Application of Deep Learning on Hyperspectral Data for Classification of Shoe Upper Materials

The growing environmental awareness in society is driving an increasing demand for second-hand clothing and footwear in Europe. However, the high labor costs in EU countries make manual sorting of clothing not economically viable. Effective classification of garment and footwear materials using traditional solutions, such as mono/RGB cameras, is not possible. In our article, we describe the development and implementation of an automated system for classifying upper shoe materials on a sorting line, utilizing artificial neural networks to analyze images from hyperspectral cameras in the NIR-SWIR range (900–1700 nm).