Shortwave infrared hyperspectral reflectance imaging for cotton foreign matter classification

Abstract

Cotton contaminants seriously reduce the commercial value of cotton lint and further degrade the quality of textile products. This research aims to investigate the potential of a non-contact technique, i.e., liquid crystal tunable filter (LCTF) hyperspectral imaging, to inspect foreign matter on the surface of cotton lint. The foreign matter samples used in this study included 11 types of botanical foreign matter and 5 types of non-botanical foreign matter. Hyperspectral images of the foreign matter were acquired using a LCTF hyperspectral imaging system with a spectral range from 900 to 1700nm. The mean spectra of the foreign matter and lint samples were extracted manually from the images. Linear discriminant analysis was applied to classify different types of foreign matter and cotton lint according to their spectral features. Classification accuracies of 96.5% and 95.1% were achieved with leave-one-out and four fold cross-validation, respectively. For pixel-level image classification, a majority of the pixels for different types of foreign matter were classified correctly by a support vector machine, using the top features of the minimum noise fraction transformation. The results demonstrate that non-contact liquid crystal tunable filter hyperspectral imaging is a promising method to discriminate foreign matter materials from cotton lint.