Spectral reflectance reconstruction based on multi-kernel support vector regression

In the process of spectral reflectance reconstruction by traditional methods, it doesn't take into account of the characteristics of highly dimensional and redundant multi-spectral training data, which results in low reconstruction accuracy, poor learning ability and generalization ability of the reconstruction model. In order to solve such problems, the spectral reflectance reconstruction of multi-kernel support vector regression was proposed. Firstly, the product of Cauchy kernel function and polynomial kernel function was introduced as multi-kernel function combining with the characteristics of overall situation and part of kernel function, and then the parameters to improve the performance of the model were obtained from the training samples by cut-and-trial. Finally, the multi-kernel support vector regression model was used to reconstruct the reflectance of the test samples, and evaluation was made through spectral error and fitness. The experimental results show that compared with the pseudo-inverse and single-kernel support vector regression, the spectral error of the reconstruction method is reduced by 0.4 - 0.785, the decision-making coefficient is increased by 2.84 - 5.27%, and the average fitness coefficient is increased by 2% - 3%. In color reproduction, the reconstruction accuracy is high and the chromatic aberration is small, so that it can meet the tolerance range of human vision.