A novel wood species spectral classification scheme is proposed based on a fuzzy rule classifier. The visible/near-infrared (VIS/NIR) spectral reflectance curve of a wood sample’s cross section was captured using a USB 2000-VIS-NIR spectrometer and a FLAME-NIR spectrometer. First, the wood spectral curve—with spectral bands of 376.64–779.84 nm and 950–1650 nm—was processed using the principal component analysis (PCA) dimension reduction algorithm. The wood spectral data were divided into two datasets, namely, training and testing sets. The training set was used to generate the membership functions and the initial fuzzy rule set, with the fuzzy rule being adjusted to supplement and refine the classification rules to form a perfect fuzzy rule set. Second, a fuzzy classifier was applied to the VIS and NIR bands. An improved decision-level fusion scheme based on the Dempster–Shafer (D-S) evidential theory was proposed to further improve the accuracy of wood species recognition. The test results using the testing set indicated that the overall recognition accuracy (ORA) of our scheme reached 94.76% for 50 wood species, which is superior to that of conventional classification algorithms and recent state-of-the-art wood species classification schemes. This method can rapidly achieve good recognition results, especially using small datasets, owing to its low computational time and space complexity.

中文翻译：

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基于可见光和近红外光谱分析的木材物种识别，使用模糊推理和决策级融合

提出了一种基于模糊规则分类器的新的木材树种光谱分类方案。使用 USB 2000-VIS-NIR 光谱仪和 FLAME-NIR 光谱仪捕获木材样品横截面的可见/近红外 (VIS/NIR) 光谱反射曲线。首先，使用主成分分析 (PCA) 降维算法处理木材光谱曲线 - 光谱带为 376.64-779.84 nm 和 950-1650 nm。木材光谱数据分为两个数据集，即训练集和测试集。利用训练集生成隶属函数和初始模糊规则集，通过调整模糊规则对分类规则进行补充和细化，形成完善的模糊规则集。其次，将模糊分类器应用于 VIS 和 NIR 波段。提出了一种基于 Dempster-Shafer (DS) 证据理论的改进决策级融合方案，以进一步提高木材种类识别的准确性。使用测试集的测试结果表明，我们方案的整体识别准确率（ORA）对 50 个木种达到了 94.76%，优于传统分类算法和最近最先进的木种分类方案。由于其计算时间和空间复杂度低，该方法可以快速获得良好的识别结果，尤其是在使用小数据集时。50 种木种的 76%，优于传统分类算法和最近最先进的木种分类方案。由于其计算时间和空间复杂度低，该方法可以快速获得良好的识别结果，尤其是在使用小数据集时。50 种木种的 76%，优于传统分类算法和最近最先进的木种分类方案。由于其计算时间和空间复杂度低，该方法可以快速获得良好的识别结果，尤其是在使用小数据集时。