Information fusion technology for terahertz spectra and hyperspectral imaging in wood species identification

This paper proposes the use of information fusion technology to identify different wood species by combining spectral and spatial information from hyperspectral images and terahertz (THz) spectra. The study utilized five species of coniferous wood as experimental samples. The hyperspectral and terahertz raw images and spectra acquired by the spectroscopic instruments were preprocessed using standard normal variational transform (SNV). Three methods, namely, competitive adaptive reweighting (CARS), uninformative variable elimination (UVE), and random frog hopping (RF), were employed to select relevant frequency features in both hyperspectral image spectral information and THz spectra. For hyperspectral image spatial information, three algorithms, grayscale co-occurrence matrix (GLCM), local binary pattern (LBP), and Gaussian Markov random field (GMRF) were used to extract texture features. Subsequently, these three sets of extracted features were recognized separately using an extreme learning machine (ELM) model. The results showed that the accuracies achieved by the three features alone in wood identification were 71.8