Material Identification in Complex Environments: Neural Network Approaches to Hyperspectral Image Analysis

Hyperspectral imagery is often used in chemometric studies for quality sorting and recycling due to its ability to produce rich spectroscopy data. In this paper, we study plastics detection in a complex environment. In particular, we analyze hyperspectral images of three scenes with spectra in the near-infrared and visible wavelength ranges; our task is to detect plastic within the scenes. The images contain materials with high intraclass variability and significant mixing. Our novel contribution compares various methods for hyperspectral pixel classification in these complicated, real-world environments, specifically deep methods such as contrastive learning and autoencoders, as well as comparing the viability of the hyperspectral cameras’ light spectrum for the application of plastic detection.