Classification of wood species using computer-related technology is not only significant in the modernization of wood industrialization, but also makes it easier for nonwood professionals to identify different wood types accurately and avoid being deceived. Presently, most wood classification methods use a single feature to describe the wood, which is restrictive and provides incomplete information. In this study, macroscopic transverse section images and spectral curves of wood were used as the research objects, and 50 species of wood were classified. A wood species identification method based on the fusion of spectral features and texture features is proposed, which has the advantages of convenient data collection, fast identification speed, high identification accuracy, and anti-noise interference. First, a digital camera and a spectrometer were used to acquire the image and spectral curve from the wood transverse sections. The acquired spectral and texture features of the wood transverse sections were extracted with the fractal method and the local binary pattern theory method, respectively, and both extracted features were fused using the canonical correlation analysis feature fusion method. The fused features were then classified using a support vector machine classifier. The experimental results demonstrated that the classification accuracy of the texture and spectral features alone was 91.96% and 92.67%, respectively, whereas that of the fused features was 99.16% in the “leave-one-out” cross-validation. The wood classification method outlined in this paper has higher classification accuracy than existing mainstream methods. In addition, even after adding noise to the image and spectrum, it was observed that the classification accuracy did not decrease significantly, which indicates that the method described in this paper achieves excellent classification even in the presence of noise interference.

利用计算机相关技术对木种进行分类，不仅对木材工业化的现代化具有重要意义，而且使非木材专业人士更容易准确识别不同的木材类型，避免上当受骗。目前，大多数木材分类方法使用单一特征来描述木材，这是限制性的并且提供的信息不完整。本研究以木材宏观横断面图像和光谱曲线为研究对象，对50种木材进行分类。提出了一种基于光谱特征和纹理特征融合的木种识别方法，具有数据采集方便、识别速度快、识别准确率高、抗噪声干扰等优点。第一的，使用数码相机和光谱仪从木材横截面获取图像和光谱曲线。分别采用分形方法和局部二元模式理论方法提取获得的木材横断面的光谱和纹理特征，并采用典型相关分析特征融合方法对两种提取的特征进行融合。然后使用支持向量机分类器对融合的特征进行分类。实验结果表明，单独纹理和光谱特征的分类准确率分别为 91.96% 和 92.67%，而在“留一法”交叉验证中，融合特征的分类准确率为 99.16%。本文概述的木材分类方法比现有的主流方法具有更高的分类精度。此外，