Wood Species Recognition Based on Visible and Near-Infrared Spectral Analysis Using Fuzzy Reasoning and Decision-Level Fusion

Author:

Zhao Peng12ORCID,Li Zhen-Yu2ORCID,Wang Cheng-Kun2ORCID

Affiliation:

1. School of Computer Science, Software Engineering and Communication Engineering, Guangxi University of Science and Technology, Liuzhou 545006, China

2. School of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, China

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.

Funder

National Natural Science Foundation of China

Publisher

Hindawi Limited

Subject

Spectroscopy,Atomic and Molecular Physics, and Optics,Analytical Chemistry

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