1. The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Applications, Chinese Academy of Sciences, Beijing 100101, China 2. University of Chinese Academy of Sciences, Beijing 100049, China
Abstract：The precise analysis of mineral abundance is a key difficulty in hyperspectral remote sensing research. In the present paper, based on linear spectral mixture model, the derivative of ratio spectroscopy (DRS) was introduced for spectral unmixing of visible to short-wave infrared (Vis-SWIR; 0.4～2.5 μm) reflectance data. The mixtures of different proportions of plaster and allochite were analyzed to estimate the accuracy of the spectral unmixing model based on DRS. For the best 5 strong linear bands, the Pearson correlation coefficient (PCC) of the abundances and the actual abundances were higher than 99.9%, while the root mean square error (RMSE) is less than 2.2%. The result shows that the new spectral unmixing model based on DRS is simple, of rigorous mathematical proof, and highly precise. It has a great potential in high-precision quantitative analysis of spectral mixture with fixed endmembers.
Key words：Hyperspectral;Spectral unmixing;Derivative of ratio spectroscopy;Linear mixture model
赵恒谦1,2，张立福1*，吴太夏1，黄长平1,2 . 比值导数法矿物组分光谱解混模型研究[J]. 光谱学与光谱分析, 2013, 33(01): 172-176.
ZHAO Heng-qian1, 2, ZHANG Li-fu1*, WU Tai-xia1, HUANG Chang-ping1, 2 . Research on the Model of Spectral Unmixing for Minerals Based on Derivative of Ratio Spectroscopy . SPECTROSCOPY AND SPECTRAL ANALYSIS, 2013, 33(01): 172-176.
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