Spectral reflectance of marine macroplastics in the VNIR and SWIR measured in a controlled environment
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TLDR
In this paper, the hyperspectral reflectances of virgin and naturally weathered polyethylene terephthalate (PET) submerged in water at varying suspended sediment concentrations and depth were studied.Abstract:
While at least 8 million tons of plastic litter are ending up in our oceans every year and research on marine litter detection is increasing, the spectral properties of wet as well as submerged plastics in natural marine environments are still largely unknown. Scientific evidence-based knowledge about these spectral characteristics has relevance especially to the research and development of future remote sensing technologies for plastic litter detection. In an effort to bridge this gap, we present one of the first studies about the hyperspectral reflectances of virgin and naturally weathered plastics submerged in water at varying suspended sediment concentrations and depth. We also conducted further analyses on the different polymer types such as Polyethylene terephthalate (PET), Polypropylene (PP), Polyester (PEST) and Low-density polyethylene (PE-LD) to better understand the effect of water absorption on their spectral reflectance. Results show the importance of using spectral wavebands in both the visible and shortwave infrared (SWIR) spectrum for litter detection, especially when plastics are wet or slightly submerged which is often the case in natural aquatic environments. Finally, we demonstrate in an example how to use the open access data set driven from this research as a reference for the development of marine litter detection algorithms.read more
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MARIDA: A benchmark for Marine Debris detection from Sentinel-2 remote sensing data
K. Kikaki,Ioannis Kakogeorgiou,Paraskevi Mikeli,Dionysios E. Raitsos,Konstantinos Karantzalos +4 more
TL;DR: An open-access dataset which enables the research community to explore the spectral behaviour of certain floating materials, sea state features and water types, to develop and evaluate Marine Debris detection solutions based on artificial intelligence and deep learning architectures, as well as satellite pre-processing pipelines.
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Advancing Floating Macroplastic Detection from Space Using Experimental Hyperspectral Imagery
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Operational use of multispectral images for macro-litter mapping and categorization by Unmanned Aerial Vehicle.
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TL;DR: In this paper , the use of multispectral images (5 bands) to classify litter type and material on a beach-dune system was used for marine litter surveys.
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Marine plastic litter detection offshore Hawai'i by Sentinel-2.
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Development of automated marine floating plastic detection system using Sentinel-2 imagery and machine learning models.
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