3039-3054.
[5] Hapke B,Wells E.Bidirectional reflectance spectroscopy:2.Experiments and observations[J].Journal of Geophysical Research,1981,86(B4):3055-3060.
[6] Chandrasekhar S.Radiative Transfer[M].New York:Dover Publications,1960.
[7] 徐元柳.基于裸露地表辐射传输模型的粗糙度反演与地形校正[D].北京:中国地质大学(北京),2009. Xu Y L.Roughness Retrieval and Topographic Correction Based on Bare Surface Radiative Transfer Model[D].Beijing:China University of Geosciences(Beijing),2009.
[8] Shepard M K,Helfenstein P.A test of the Hapke photometric model[J].Journal of Geophysical Research,2007,112(E3):E03001.
[9] Ciarniello M,Capaccioni F,Filacchione G,et al.Hapke modeling of Rhea surface properties through Cassini-VIMS spectra[J].Icarus,2011,214(2):541-555.
[10] Li S,Li L.Radiative transfer modeling for quantifying lunar surface minerals,particle size,and submicroscopic metallic Fe[J].Journal of Geophysical Research,2011,116(E9):E09001.
[11] Mustard J F,Pieters C M.Photometric phase functions of common geologic minerals and applications to quantitative analysis of mineral mixture reflectance spectra[J].Journal of Geophysical Research,1989,94(B10):13619-13634.
[12] Cheek L C,Pieters C M.Reflectance spectroscopy of plagioclase-dominated mineral mixtures:Implications for characterizing lunar anorthosites remotely[J].American Mineralogist,2014,99(10):1871-1892.
[13] Papike J J,Simon S B,Laul J C.The lunar regolith:Chemistry,mineralogy,and petrology[J].Reviews of Geophysics,1982,20(4):761-826.
[14] Hapke B.Bidirectional reflectance spectroscopy:5.The coherent backscatter opposition effect and anisotropic scattering[J].Icarus,2002,157(2):523-534.
[15] Hapke B.Theory of Reflectance and Emittance Spectroscopy[M].New York:Cambridge University Press,2005.
[16] 陈明.基于分形理论的岩矿光谱模型研究[D].武汉:华中科技大学,2010. Chen M.Study on the Spectral Model of Rocks and Minerals Based on Fractal[D].Wuhan:Huazhong University of Science and Technology,2010.
[17] 闫柏琨,李建忠,甘甫平,等.一种月壤主要矿物组分含量反演的光谱解混方法[J].光谱学与光谱分析,2012,32(12):3335-3340. Yan B K,Li J Z,Gan F P,et al.A spectral unmixing method of estimating main minerals abundance of lunar soils[J].Spectroscopy and Spectral Analysis,2012,32(12):3335-3340.
[18] 程街亮,史舟,李洪义.不同类型土壤的二向反射光谱特性及模拟[J].光谱学与光谱分析,2008,28(5):1007-1011. Cheng J L,Shi Z,Li H Y.Observation and simulation of bi-directional spectral reflectance on different type of soils[J].Spectroscopy and Spectral Analysis,2008,28(5):1007-1011.
[19] 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(北京),2010. Tian F.Identification and Quantitative Retrival of Minerals Information Integrating VIS-NIR-MIR-TIR(0.35~25μm) Hyspectral Data[D].Beijing:China University of Geosciences(Beijing),2010.
[20] Lemelin M,Morisset C E,Germain M,et al.Ilmenite mapping of the lunar regolith over mare australe and mare ingenii regions:An optimized multisource approach based on Hapke radiative transfer theory[J].Journal of Geophysical Research:Planets,2013,118(2):2582-2593.

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