{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T03:22:42Z","timestamp":1769743362086,"version":"3.49.0"},"reference-count":39,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2023,7,8]],"date-time":"2023-07-08T00:00:00Z","timestamp":1688774400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union","award":["CPER IDEAL 2021\u20132027"],"award-info":[{"award-number":["CPER IDEAL 2021\u20132027"]}]},{"name":"European Union","award":["ANR-21-EXES-00 11"],"award-info":[{"award-number":["ANR-21-EXES-00 11"]}]},{"name":"European Regional Development Fund (ERDF)","award":["CPER IDEAL 2021\u20132027"],"award-info":[{"award-number":["CPER IDEAL 2021\u20132027"]}]},{"name":"European Regional Development Fund (ERDF)","award":["ANR-21-EXES-00 11"],"award-info":[{"award-number":["ANR-21-EXES-00 11"]}]},{"name":"French State, and the French Region Hauts-de-France and Ifremer","award":["CPER IDEAL 2021\u20132027"],"award-info":[{"award-number":["CPER IDEAL 2021\u20132027"]}]},{"name":"French State, and the French Region Hauts-de-France and Ifremer","award":["ANR-21-EXES-00 11"],"award-info":[{"award-number":["ANR-21-EXES-00 11"]}]},{"name":"National Research Agency","award":["CPER IDEAL 2021\u20132027"],"award-info":[{"award-number":["CPER IDEAL 2021\u20132027"]}]},{"name":"National Research Agency","award":["ANR-21-EXES-00 11"],"award-info":[{"award-number":["ANR-21-EXES-00 11"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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 C\u00f4te d\u2019Opale (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.<\/jats:p>","DOI":"10.3390\/rs15143455","type":"journal-article","created":{"date-parts":[[2023,7,10]],"date-time":"2023-07-10T00:47:35Z","timestamp":1688950055000},"page":"3455","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["A New Remote Hyperspectral Imaging System Embedded on an Unmanned Aquatic Drone for the Detection and Identification of Floating Plastic Litter Using Machine Learning"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9555-7471","authenticated-orcid":false,"given":"Ahed","family":"Alboody","sequence":"first","affiliation":[{"name":"Laboratoire d\u2019Informatique Signal et Image de la C\u00f4te d\u2019Opale, UR 4491, LISIC, University Littoral C\u00f4te d\u2019Opale, F-62100 Calais, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7766-4898","authenticated-orcid":false,"given":"Nicolas","family":"Vandenbroucke","sequence":"additional","affiliation":[{"name":"Laboratoire d\u2019Informatique Signal et Image de la C\u00f4te d\u2019Opale, UR 4491, LISIC, University Littoral C\u00f4te d\u2019Opale, F-62100 Calais, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1482-5447","authenticated-orcid":false,"given":"Alice","family":"Porebski","sequence":"additional","affiliation":[{"name":"Laboratoire d\u2019Informatique Signal et Image de la C\u00f4te d\u2019Opale, UR 4491, LISIC, University Littoral C\u00f4te d\u2019Opale, F-62100 Calais, France"}]},{"given":"Rosa","family":"Sawan","sequence":"additional","affiliation":[{"name":"Laboratoire d\u2019Oc\u00e9anologie et de G\u00e9osciences, University Littoral C\u00f4te d\u2019Opale, UMR 8187, LOG, CNRS, IRD, University Lille, F-62930 Wimereux, France"}]},{"given":"Florence","family":"Viudes","sequence":"additional","affiliation":[{"name":"Laboratoire d\u2019Oc\u00e9anologie et de G\u00e9osciences, University Littoral C\u00f4te d\u2019Opale, UMR 8187, LOG, CNRS, IRD, University Lille, F-62930 Wimereux, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5862-2115","authenticated-orcid":false,"given":"Perine","family":"Doyen","sequence":"additional","affiliation":[{"name":"University Littoral C\u00f4te d\u2019Opale, UMRt 1158, BioEcoAgro, USC Anses, INRAe, University Artois, University Lille, University Picardie Jules Verne, University Li\u00e8ge, Junia, F-62200 Boulogne-sur-Mer, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7183-1333","authenticated-orcid":false,"given":"Rachid","family":"Amara","sequence":"additional","affiliation":[{"name":"Laboratoire d\u2019Oc\u00e9anologie et de G\u00e9osciences, University Littoral C\u00f4te d\u2019Opale, UMR 8187, LOG, CNRS, IRD, University Lille, F-62930 Wimereux, France"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"114042","DOI":"10.1088\/1748-9326\/abbd01","article-title":"Machine learning for aquatic plastic litter detection, classification and quantification (APLASTIC-Q)","volume":"15","author":"Wolf","year":"2020","journal-title":"Environ. Res. Lett."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"e2022EA002518","DOI":"10.1029\/2022EA002518","article-title":"Toward robust river plastic detection: Combining lab and field-based hyperspectral imagery","volume":"9","author":"Tasseron","year":"2022","journal-title":"Earth Space Sci."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Tasseron, P., van Emmerik, T., Peller, J., Schreyers, L., and Biermann, L. (2021). Advancing floating macroplastic detection from space using experimental hyperspectral imagery. Remote Sens., 13.","DOI":"10.3390\/rs13122335"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Freitas, S., Silva, H., and Silva, E. (2021). Remote hyperspectral imaging acquisition and characterization for marine litter detection. Remote Sens., 13.","DOI":"10.3390\/rs13132536"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"113263","DOI":"10.1016\/j.rse.2022.113263","article-title":"Identifying distinct plastics in hyperspectral experimental lab, aircraft-, and satellite data using machine\/deep learning methods trained with synthetically mixed spectral data","volume":"281","author":"Zhou","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"5436","DOI":"10.1038\/s41598-021-84867-6","article-title":"Spectral reflectance of marine macroplastics in the VNIR and SWIR measured in a controlled environment","volume":"11","author":"Moshtaghi","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_7","first-page":"115040G","article-title":"Detection and identification of plastics using SWIR hyperspectral imaging","volume":"Volume 11504","author":"Mehrubeoglu","year":"2020","journal-title":"Imaging Spectrometry XXIV: Applications, Sensors, and Processing"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"648","DOI":"10.1093\/icesjms\/fsac006","article-title":"Aerial detection of beached marine plastic using a novel, hyperspectral short-wave infrared (SWIR) camera","volume":"79","author":"Cocking","year":"2022","journal-title":"ICES J. Mar. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"112414","DOI":"10.1016\/j.rse.2021.112414","article-title":"Remote detection of marine debris using satellite observations in the visible and near infrared spectral range: Challenges and potentials","volume":"259","author":"Hu","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Balsi, M., Moroni, M., Chiarabini, V., and Tanda, G. (2021). High-resolution aerial detection of marine plastic litter by hyperspectral sensing. Remote Sens., 13.","DOI":"10.3390\/rs13081557"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"745","DOI":"10.5194\/essd-15-745-2023","article-title":"Hyperspectral reflectance dataset of pristine, weathered and biofouled plastics","volume":"15","author":"Leone","year":"2022","journal-title":"Earth Syst. Sci. Data Discuss."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"112598","DOI":"10.1016\/j.rse.2021.112598","article-title":"A knowledge-based, validated classifier for the identification of aliphatic and aromatic plastics by WorldView-3 satellite data","volume":"264","author":"Zhou","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_13","first-page":"2825","article-title":"Finding plastic patches in coastal waters using optical satellite data","volume":"10","author":"Biermann","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_14","unstructured":"Bentley, J. (2023, March 28). Detecting Ocean Microplastics with Remote Sensing in the Near-Infrared: A Feasibility Study. Available online: https:\/\/vc.bridgew.edu\/cgi\/viewcontent.cgi?article=1309&context=honors_proj."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Topouzelis, K., Papageorgiou, D., Karagaitanakis, A., Papakonstantinou, A., and Ballesteros, M.A. (2020). Remote sensing of sea surface artificial floating plastic targets with Sentinel-2 and unmanned aerial systems (plastic litter project 2019). Remote Sens., 12.","DOI":"10.3390\/rs12122013"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"388","DOI":"10.1016\/j.marpolbul.2018.08.009","article-title":"Monitoring of beach litter by automatic interpretation of unmanned aerial vehicle images using the segmentation threshold method","volume":"137","author":"Bao","year":"2018","journal-title":"Mar. Pollut. Bull."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"112675","DOI":"10.1016\/j.marpolbul.2021.112675","article-title":"Floating marine litter detection algorithms and techniques using optical remote sensing data: A review","volume":"170","author":"Topouzelis","year":"2021","journal-title":"Mar. Pollut. Bull."},{"key":"ref_18","unstructured":"Ramavaram, H.R., Kotichintala, S., Naik, S., Critchley-Marrows, J., Isaiah, O.T., Pittala, M., Wan, S., and Irorere, D. (2018, January 1\u20135). Tracking Ocean Plastics Using Aerial and Space Borne Platforms: Overview of Techniques and Challenges. Proceedings of the 69th International Astronautical Congress (IAC), Bremen, Germany. IAC 2018 Congress Proceedings."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Iordache, M.-D., Keukelaere, L.D., Moelans, R., Landuyt, L., Moshtaghi, M., Corradi, P., and Knaeps, E. (2022). Targeting plastics: Machine learning applied to litter detection in aerial multispectral images. Remote Sens., 14.","DOI":"10.3390\/rs14225820"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"139632","DOI":"10.1016\/j.scitotenv.2020.139632","article-title":"Mapping marine litter on coastal dunes with unmanned aerial systems: A showcase on the Atlantic Coast","volume":"736","author":"Andriolo","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Geraeds, M., van Emmerik, T., de Vries, R., and bin Ab Razak, M.S. (2019). Riverine plastic litter monitoring using Unmanned Aerial Vehicles (UAVs). Remote Sens., 11.","DOI":"10.3390\/rs11172045"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Freitas, S., Silva, H., and Silva, E. (2022). Hyperspectral imaging zero-shot learning for remote marine litter detection and classification. Remote Sens., 14.","DOI":"10.3390\/rs14215516"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Kikaki, K., Kakogeorgiou, I., Mikeli, P., Raitsos, D.E., and Karantzalos, K. (2022). MARIDA: A benchmark for Marine Debris detection from Sentinel-2 remote sensing data. PLoS ONE, 17.","DOI":"10.1371\/journal.pone.0262247"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Freitas, S., Silva, H., Almeida, C., Viegas, D., Amaral, A., Santos, T., Dias, A., Jorge, P.A.S., Pham, C.K., and Moutinho, J. (2021, January 20\u201323). Hyperspectral imaging system for marine litter detection. Proceedings of the OCEANS 2021: San Diego\u2014Porto, San Diego, CA, USA.","DOI":"10.23919\/OCEANS44145.2021.9705953"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"e11662","DOI":"10.1016\/j.heliyon.2022.e11662","article-title":"An innovative approach for microplastic sampling in all surface water bodies using an aquatic drone","volume":"8","author":"Pasquier","year":"2022","journal-title":"Heliyon"},{"key":"ref_26","first-page":"1","article-title":"Plastic debris: Remote sensing and characterization data streams and micro-satellites reflected infrared spectroscopy raman spectroscopy great lakes marine debris network","volume":"22","author":"Driedger","year":"2007","journal-title":"Int. J. Remote Sens. Mar. Pollut. Bull."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"106217","DOI":"10.1016\/j.resconrec.2022.106217","article-title":"A review on chemometric techniques with infrared, Raman and laser-induced breakdown spectroscopy for sorting plastic waste in the recycling industry","volume":"180","author":"Neo","year":"2022","journal-title":"Resour. Conserv. Recycl."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"16553","DOI":"10.1364\/OE.451415","article-title":"Top-of-atmosphere hyper and multispectral signatures of submerged plastic litter with changing water clarity and depth","volume":"30","author":"Garaba","year":"2022","journal-title":"Opt. Express"},{"key":"ref_29","first-page":"4099","article-title":"Local manifold learning-based k-nearest-neighbor for hyperspectral image classification","volume":"48","author":"Ma","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1778","DOI":"10.1109\/TGRS.2004.831865","article-title":"Classification of hyperspectral remote sensing images with support vector machines","volume":"42","author":"Melgani","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"118902","DOI":"10.1016\/j.watres.2022.118902","article-title":"Close-range remote sensing-based detection and identification of macroplastics on water assisted by artificial intelligence: A review","volume":"222","author":"Gnann","year":"2022","journal-title":"Water Res."},{"key":"ref_32","unstructured":"Glorot, X., and Bengio, Y. (2010, January 13\u201315). Understanding the difficulty of training deep feedforward neural networks. Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, Sardinia, Italy. Available online: http:\/\/proceedings.mlr.press\/v9\/glorot10a\/glorot10a.pdf."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015, January 11\u201318). Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref_34","unstructured":"Snoek, J., Larochelle, H., and Adams, R.P. (2012). Practical bayesian optimization of machine learning algorithms. Adv. Neural Inf. Process. Syst., 25, Available online: https:\/\/arxiv.org\/pdf\/1206.2944."},{"key":"ref_35","unstructured":"Gelbart, M.A., Snoek, J., and Adams, R.P. (2014). Bayesian optimization with unknown constraints. arXiv."},{"key":"ref_36","first-page":"281","article-title":"Random search for hyper-parameter optimization","volume":"13","author":"Bergstra","year":"2012","journal-title":"J. Mach. Learn. Res."},{"key":"ref_37","unstructured":"Jolliffe, I.T. (2002). Principal Component Analysis, Springer. [2nd ed.]."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Lapajne, J., Knapi\u010d, M., and \u017dibrat, U. (2022). Comparison of selected dimensionality reduction methods for detection of root-knot nematode infestations in potato tubers using hyperspectral imaging. Sensors, 22.","DOI":"10.3390\/s22010367"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"48588","DOI":"10.1007\/s11356-022-18501-x","article-title":"Classification and distribution of freshwater microplastics along the Italian Po river by hyperspectral imaging","volume":"29","author":"Fiore","year":"2022","journal-title":"Environ. Sci. Pollut. Res."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/14\/3455\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:08:54Z","timestamp":1760126934000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/14\/3455"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,8]]},"references-count":39,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["rs15143455"],"URL":"https:\/\/doi.org\/10.3390\/rs15143455","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,8]]}}}