针对R矩阵光谱重构法面临的问题, 提出了一种基于相机响应特性的光谱分解方法, 对分解出的同色异谱黑的反演建立粒子群优化 BP 神经网络模型 (PSOBP) 以实现网络训练权重的优化, 并利用全局训练样本和局部训练样本的二次光谱重构方式进行了仿真实验。结果表明, 在D65光源下, 利用所提出的方法, RGB相机观测下重构两种测试集均方误差平均值分别至少降低了1.71%和0.51%, 色差最大值分别为3.5579和2.3776, 满足人眼辨别颜色阈值要求; WorldView3观测下光谱重构精度均方误差在410~510、555~565、590~685、705~740 nm波段内不超过2%, 适应度系数表示的可接受样本占比均为91.667%, 色差最大值分别为1.6002和1.1177, 其光谱重构精度以及色度精度较其他方法均有所提高, 且6通道多光谱相机已能满足较高精度光谱重构的要求。

Aiming to the problem faced by the R matrix spectral decomposition method, a spectral decomposition method based on the camera response characteristics is proposed, a particle swarm optimization BP neural network model (PSOBP) is established for the inversion of the decomposed metameric black to realize the optimization of network training weights, and simulation experiments are conducted using the global training samples and local training samples quadratic spectral reconstruction method. The results show that under the D65 light source, using the PSOBP combined reconstruction method, the mean square error of the two test sets reconstructed by the RGB camera is reduced by at least 1.71% and 0.51%, respectively, compared with the other traditional method, and the maximum color difference is 3.5579 and 2.3776, basically meeting the requirements of the human eye color discrimination threshold. While the mean square error of spectral reconstruction accuracy of WorldView3 is less than 2% in bands of 410-510, 555-565, 590-685 and 705-740 nm, the proportion of acceptable samples represented by the fitness coefficient is 91.667%, and the maximum color difference is 1.6002 and 1.1177, respectively. In addition, the spectral reconstruction accuracy and chromaticity accuracy of the proposed method have been improved compared with other methods, and the 6-channel multi-spectral camera can meet the requirements of high precision spectral reconstruction.