Cross-Domain Machine Learning Approaches using Hyperspectral Imaging for Plastics litter detection

Plastics detection and polymer separation are important tasks for environmental monitoring and waste recycling. The authors previously used hyperspectral data in the lower bands of the SWIR range from 900nm to 1700 nm to perform plastics classification, by applying supervised machine learning methods to data gathered by a custom-assembled stand-alone push-broom system in indoor and outdoor conditions. In this work, we explored the application of two unsupervised learning algorithms: K-Means clustering and Gaussian Mixture Models (GMM), in order to avoid the use of a manually-labeled test set, and to obtain more reliable and flexible application in different environmental conditions. The results suggest that GMM performs well indoors but poorly outside, with GMM exhibiting subtle segmentation and K-Means displaying broader, more generalist categorization. The cross-domain adaptation is difficult and models that balance granularity and generalization are needed to detect plastic in varied settings. This effort advances garbage management and recycling and establishes hyperspectral imaging in real-world environments.