Abstract
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.
Highlights
Wood species classification has been investigated for several years, as different wood species have diverse physical and chemical properties
There were 2,500 spectral curves for 50 wood species. ese samples were divided into a training set and a testing set using k-fold cross validation [29] with k 5, so that the training set consisted of 2000 samples and the testing set consisted of 500 samples
Comparisons with Conventional Algorithms. e fuzzy rule classifier was applied to the wood species spectral classification experiments and compared with other important classification schemes, such as the Bayes classifier (BC) [30], random forest (RF) [31], BP network (BPN) [32], support vector machine (SVM) [33], and LeNet-5 [34]. e overall recognition accuracy (ORA) and processing speed for a testing sample from our testing dataset are listed in Table 11. e computer configurations used are presented in Table 12. e ORA was calculated as follows: ORA nright. ntest
Summary
Wood species classification has been investigated for several years, as different wood species have diverse physical and chemical properties. Yusof et al proposed a fuzzy-logic-based preclassifier to classify tropical wood species into four broad categories, followed by performing specific wood species recognition using definite classifiers to increase recognition accuracy and decrease processing time requirements [7]. We propose a fuzzy rule classification scheme for the recognition of 50 wood species using 1D spectral reflectance data. For the wood spectral curves, the VIS band may vary as the color of a wood sample may change owing to environmental variations, while the NIR band remains relatively stable. These two bands were processed using different fuzzy classifiers, and the two classification results were further processed using an improved decision-level fusion based on the D-S evidential theory. Several comparisons with other classical and state-of-the-art classifiers in wood species recognition verified the merits of our scheme
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