针对光谱反射率研究中因训练样本数据量大造成的冗杂等问题，提出了一种基于RGB信息进行聚类的样本分类方法。首先对颜色进行聚类并确定聚类个数，后对每一类光谱反射率使用BP神经网络分别进行重建。对于实验结果，使用ΔE₀₀、均方根误差（RMSE）以及适应度系数等标准进行评价，同时与主成分分析算法进行对比。从实验分析可得出，在聚类数目为7时光谱反射率重建效果最好，这时的平均CIE2000的色差为0.836，平均RMSE为0.0149，平均适应度系数为99.82%，并且，在最后对重建色差较大的色块进行了优化处理。实验证明，颜色聚类方法可以很好的应用于光谱反射率重建。

Aiming at the problem of redundancy caused by the large amount of training sample data in the study of spectral reflectance, a sample classification method based on RGB information is proposed in this paper. Firstly, the color is clustered and the number of clusters is determined. Then, the BP neural network is used to reconstruct each spectral reflectance. The experimental results are evaluated by color difference, root mean square error and fitness coefficient, and compared with principal component analysis algorithm. From the experimental analysis, it can be concluded that the spectral reflectance reconstruction effect is the best when the number of clusters is 7. The average CIE2000 chromatic aberration is 0.836. The average root mean square error is 0.0149, and the average fitness coefficient is 99.82%. Finally, the color blocks with large reconstruction chromatic aberration are optimized. Experiments show that color clustering method can be well applied to spectral reflectance reconstruction.