Optical Detection of Marine Debris Using Deep Knockoff

Mohammadali Olyaei, Ardeshir Ebtehaj, Jiarong Hong

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

This article investigates the use of deep knockoff, a modern statistical variable selection methodology, to uncover the spectral signatures of marine debris (MD). This method uses a generative model by leveraging deep neural networks (DNNs) to learn the high-dimensional distribution of reflectance in visible to near infrared (NIR) wavelengths. To that end, a public dataset obtained from ground-based labeling of the observations by the multispectral instrument (MSI) on-board Sentinel-2 (S2) satellite is used. Through controlling the false discovery rate (FDR), consistent with the known physical causalities, the results indicate that the NIR (band 8, 833 nm) and red (band 4, 665 nm) are the most important bands, respectively, for discrimination of marine (plastic) debris from the background water. In the presence of dense Sargassum macroalgae, the deep knockoff isolates the green (band 3, 560 nm) and the narrow NIR (band 8a, 864.7 nm) as another important band.

Original languageEnglish (US)
Article number5413912
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume60
DOIs
StatePublished - 2022

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

  • Deep knockoffs
  • Indexes
  • Input variables
  • Marine debris
  • optical
  • Optical reflection
  • Optical sensors
  • Plastics
  • Reflectivity
  • remote sesning
  • Satellites
  • Sentinel-2
  • short-infrared

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