针对传统光谱匹配法在进行古代壁画颜料识别时存在的获取每个点反射率的过程复杂、计算具有一定误差等会影响识别精度的问题,将壁画颜料识别问题转换成多光谱图像分类问题,利用在图像分类领域有较强优势的卷积神经网络算法对多光谱图像进行处理,设计了一种新的卷积神经网络模型,并提出了光谱特征重组的数据预处理方式,通过加入两次dropout防止训练过程出现过拟合问题,进而实现了对古壁画颜料的分类。实验结果表明,该方法与统计流形支持向量机分类方法,以及未加入dropout的卷积神经网络分类方法相比,在分类效果和分类精度上具有明显的优势。

The traditional spectral matching method is based on the spectral reflectivity. However, the process of obtaining the reflectivity of each point is complicated, and the calculation has some errors, which will affect the recognition accuracy. In order to solve this issue, the problem of mural pigment recognition has been transformed into multi-spectral image classification, and a convolutional neural network algorithm with strong advantages is used in image classification to process multi-spectral images. Meanwhile, a new convolution neural network model is designed, and a data preprocessing method of spectral feature reorganization is proposed. By adding two dropouts, the problem of over-fitting in the training process is prevented, and the classification of ancient mural pigments is realized. The experimental results show that compared with the statistical manifold support vector machine classification method and the convolutional neural network classification method without dropout, the proposed method has obvious advantages in classification effect and classification accuracy.