TY - JOUR
T1 - Multiparameter Optimization for Mineral Mapping Using Hyperspectral Imagery
AU - Li, Na
AU - Huang, Xinchen
AU - Zhao, Huijie
AU - Qiu, Xianfei
AU - Geng, Ruonan
AU - Jia, Xiuping
AU - Wang, Daming
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/4
Y1 - 2018/4
N2 - A multi parameter optimization model is proposed for the mineral mapping of hyperspectral imagery. This model can provide guidance for selecting system parameters when a new sensor is designed or offer performance estimation in mineral mapping with a given imaging system. A multivariate regression analysis is performed to investigate the quantitative relationship between mineral identification capability and imaging spectrometer parameters. The objective function of the proposed optimization model is set to maximize performance with the constraint on system parameters due to user requirements and technology maturity. The number of minerals identified is selected to measure the data usefulness. The parameters of an imaging system include ground sample distance, signal-to-noise ratio, modulation transfer function, and spectral resolution. A set of evaluation experiments is conducted using hyperspectral data collected in Dongtianshan area, Xinjiang, China. The predicted error is less than 15%, and the best mineral mapping can be obtained using the optimized system parameters.
AB - A multi parameter optimization model is proposed for the mineral mapping of hyperspectral imagery. This model can provide guidance for selecting system parameters when a new sensor is designed or offer performance estimation in mineral mapping with a given imaging system. A multivariate regression analysis is performed to investigate the quantitative relationship between mineral identification capability and imaging spectrometer parameters. The objective function of the proposed optimization model is set to maximize performance with the constraint on system parameters due to user requirements and technology maturity. The number of minerals identified is selected to measure the data usefulness. The parameters of an imaging system include ground sample distance, signal-to-noise ratio, modulation transfer function, and spectral resolution. A set of evaluation experiments is conducted using hyperspectral data collected in Dongtianshan area, Xinjiang, China. The predicted error is less than 15%, and the best mineral mapping can be obtained using the optimized system parameters.
KW - Hyperspectral imaging system
KW - mineral mapping
KW - multivariate regression analysis
KW - optimization modeling
UR - https://www.scopus.com/pages/publications/85045571901
U2 - 10.1109/JSTARS.2018.2814617
DO - 10.1109/JSTARS.2018.2814617
M3 - 文章
AN - SCOPUS:85045571901
SN - 1939-1404
VL - 11
SP - 1348
EP - 1357
JO - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
JF - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
IS - 4
ER -