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

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...

Full description

Saved in:
Bibliographic Details
Published in:Journal of spectroscopy (Hindawi) 2021, Vol.2021, p.1-16
Main Authors: Zhao, Peng, Li, Zhen-Yu, Wang, Cheng-Kun
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Classification
Classifiers
Color
Computing time
Datasets
Decision analysis
Decision theory
Fuzzy logic
Fuzzy sets
Infrared analysis
Infrared spectra
Near infrared radiation
Principal components analysis
Recognition
Reflectance curves
Species classification
Spectral bands
Spectral classification
Spectral reflectance
Spectrum analysis
Support vector machines
Training
Universal Serial Bus
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2314-4920
2314-4939
DOI:10.1155/2021/6088435