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Article

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

by
Mohammadali Olyaei
* and
Ardeshir Ebtehaj
Department of Civil, Environmental and Geo-Engineering, University of Minnesota, Minneapolis, MN 55455, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2024, 16(1), 172; https://doi.org/10.3390/rs16010172
Submission received: 28 November 2023 / Revised: 28 December 2023 / Accepted: 29 December 2023 / Published: 31 December 2023
(This article belongs to the Section AI Remote Sensing)

Abstract

This article provides insights into the optical signatures of plastic litter based on a published laboratory-scale reflectance data set (350–2500 nm) 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 nm that produce relatively broad reflectance peaks in the measured spectra of wet plastics around 475–490, 635–650, 810–815, and 1070 nm. The results of band selection consistently identify three important regions across 450–470, 650–690, and 1050–1100 nm 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 nm) 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 nm 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 nm, (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 nm.
Keywords: plastic litter; hyperspectral remote sensing; band selection; sparse variable selection; hierarchical clustering; density peak clustering plastic litter; hyperspectral remote sensing; band selection; sparse variable selection; hierarchical clustering; density peak clustering

Share and Cite

MDPI and ACS Style

Olyaei, M.; Ebtehaj, A. Uncovering Plastic Litter Spectral Signatures: A Comparative Study of Hyperspectral Band Selection Algorithms. Remote Sens. 2024, 16, 172. https://doi.org/10.3390/rs16010172

AMA Style

Olyaei M, Ebtehaj A. Uncovering Plastic Litter Spectral Signatures: A Comparative Study of Hyperspectral Band Selection Algorithms. Remote Sensing. 2024; 16(1):172. https://doi.org/10.3390/rs16010172

Chicago/Turabian Style

Olyaei, Mohammadali, and Ardeshir Ebtehaj. 2024. "Uncovering Plastic Litter Spectral Signatures: A Comparative Study of Hyperspectral Band Selection Algorithms" Remote Sensing 16, no. 1: 172. https://doi.org/10.3390/rs16010172

APA Style

Olyaei, M., & Ebtehaj, A. (2024). Uncovering Plastic Litter Spectral Signatures: A Comparative Study of Hyperspectral Band Selection Algorithms. Remote Sensing, 16(1), 172. https://doi.org/10.3390/rs16010172

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