Advancing floating macroplastic detection from space using hyperspectral imagery

Airborne and spaceborne remote sensing (RS) collecting hyperspectral imagery provides unprecedented opportunities for the detection and monitoring of floating riverine and marine plastic debris. However, a major challenge in the application of RS techniques is the lack of fundamental understanding of spectral signatures of water-borne plastic debris. Recent work has emphasised the case for open-access hyperspectral reflectance reference libraries of commonly used polymer items. In this paper, we present and analyse a high-resolution hyperspectral image databaseof a unique mix of 40 virgin macroplastic items and vegetation. Our double camera setup covered the visual to shortwave infrared (VIS-SWIR) range from 400-1700 nm in a dark room experiment with controlled illumination. The cameras scanned the samples floating in water and captured high-resolution images in 336 spectral bands. Using these resulting reflectance spectra as a baseline, a linear discriminant analysis was done to determine which wavelengths are more useful for discriminating between water and mixed floating debris, and vegetation and plastics. We then examined current Sentinel-2 and Worldview-3 satellite techniques, and the Normalised Vegetation Difference Index (NDVI) and Floating Debris Index (FDI) to determine why they work, and how they could potentially be improved. These findings could be used to enhance existing efforts in monitoring macroplastic pollution, as well as form a baseline for the design of future multispectral RS systems.