复制的多光谱数据获取要求图像数据具有设备无关、 场景无关特性, 能够真实客观表征物体颜色信息。 针对获取系统扰动、 噪声误差以及光谱重建中训练样本典型代表性与相关性要求, 提出了基于正交回归的光谱重建算法, 并通过子空间跟踪的训练样本选择算法, 选择重建样本与训练样本集中相关性与代表性最好的样本参与光谱重建。 实验通过改造后的仙娜宽带多通道成像系统进行验证, 数据表明本文提出的方法, 所选训练样本能较好的表征样本空间并具有较好的正交性, 在宽带多光谱成像方面, 重建光谱平均色度误差为3.6, 其光谱精度与色度精度较其他方法具有明显提高。

The multispectral image acquisition oriented to reproduction requests that the data is device independent and scenes independent, and can realize the characterization of the original color information. Aiming at disturbance, noise error of system, and the requirement for training samples’ typical representative and correlation, the authors proposed orthogonal regression spectral algorithm and training samples selection algorithm based on subspace tracking, through the mapping function between the spectral space and color space, by selecting the best samples in typical representative and correlation samples between target samples and selected samples. The modified Sinar 75H trichromatic digital camera combined with bandpass filter glasses were used for experiment, the data show that our method has higher spectral and chromaticity accuracy, the training samples selected by subspace tracking method are uniformly distributed in the sample space, and have good orthogonality. The statistics experimental results indicate that the performance of the proposed method is obviously better than that of previous method, in both color difference error and spectral reflectance error.