基于颜色属性的光谱重建训练样本正交优化

选择具有代表性的颜色作为光谱重建的训练样本可以有效减少样本冗余,提高光谱重建精度。采用正交试验方法,基于色相、明度和饱和度在Munsell颜色集中选择具有代表性的颜色样本,并分析颜色三属性对光谱重建精度的影响。结果表明,采用主成分分析(PCA)法重建得到的反射率与原反射率的平均均方差(RMS)最大可达0.120 4,而采用违逆(PSE)法和R矩阵(R-matrix)法重建得到的平均RMS相对较小。三属性的优先级别R极差分析中,明度明显大于色相和饱和度。颜色三属性对P C A法的影响大于对PSE法和R-matrix法。明度对光谱重建精度的影响较大,而色相和饱和度对光谱重建精度的影响相对较小。

Orthogonal Optimization of Spectral Reconstruction Training Samples Based on Color Properties

It could effectively reduce sample redundancy and improve reconstruction accuracy by selecting representative colors as spectral reconstruction training samples. Representative colors were selected as training samples in the Munsell color set by orthogonal experimental method based on color properties, including hue, value and saturation, and the effects of color properties on reconstruction accuracy were analyzed. The results showed that the mean of reflectance RMS could be up to 0.120 4 by PCA method, while it was relatively smaller by PSE method and R-matrix method. Value priority level was greater than the hue and saturation by range analysis. In conclusion, the effects of color properties on PCA method were greater than PSE method and R-matrix method, and value exerted more effects on reconstruction accuracy than hue and saturation.