结合EEMD和比值导数的岩石-植物混合波谱分解

由于混合像元的影响，野外实测波谱或从遥感影像提取的像元波谱多为混合波谱。针对高光谱遥感应用中混合像元导致的混合波谱问题，提出了一种改进的比值导数混合波谱分解方法。首先，对野外实测的岩石与植被的混合波谱预处理，消除水汽噪声；其次，使用总体平均经验模态分解法（Ensemble Empirical Mode Decomposition，EEMD）进行IMF分解，获取r分量波谱；然后，利用比值导数方法对r分量波谱进行解混；最后，选取岩石面积比为自变量，近红外波谱的特征波段反射率值为因变量，利用回归分析定量反演野外混合波谱中岩石面积比。结果表明：①基于EEMD分解获取r分量波谱消除了环境干扰，反映了混合波谱总体趋势，体现了混合波谱中的主要地物波谱特征；②对EEMD分解获取的r分量波谱进行比值导数处理，抑制植被端元组分的同时，突出岩石组分对于混合光谱的影响；③结合EEMD和比值导数法处理后的特征波谱，提高了岩石—植物混合波谱反演精度。

Unmixing of Mixed Spectrum of Rocks and Vegetation Using EEMD and Derivative of Ratio Spectroscopy

The spectra field measured or from remote sensing images are mostly mixed spectra because of the influence of mixed pixel. An improved derivative of ratio spectroscopy was proposed in order to solve it in hyperspectral remote sensing applications. Firstly, the measured mixed spectra of rocks and vegetation were preprocessed, reduce moisture noise. Secondly, it was decomposed by EEMD (Ensemble Empirical Mode Decomposition) to get r component spectra. Thirdly, r component spectra were unmixed by derivative of ratio spectroscopy. Finally, the area ratio of rocks of mixed spectra can be calculated through regression analysis using the area ratio of rocks as independent variable and characteristic band reflectivity of near infrared spectroscopy as dependent variable.