A Vision for Cleaner Rivers: Harnessing Snapshot Hyperspectral Imaging
  to Detect Macro-Plastic Litter
Nathaniel Hanson Ahmet Demirkaya Deniz Erdoğmuş Aron Stubbins Taşkın Padır Tales Imbiriba
Plastic waste entering the riverine harms local ecosystems leading to
negative ecological and economic impacts. Large parcels of plastic waste are
transported from inland to oceans leading to a global scale problem of floating
debris fields. In this context, efficient and automatized monitoring of
mismanaged plastic waste is paramount. To address this problem, we analyze the
feasibility of macro-plastic litter detection using computational imaging
approaches in river-like scenarios. We enable near-real-time tracking of
partially submerged plastics by using snapshot Visible-Shortwave Infrared
hyperspectral imaging. Our experiments indicate that imaging strategies
associated with machine learning classification approaches can lead to high
detection accuracy even in challenging scenarios, especially when leveraging
hyperspectral data and nonlinear classifiers. All code, data, and models are
available online:
https://github.com/RIVeR-Lab/hyperspectral_macro_plastic_detection.