光谱重建训练样本选择方法研究

Selection Method of Training Samples for Spectral Reconstruction

目的研究光谱重建训练样本选择方法及训练样本集样本数量对光谱重建精度的影响。方法以光谱反射率已知的1687个矿物颜料样本为研究对象,搭建真实六通道宽带光谱成像系统并进行标定,利用宽带光谱成像系统对样本进行六通道数字响应值采集并进行校正,通过R-matrix光谱重建方法,对现有以及文中引入和提出的训练样本选择方法的光谱重建精度和计算效率进行比较。结果实验结果表明,分区最大化色域边界描述方法可以较好地应用于光谱成像过程中训练样本选择,其在保证光谱重建精度的同时大大提高了训练样本选择的效率,实验证明文中提出的基于色域最大化思想的训练样本方法,在面向复制的光谱成像流程中也具有一定的实用性。结论研究结果对于光谱成像过程中训练样本选择方法的选择具有一定指导作用。

This experiment aimed to study the influence of the training sample selection method and the training sample number on the accuracy of spectral reconstruction. A real six-channel spectral imaging workflow was set up and calibrated where the R-matrix method was employed for spectral reconstruction. The database that consisted of 1687 mineral pigment samples whose spectral reflectance was known was used for spectral reconstruction. The spectral reconstruction accuracy and the computational efficiency of the training sample selection method introduced and proposed in this study were compared with the current values based on the established six-channel spectral imaging systems and the prepared sample set. The experimental results showed that the segment description method of maximum gamut boundary could be well applied to selection of training samples in spectral imaging workflow, which can greatly improve the efficiency of the training sample selection and keep the spectral reconstruction accuracy at the same time. The experimental results also illustrated that the proposed selection method of training samples based on gamut maximization was practical for reproduction-oriented spectral imaging workflow. The results had a certain referencing value for the selection of training samples in spectral imaging process.