Abstract
We propose a hardwood species identification method based on wood hyperspectral microscopic images. A SOC710VP hyperspectral stereomicroscope was used to acquire microscopic images of a hardwood cross section. In these microscopic images, each part’s spectral features are discussed. We found that the spectral divisibility of wood vessels’ peripheral and central regions in the hyperspectral microscopic images can be used for hardwood species recognition. Mathematical morphological operation and K-L divergence were used to extract spectral features at the wood vessels’ peripheral regions and central regions, respectively. By comparing wood vessels’ spectral similarity across wood species samples, we found that wood vessels’ peripheral spectral divisibility is larger than its central. Finally, the spectral information from randomly selected regions of interest (i.e., ROI) and that of wood vessels’ peripheral and central regions have value as a classification basis. In our hardwood species classification experiments, three dimensionality reduction algorithms, principal component analysis (PCA), kernel principal component analysis (KPCA), and multidimensional scaling (MDS), and the three classifiers, BP neural network, support vector machine (SVM), and Mahalanobis distance (MD), are combined to perform hardwood species classification work. Experimental results indicate that the best recognition effect can be achieved at the peripheral region of wood vessels using PCA or MDS with the MD algorithm.
Highlights
Wood is an important renewable natural resource
Pan and Kudo [1] and Yusof et al [2] used microscopic images of wood cross sections to discuss the characteristics of different hardwood species
We have proposed a novel hardwood species identification scheme using hardwood microscopic hyperspectral images
Summary
Wood is an important renewable natural resource. Different tree species result in many different wood products circulating in the market. Many hardwood species are widely used to make furniture, and they cannot be identified by visual observation or odor discrimination. Hardwood misclassification may result in significant economic losses due to the differences in quality and price of different hardwood species. With the rapid development of machine vision, it has become possible to identify the processed hardwood species by using intelligent methods. Pan and Kudo [1] and Yusof et al [2] used microscopic images of wood cross sections to discuss the characteristics of different hardwood species
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