针对多光谱图像数据维数高导致图像色彩再现过程中数据处理复杂度高的问题，提出一种多光谱图像非线性降维方法。首先根据人眼视觉系统特征，用CIE标准观察者色匹配函数对源光谱进行加权，对加权光谱采用主成分分析(PCA)方法降维来提高降维的色度精度及光照变换时的色差稳定性；然后针对因色匹配函数加权降维引起的光谱损失，采用PCA方法对损失的光谱进行降维，补偿因色度精度提升引起的光谱损失，有效提高降维的光谱精度。最后根据应用精度要求用前两步获得的主成分组合形成降维后数据。实验结果显示，提出方法的平均光谱精度为0.013 9，平均色度精度为0.705 8，色差稳定性为1.950 6，比现有的线性变换PCA法和LabPQR法分别提高了14%,15%;47%,68%和82%，表明新方法在光照变换色差稳定性、光谱精度及色度精度三方面均优于现有其他算法。

To solve the problem brought by high dimensionality of multi-spectral images during color reproduction, a nonlinear dimensionality reduction method for multi-spectral images was presented. Firstly, according to the characteristics of human visual system, the CIE standard observer color matching functions were weighted to the source spectral reflectance and a Principal Component Analysis (PCA) method was used to the weighted spectrum to effectively improve the colorimetric precision and color difference stability of dimensionality reduction. Then, for the spectral reflectance loss caused by weighting color matching functions, a PCA method was imposed on the lost spectrum to compensate the lost spectral accuracy caused by the improvement of colorimetric precision to effectively improve the spectral precision of dimensionality reduction. Finally the principal components obtained from the first two steps were combined to form the low-dimensional spectral data. Experiments show that the proposed method can offer the average spectral precision in 0.013 9, average colorimetric precision in 0.705 8, and the color difference stability in 1.950 6,which is increased by 14% and 15%,47% and 68%,as well 84% and 82% as comparied those of the PCA cand LabPQR methods. The method outperforms the existing methods in the colorimetric accuracy, spectral accuracy and color difference stability under different illuminants.