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REMOTE SENSING FOR LAND & RESOURCES    2017, Vol. 29 Issue (1) : 158-163     DOI: 10.6046/gtzyyg.2017.01.24
Technology Application |
Inversion of geochemical compositions of basalts based on field measured spectra
YU Junchuan1, LIU Wenliang2, YAN Bokun1, DONG Xinfeng1, WANG Zhe1, LI Na1
1. China Aero Geophysical Survey and Remote Sensing Center for Land and Resources, Beijing 100083, China;
2. State Key Laboratory of Geological Processes and Mineral Resources, School of Earth Sciences and Mineral Resources, China University of Geosciences(Beijing), Beijing 100083, China
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Geochemical compositions have significant implications for rock classification,identification of the petrogenesis and evolution of the rocks. The utilization of remote sensing method to estimate the geochemical compositions of the rocks is a new subject, and is also a difficult point in remote sensing related researches due to its relatively immature applications. In this study, he Permian basalts were chosen as the study object. Based on systematical sampling, spectral analysis and geochemical test, the authors constructed a mathematical model between field measured spectra data (2 150 bands) and available data of six representative major elements by using partial least squares regression (PLSR). It is essential to initially choose proper preprocessing method to optimize the spectra data, and then search for the optimal number of principal components with minimum root-mean-square error through k-fold cross-validation. The results show that the PLSR model yields higher stability and precision,and plays a significant role in applications of geochemical composition inversion using remote sensing data.

Keywords LiDAR      building contour      active contour model      graph cuts      images     
:  TP79  
Issue Date: 23 January 2017
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WANG Chunlin
SUN Jinyan
ZHOU Shaoguang
QIAN Haiming
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WANG Chunlin,SUN Jinyan,ZHOU Shaoguang, et al. Inversion of geochemical compositions of basalts based on field measured spectra[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(1): 158-163.
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