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Article

Remote Hyperspectral Imaging Acquisition and Characterization for Marine Litter Detection

1
INESCTEC—Institute for Systems and Computer Engineering, Technology and Science, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
2
ISEP—School of Engineering, Polytechnic Institute of Porto, Rua Dr. António Bernardino de Almeida 431, 4249-015 Porto, Portugal
*
Author to whom correspondence should be addressed.
Academic Editors: Lefei Zhang, Liangpei Zhang, Qian Shi and Yanni Dong
Remote Sens. 2021, 13(13), 2536; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13132536
Received: 4 May 2021 / Revised: 15 June 2021 / Accepted: 23 June 2021 / Published: 29 June 2021
(This article belongs to the Special Issue Recent Advances in Hyperspectral Image Processing)
This paper addresses the development of a remote hyperspectral imaging system for detection and characterization of marine litter concentrations in an oceanic environment. The work performed in this paper is the following: (i) an in-situ characterization was conducted in an outdoor laboratory environment with the hyperspectral imaging system to obtain the spatial and spectral response of a batch of marine litter samples; (ii) a real dataset hyperspectral image acquisition was performed using manned and unmanned aerial platforms, of artificial targets composed of the material analyzed in the laboratory; (iii) comparison of the results (spatial and spectral response) obtained in laboratory conditions with the remote observation data acquired during the dataset flights; (iv) implementation of two different supervised machine learning methods, namely Random Forest (RF) and Support Vector Machines (SVM), for marine litter artificial target detection based on previous training. Obtained results show a marine litter automated detection capability with a 70–80% precision rate of detection in all three targets, compared to ground-truth pixels, as well as recall rates over 50%. View Full-Text
Keywords: hyperspectral imaging; remote sensing; unmanned aerial vehicles; marine litter; machine learning; deep learning hyperspectral imaging; remote sensing; unmanned aerial vehicles; marine litter; machine learning; deep learning
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MDPI and ACS Style

Freitas, S.; Silva, H.; Silva, E. Remote Hyperspectral Imaging Acquisition and Characterization for Marine Litter Detection. Remote Sens. 2021, 13, 2536. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13132536

AMA Style

Freitas S, Silva H, Silva E. Remote Hyperspectral Imaging Acquisition and Characterization for Marine Litter Detection. Remote Sensing. 2021; 13(13):2536. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13132536

Chicago/Turabian Style

Freitas, Sara, Hugo Silva, and Eduardo Silva. 2021. "Remote Hyperspectral Imaging Acquisition and Characterization for Marine Litter Detection" Remote Sensing 13, no. 13: 2536. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13132536

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