Unsupervised wood species identification based on multiobjective optimal clustering and feature fusion

IF 3.1 2区 农林科学 Q1 FORESTRY
Yuan Wang, Wen-Jin Ma, Meng Yang, Ren-He Qu, Stavros Avramidis
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Abstract

This paper proposes an unsupervised wood species identification approach utilizing multiobjective optimization clustering and feature fusion. To address the inherent limitations of single-band spectra in capturing comprehensively wood characteristics, this approach integrates preprocessed low-dimensional terahertz (THz) and hyperspectral data. Additionally, to address the challenge of selecting the optimal k-value in clustering, an unsupervised wood clustering algorithm was developed, employing multiobjective optimization and evolutionary algorithms. This proposed algorithm incorporated a prototype coding method for initialization, density peak clustering for pattern identification, and an improved firefly optimization algorithm to introduce cross-variation and maintain population diversity. To further refine the clustering process, a selection operator based on grid division and fast non-dominated sorting was designed, optimizing the clustering performance. The model was evaluated on a dataset containing hyperspectral and THz spectra from 400 samples, representing ten wood species—five coniferous and five broadleaf species. Experimental results indicated that fusing the spectral data resulted in a 3.5% increase in clustering purity compared to individual datasets. Moreover, the proposed algorithm outperformed established clustering methods such as DBSCAN, OPTICS, and density peak clustering, achieving a maximum clustering purity of 91.25% across both internal and external clustering metrics. These findings demonstrate the effectiveness of the multi-spectral fusion approach and the proposed algorithm in enhancing wood species identification accuracy, offering a promising avenue for improving non-destructive evaluation methods in forestry and material sciences.

Abstract Image

基于多目标最优聚类和特征融合的无监督树种识别
提出了一种基于多目标优化聚类和特征融合的无监督树种识别方法。为了解决单波段光谱在全面捕获木材特征方面的固有局限性,该方法集成了预处理的低维太赫兹(THz)和高光谱数据。此外,为了解决聚类中选择最优k值的挑战,开发了一种采用多目标优化和进化算法的无监督木材聚类算法。该算法采用原型编码方法进行初始化,采用密度峰聚类方法进行模式识别,采用改进的萤火虫优化算法引入交叉变异,保持种群多样性。为了进一步细化聚类过程,设计了基于网格划分和快速非支配排序的选择算子,优化了聚类性能。该模型在包含400个样本的高光谱和太赫兹光谱数据集上进行了评估,这些样本代表了10种木材——5种针叶树和5种阔叶树。实验结果表明,与单个数据集相比,融合光谱数据可使聚类纯度提高3.5%。此外,该算法优于现有的聚类方法,如DBSCAN、OPTICS和密度峰聚类,在内部和外部聚类指标上实现了91.25%的最大聚类纯度。这些发现证明了多光谱融合方法和算法在提高木材树种识别精度方面的有效性,为改进林业和材料科学的无损评估方法提供了一条有希望的途径。
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来源期刊
Wood Science and Technology
Wood Science and Technology 工程技术-材料科学:纸与木材
CiteScore
5.90
自引率
5.90%
发文量
75
审稿时长
3 months
期刊介绍: Wood Science and Technology publishes original scientific research results and review papers covering the entire field of wood material science, wood components and wood based products. Subjects are wood biology and wood quality, wood physics and physical technologies, wood chemistry and chemical technologies. Latest advances in areas such as cell wall and wood formation; structural and chemical composition of wood and wood composites and their property relations; physical, mechanical and chemical characterization and relevant methodological developments, and microbiological degradation of wood and wood based products are reported. Topics related to wood technology include machining, gluing, and finishing, composite technology, wood modification, wood mechanics, creep and rheology, and the conversion of wood into pulp and biorefinery products.
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