In this study, a method based on fuzzy gray level aura matrix (FGLAM) textural feature and spectral feature fusion is proposed to improve the accuracy of wood species classification. The experimental dataset is acquired by two sensors. The visible images of wood samples are acquired using a RGB image acquisition platform, and the spectral curves are acquired using a USB2000-VIS-NIR spectrometer having a spectral band of 350–1100 nm. Firstly, the textural features are computed by using FGLAM for gray level wood sample images. The original spectral curve is smoothed and corrected to remove noise. Secondly, the obtained textural features and spectral features are processed in extreme learning machine to complete the decision-level fusion and obtain the classification results. The experimental results for 15 wood species show that the classification accuracy of the textural feature by using FGLAM alone can reach to 90%, which is better than that acquired using the traditional gray level co-occurrence matrix, local binary pattern, and the mainstream improved basic gray level aura matrix. Moreover, the classification accuracy based on the FGLAM textural feature and spectral feature fusion proposed in this paper can reach to 100%. Finally, for 8 similar wood species, the recognition accuracy of the present fusion scheme reaches to 98.75%, which is also better than those state-of-the-art methods.

本文提出了一种基于模糊灰度光环矩阵（FGLAM）纹理特征和光谱特征融合的方法，以提高木材分类的准确性。实验数据集由两个传感器获取。使用RGB图像采集平台采集木材样品的可见图像，并使用具有350-1100 nm光谱带的USB2000-VIS-NIR光谱仪采集光谱曲线。首先，通过使用FGLAM计算灰度木材样本图像的纹理特征。原始光谱曲线经过平滑和校正以消除噪声。其次，将获得的纹理特征和光谱特征在极限学习机中进行处理，以完成决策级融合并获得分类结果。15种木材的实验结果表明，单独使用FGLAM可以对纹理特征进行分类，准确率可达到90％，优于传统灰度共现矩阵，局部二值模式和主流改进的纹理特征分类精度。基本的灰度光环矩阵。此外，本文提出的基于FGLAM纹理特征和光谱特征融合的分类精度可以达到100％。最后，对于8种相似的木材物种，本融合方案的识别精度达到98.75％，也优于那些现有技术。以及主流改进的基本灰度光环矩阵。此外，本文提出的基于FGLAM纹理特征和光谱特征融合的分类精度可以达到100％。最后，对于8种相似的木材物种，本融合方案的识别精度达到98.75％，也优于那些现有技术。以及主流改进的基本灰度光环矩阵。此外，本文提出的基于FGLAM纹理特征和光谱特征融合的分类精度可以达到100％。最后，对于8种相似的木材物种，本融合方案的识别精度达到98.75％，也优于那些现有技术。