目的 以光谱图像作为检测样本讨论不同训练样本数量、分布对光谱图像重构的影响。 方法选择ColorChecker SG(140色)和 ColorChecker Color Rendition Chart(24色)以及Munsell（1269 色）等 3 种色卡作为训练样本， 对其光谱反射率进行主成分分析， 利用提取的主成分对光谱图像进行重构。 结果采用 ColorChecker Color Rendition Chart(24 色)色卡的 7 个主成分重构光谱图像对图像的再现精度最高， 其色差比其他 2 种色卡小， 且最大色差小于 3。 结论 在同一重构条件下， 光谱图像的重构精度并不随着训练样本数量增多以及分布范围增大而提高，3 种训练样本对红紫色的重构精度相对较低。

Objective The spectral image was chosen as a test sample, to discuss the effects of different number and distribution of training samples on the reconstruction of spectral image. Methods The spectral reflectance of ColorChecker SG, Color Rendition Chart and Munsell color cards were chosen as training samples and were analyzed by principal component analysis (PCA). And spectral image reconstruction was carried out using the principal component extracted by the analysis to color cards. Results Experimental results showed that the spectral image reconstructed using the seven principal components of the color card of ColorChecker Color Rendition Chart (24) had the highest precision, and the color difference was smaller than those of the other two color cards, with a largest color difference of less than 3. Conclusion The reconstruction precision of the spectral image did not increase with the increasing training sample number and distribution range under the same reconstruction condition. The reconstruction precision of red and purple was relatively low for the three kinds of training samples.