A novel hyperspectral imaging and modeling method for the component identification of woven fabrics

Abstract

The component identification of textile materials is critical for quality control and measurement in the textile field. A novel hyperspectral imaging method and the related identification model are proposed to classify single-component textiles. Firstly, the hyperspectral data of the single-component fabrics were processed to conduct dimensionality reduction based on locally linear embedding (LLE), principal component analysis (PCA), and locally preserving projection (LPP) algorithms. Moreover, the original data of 288 wavelengths from 920 nm to 2500 nm were compressed to keep the typical wavelength regions. After that, these data were imported into two classifiers (decision tree classifier and K nearest neighbor (KNN) classifier) for training, and an identification model based on these training data was developed for the sample classification. The experimental results showed that all the samples could be identified correctly by the established identification model. The recognition rate and the stability of the classifier based on LPP model and KNN classification algorithm were proved to have the highest accuracy in our research.