Spectral Analysis of Marine Debris in Simulated and Observed
  Sentinel-2/MSI Images using Unsupervised Classification
Bianca Matos de Barros Douglas Galimberti Barbosa Cristiano Lima Hackmann
Marine litter poses significant threats to marine and coastal environments,
with its impacts ever-growing. Remote sensing provides an advantageous
supplement to traditional mitigation techniques, such as local cleaning
operations and trawl net surveys, due to its capabilities for extensive
coverage and frequent observation. In this study, we used Radiative Transfer
Model (RTM) simulated data and data from the Multispectral Instrument (MSI) of
the Sentinel-2 mission in combination with machine learning algorithms. Our aim
was to study the spectral behavior of marine plastic pollution and evaluate the
applicability of RTMs within this research area. The results from the
exploratory analysis and unsupervised classification using the KMeans algorithm
indicate that the spectral behavior of pollutants is influenced by factors such
as the type of polymer and pixel coverage percentage. The findings also reveal
spectral characteristics and trends of association and differentiation among
elements. The applied methodology is strongly dependent on the data, and if
reapplied in new, more diverse, and detailed datasets, it can potentially
generate even better results. These insights can guide future research in
remote sensing applications for detecting marine plastic pollution.