Uncovering Plastic Litter Spectral Signatures: A Comparative Study of Hyperspectral Band Selection Algorithms

Mohammadali Olyaei, Ardeshir Ebtehaj

Research output: Contribution to journalArticlepeer-review

Abstract

This article provides insights into the optical signatures of plastic litter based on a published laboratory-scale reflectance data set (350–2500 (Formula presented.)) of dry and wet plastic debris under clear and turbid waters using different band selection techniques, including sparse variable selection, density peak clustering, and hierarchical clustering. The variable selection method identifies important wavelengths by minimizing a reconstruction error metric, while clustering approaches rely on the strengths of the correlation and local density of the spectra. Analyses of the data reveal three distinct absorption lines at 560, 740, and 980 (Formula presented.) that produce relatively broad reflectance peaks in the measured spectra of wet plastics around 475–490, 635–650, 810–815, and 1070 (Formula presented.). The results of band selection consistently identify three important regions across 450–470, 650–690, and 1050–1100 (Formula presented.) that are close to the reflectance peaks of the mean of wet plastic spectra over clear and turbid waters. However, as the number of isolated important wavelengths increases, the results of the methodologies diverge. Density peak clustering identifies additional wavelengths in the short-wave infrared (SWIR) region of 1170–1180 (Formula presented.)) as a result of a high local density of the reflectance points. In contrast, hierarchical clustering isolates more wavelengths in the visible range of 365–400 (Formula presented.) due to weak correlations of nearby wavelengths. The results of the clustering methods are not consistent with the visual inspection of the signatures as peaks and valleys in the spectra, which are effectively captured by the variable selection method. It is also found that the presence of suspended sediments can (i) shift the important wavelength towards higher values in the visible part of the spectrum by less than 50 (Formula presented.), (ii) attenuate the magnitude of wet plastic reflectance by up to 80% across the entire spectrum, and (iii) manifest a similar spectral signature with plastic litter from 1070 to 1100 (Formula presented.).

Original languageEnglish (US)
Article number172
JournalRemote Sensing
Volume16
Issue number1
DOIs
StatePublished - Jan 2024
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2023 by the authors.

Keywords

  • band selection
  • density peak clustering
  • hierarchical clustering
  • hyperspectral remote sensing
  • plastic litter
  • sparse variable selection

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