Wood-species identification based on terahertz spectral data augmentation and pseudo-label guided deep clustering

In order to address the problem that existing methods combining spectral data and machine learning for wood species identification rely on labeled samples, this study introduces unsupervised learning into the field of wood identification. It proposes a novel wood-identification model called DCVAE (d...

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Vydáno v:Wood material science and engineering Ročník 19; číslo 5; s. 1004 - 1014
Hlavní autoři: Wang, Yuan, Wang, Zhi-Gang, He, Yi-Hao, Avramidis, Stavros
Médium: Journal Article
Jazyk:angličtina
Vydáno: Abingdon Taylor & Francis 02.09.2024
Taylor & Francis Ltd
Témata:
Algorithms
Clustering
conditional variational autoencoder
Data augmentation
data collection
Feature extraction
Introduced species
Labels
Machine learning
materials science
pseudo-label
species
species identification
spectral analysis
Spectroscopy
Spectrum analysis
terahertz time domain spectroscopy
Unsupervised learning
Wood
Wood identification
ISSN:1748-0272, 1748-0280, 1748-0280
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Shrnutí:In order to address the problem that existing methods combining spectral data and machine learning for wood species identification rely on labeled samples, this study introduces unsupervised learning into the field of wood identification. It proposes a novel wood-identification model called DCVAE (deep conditional variational autoencoder)-PLCAE (pseudo-label convolutional autoencoders). Terahertz time-domain spectra of wood samples at breast height of five broadleaf and five coniferous species were obtained (40 samples of each species of wood, 400 in total). A conditional variational autoencoder was applied to augment the terahertz spectroscopy dataset. Subsequently, a pseudo-label-guided deep clustering model was developed to extract more discriminative deep features. The model was compared with three traditional clustering algorithms and four deep clustering methods. Clustering experiments and visualization results show that the comprehensive clustering performance of DCVAE-PLCAE is better than the other comparative algorithms and that the extracted low-dimensional features has a more straightforward structure. The algorithm in this study can solve the problems of fewer labeled samples and the difficulty of extracting discriminative features by traditional clustering algorithms.
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ISSN:1748-0272
1748-0280
1748-0280
DOI:10.1080/17480272.2023.2293177