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Targeting plastics: Machine learning applied to litter detection in aerial multispectral images
Iordache, M.-D.; De Keukelaere, L.; Moelans, R.; Landuyt, L.; Moshtaghi, M.; Corradi, P.; Knaeps, E. (2022). Targeting plastics: Machine learning applied to litter detection in aerial multispectral images. Remote Sens. 14(22): 5820. https://dx.doi.org/10.3390/rs14225820
In: Remote Sensing. MDPI: Basel. ISSN 2072-4292; e-ISSN 2072-4292
Peer reviewed article  
Available in  Authors 
Keyword
    Marine/Coastal
Author keywords
    litter detection; plastic pollution; multispectral data; remotely piloted aircraft systems; machine learning; multiclass classification; airborne imagery
Project Top | Authors 
  • Plastic Flux for Innovation and Business Opportunities in Flanders
Authors  Top 
  • Iordache, M.-D.
  • De Keukelaere, L.
  • Moelans, R.
  • Landuyt, L.
  • Moshtaghi, M.
  • Corradi, P.
  • Knaeps, E.
Abstract
    The occurrence of litter in natural areas is nowadays one of the major environmental challenges. The uncontrolled dumping of solid waste in nature not only threatens wildlife on land and in water, but also constitutes a serious threat to human health. The detection and monitoring of areas affected by litter pollution is thus of utmost importance, as it allows for the cleaning of these areas and guides public authorities in defining mitigation measures. Among the methods used to spot littered areas, aerial surveillance stands out as a valuable alternative as it allows for the detection of relatively small such regions while covering a relatively large area in a short timeframe. In this study, remotely piloted aircraft systems equipped with multispectral cameras are deployed over littered areas with the ultimate goal of obtaining classification maps based on spectral characteristics. Our approach employs classification algorithms based on random forest approaches in order to distinguish between four classes of natural land cover types and five litter classes. The obtained results show that the detection of various litter types is feasible in the proposed scenario and the employed machine learning algorithms achieve accuracies superior to 85% for all classes in test data. The study further explores sources of errors, the effect of spatial resolution on the retrieved maps and the applicability of the designed algorithm to floating litter detection.
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