基于无信息变量和偏最小二乘投影分析的高光谱散射图像最优波段选择

提出了一种无信息变量消除和偏最小二乘投影分析相结合的苹果高光谱散射图像最优波段选择方法.经该算法提取后的波段降为全谱的26%,将选择后的波段作为输入变量建立了苹果硬度的偏最小二乘预测模型.预测均方根误差由6.00N降为5.73N,相关系数也有所提高,并与遗传算法作了比较.结果表明,该算法能有效消除原光谱矩阵中冗余的信息,且不存在遗传算法中的参量选择随机性等缺点.该算法为高光谱散射图像最优波段选择提供了一个理想的方法.

Optimal Wavelength Selection of Hyperspectral Scattering Images Based oUVE-PLS Projection Analysis

Partial least squares projection analysis combined with uninformative variable elimination was used to select optimal wavelengths from apple hyperspectral scattering images.After this algorithm,the number of effectivewavelengths decreased to 26%.The selected effective wavelengths were set as inputs of partial least squares model.Root mean square error of prediction dropped from 6.00N to 5.73N and correlation coefficient increased a little.The result shows that to select effective wavelengths using the algorithm is feasible.In the parameter selection,there is not such as random defects.It is expected that the algorithm would provide an effective method foroptimal wavelengths selection using hyperspectral scattering image technique.