A New Remote Hyperspectral Imaging System Embedded on an Unmanned Aquatic Drone for the Detection and Identification of Floating Plastic Litter Using Machine Learning

Autor: Alboody, Ahed, Vandenbroucke, Nicolas, Porebski, Alice, Sawan, Rosa, Viudes, Florence, Doyen, Perine, Amara, Rachid
Přispěvatelé: Laboratoire d'Informatique Signal et Image de la Côte d'Opale (LISIC), Université du Littoral Côte d'Opale (ULCO), Laboratoire d’Océanologie et de Géosciences (LOG) - UMR 8187 (LOG), Institut national des sciences de l'Univers (INSU - CNRS)-Université du Littoral Côte d'Opale (ULCO)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Nord]), Biochimie des Produits Aquatiques (BPA), Université du Littoral Côte d'Opale (ULCO)-Transfrontalière BioEcoAgro - UMR 1158 (BioEcoAgro), Université d'Artois (UA)-Université de Liège-Université de Picardie Jules Verne (UPJV)-Université du Littoral Côte d'Opale (ULCO)-Université de Lille-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-JUNIA (JUNIA), Université catholique de Lille (UCL)-Université catholique de Lille (UCL)-Université d'Artois (UA)-Université de Liège-Université de Picardie Jules Verne (UPJV)-Université du Littoral Côte d'Opale (ULCO)-Université de Lille-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-JUNIA (JUNIA), Université catholique de Lille (UCL)-Université catholique de Lille (UCL), This work was financially supported by the European Union, the European Regional Development Fund (ERDF), the French State, and the French Region Hauts-de-France and Ifremer, in the framework of the project CPER IDEAL 2021–2027., ANR-21-EXES-0011,IFSEA,Transdisciplinary graduate school for marIne, Fisheries and SEAfood sciences(2021)
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Remote Sensing
Remote Sensing, 2023, 15 (14), pp.3455. ⟨10.3390/rs15143455⟩
ISSN: 2072-4292
DOI: 10.3390/rs15143455⟩
Popis: International audience; This paper presents a new Remote Hyperspectral Imaging System (RHIS) embedded on an Unmanned Aquatic Drone (UAD) for plastic detection and identification in coastal and freshwater environments. This original system, namely the Remotely Operated Vehicle of the University of Littoral Côte d’Opale (ROV-ULCO), works in a near-field of view, where the distance between the hyperspectral camera and the water surface is about 45 cm. In this paper, the new ROV-ULCO system with all its components is firstly presented. Then, a hyperspectral image database of plastic litter acquired with this system is described. This database contains hyperspectral data cubes of different plastic types and polymers corresponding to the most-common plastic litter items found in aquatic environments. An in situ spectral analysis was conducted from this benchmark database to characterize the hyperspectral reflectance of these items in order to identify the absorption feature wavelengths for each type of plastic. Finally, the ability of our original system RHIS to automatically recognize different types of plastic litter was assessed by applying different supervised machine learning methods on a set of representative image patches of marine litter. The obtained results highlighted the plastic litter classification capability with an overall accuracy close to 90%. This paper showed that the newly presented RHIS coupled with the UAD is a promising approach to identify plastic waste in aquatic environments.
Databáze: OpenAIRE
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