| 光谱学与光谱分析 |
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| Study on Recognition Model of Phyllosilicate of Martian Surface |
| ZHANG Xia1, WU Xing1, 2*, YANG Hang1, CHEN Sheng-bo3, LIN Hong-lei1, 2 |
| 1. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China2. University of Chinese Academy of Sciences, Beijing 100049, China3. Jilin University, Changchun 130012, China |
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Abstract Phyllosilicate belongs to hydrated silica, which is a principal form of hydrous minerals on the martian surface. It’s also an indicator in comparing different sediments and degree of aqueous alteration. Therefore, it’s essential to establish its recognition model for studying the geologic evolution of the Mars. Short-wave infrared (SWIR) spectral bands and thermal infrared (TIR) spectral bands have distinct spectral response to the mineral groups and ions, so they have distinctive advantages in detecting minerals. However the method of combining SWIR and TIR to recognize phyllosilicate is rarely studied. Based on the USGS spectral library, facing Compact Reconnaissance Imaging Spectrometer for Mars(CRISM) and Thermal Emission Imaging System(THEMIS),we conducted the research on the mechanism of the spectral response of phyllosilicate, and established the SWIR and TIR identification model respectively, then combined the SWIR and TIR spectral features to build the combined recognition model of phyllosilicate with Fisher discriminant analysis. The results of cross validation show that the identification accuracy of combined model is the highest, which can correctly classify 90.6% of the mineral samples and improve the identification precision of phyllosilicate effectively.
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Received: 2015-08-27
Accepted: 2015-12-18
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Corresponding Authors:
WU Xing
E-mail: wuxing15@mails.ucas.ac.cn
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[1] Poulet F, Mangold N, Loizeau D, et al. Astronomy & Astrophysics, 2008，487：L41.
[2] Smith M R, Bandfield J L, Cloutis E A, et al. Icarus, 2013，223：633.
[3] Carter J, Poulet F, Bibring J P, et al. Journal of Geophysical Research: Planets, 2013，118：831.
[4] Bishop J L, Loizeau D, McKeown N K, et al. Planetary and Space Science, 2013，86：130.
[5] Gurunadham R，Kumar S. Remote Sensing and Spatial Information Sciences, 2014，40：431.
[6] Bandfield J L. Geophysical Research Letters, 2008，35(12): 82.
[7] McDowell M L，Hamilton V E. Journal of Geophysical Research: Planets, 2009，114(E6): 258.
[8] Murchie S, Arvidson R, Bedini P, et al. Journal of Geophysical Research: Planets, 2007，112(E5): 431.
[9] Viviano Beck C E, Seelos F P, Murchie S L, et al. Journal of Geophysical Research: Planets, 2014，119：1403.
[10] YUAN Yue, PAN Jun, XING Li-xin，et al(袁 悦，潘 军，邢立新，等). Science Technology and Engineering(科学技术与工程), 2015. 109.
[11] HE Xiao-qun(何晓群). Multivariate Statistical Analysis(多元统计分析). 4 Ed.(第四版). Beijing: China Renmin University Press(北京：人民大学出版社), 2015.
[12] ZHOU Zhi-hua(周志华). Machine Learning(机器学习). Beijing: Tsinghua University Press(北京：清华大学出版社), 2016. |
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