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Johan Mijnendonckx

Bio: Johan Mijnendonckx is an academic researcher from Flemish Institute for Technological Research. The author has an hindex of 1, co-authored 1 publications receiving 6 citations.

Papers
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TL;DR: In this paper, the authors describe a dataset consisting of 47 hyperspectral reflectance measurements of plastic litter samples from the Port of Antwerp in Belgium, including real and simulated plastic litter.
Abstract: . This paper describes a dataset consisting of 47 hyperspectral-reflectance measurements of plastic litter samples. The plastic litter samples include virgin and real samples from the Port of Antwerp. They were measured in dry conditions in the Flemish Institute for Technological Research (VITO) calibration facility, and a selection of the samples were also measured in wet conditions and submerged in a water tank at Flanders Hydraulics. The construction on top of the tank allowed us to submerge the plastics and keep sediments in suspension. The spectral measurements were performed using an Analytical Spectral Devices (ASD) FieldSpec 4 and a Spectral Evolution (SEV) spectrometer. The datasets are available on the 4TU.ResearchData open-access repository (ASD dataset: https://doi.org/10.4121/12896312.v2 , Knaeps et al., 2020; SEV dataset: https://doi.org/10.4121/uuid:9ee3be54-9132-415a-aaf2-c7fbf32d2199 ; Garaba et al., 2020).

19 citations


Cited by
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Journal ArticleDOI
TL;DR: 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.

32 citations

Journal ArticleDOI
TL;DR: In this paper, a review of the challenges in microplastics analysis is presented, focusing on improving spatial resolution to detect smaller MP and development of robust models for data analysis, as well as reporting quality assurance and quality control measures.
Abstract: A central challenge in microplastics (MP, diameter 250 μm, with drastic improvements in analysis time as compared with the best available technology, such as Fourier transform infrared (FT-IR) and Raman spectroscopy. Primary challenges we identified through the review include improving spatial resolution to detect smaller MP and development of robust models for data analysis. Parameters and practices for reporting quality assurance and quality control measures are summarized and recommendations are made for future research. We conclude that HSI is a promising technology for MP analysis but requires adaptation for this new application.

24 citations

Journal ArticleDOI
07 Jan 2022-PLOS ONE
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.
Abstract: Currently, a significant amount of research is focused on detecting Marine Debris and assessing its spectral behaviour via remote sensing, ultimately aiming at new operational monitoring solutions. Here, we introduce a Marine Debris Archive (MARIDA), as a benchmark dataset for developing and evaluating Machine Learning (ML) algorithms capable of detecting Marine Debris. MARIDA is the first dataset based on the multispectral Sentinel-2 (S2) satellite data, which distinguishes Marine Debris from various marine features that co-exist, including Sargassum macroalgae, Ships, Natural Organic Material, Waves, Wakes, Foam, dissimilar water types (i.e., Clear, Turbid Water, Sediment-Laden Water, Shallow Water), and Clouds. We provide annotations (georeferenced polygons/ pixels) from verified plastic debris events in several geographical regions globally, during different seasons, years and sea state conditions. A detailed spectral and statistical analysis of the MARIDA dataset is presented along with well-established ML baselines for weakly supervised semantic segmentation and multi-label classification tasks. MARIDA is 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.

23 citations

Journal ArticleDOI
TL;DR: In this article, a double camera setup covered the VIS-SWIR range from 400 to 1700 nm in a darkroom experiment with controlled illumination, and the cameras scanned the samples floating in water and captured high-resolution images in 336 spectral bands.
Abstract: Airborne and spaceborne remote sensing (RS) collecting hyperspectral imagery provides unprecedented opportunities for the detection and monitoring of floating riverine and marine plastic debris. However, a major challenge in the application of RS techniques is the lack of a fundamental understanding of spectral signatures of water-borne plastic debris. Recent work has emphasised the case for open-access hyperspectral reflectance reference libraries of commonly used polymer items. In this paper, we present and analyse a high-resolution hyperspectral image database of a unique mix of 40 virgin macroplastic items and vegetation. Our double camera setup covered the visible to shortwave infrared (VIS-SWIR) range from 400 to 1700 nm in a darkroom experiment with controlled illumination. The cameras scanned the samples floating in water and captured high-resolution images in 336 spectral bands. Using the resulting reflectance spectra of 1.89 million pixels in linear discriminant analyses (LDA), we determined the importance of each spectral band for discriminating between water and mixed floating debris, and vegetation and plastics. The absorption peaks of plastics (1215 nm, 1410 nm) and vegetation (710 nm, 1450 nm) are associated with high LDA weights. We then compared Sentinel-2 and Worldview-3 satellite bands with these outcomes and identified 12 satellite bands to overlap with important wavelengths for discrimination between the classes. Lastly, the Normalised Vegetation Difference Index (NDVI) and Floating Debris Index (FDI) were calculated to determine why they work, and how they could potentially be improved. These findings could be used to enhance existing efforts in monitoring macroplastic pollution, as well as form a baseline for the design of future multispectral RS systems.

17 citations

Journal ArticleDOI
TL;DR: The proposed proxy-based approach is a step towards future mapping techniques of suspected floating plastics with potential operational monitoring applications from the Sentinel-2 that recently started regular imaging over the GPGP that will be supported or validated by numerical solutions and net trawling survey.
Abstract: We present a direct and proxy-based approach to qualitatively and semi-quantitatively observe floating plastic litter in the Great Pacific Garbage Patch (GPGP) based on a survey in 2018 using very high geo-spatial resolution 8-waveband WorldView-3 imagery. A proxy for the plastics was defined as a waveband difference for anomalies in the top-of-the-atmosphere spectra. The anomalies were computed by subtracting spatially varying reflectance of the surrounding ocean water as background from the top-of-the-atmosphere reflectance. Spectral shapes and magnitude were also evaluated using a reference target of known plastics, The Ocean Cleanup System 001 Wilson. Presence of ‘suspected plastics’ was confirmed by the similarity in derived anomalies and spectral shapes with respect to the known plastics in the image as well as direct observations in the true color composites. The proposed proxy-based approach is a step towards future mapping techniques of suspected floating plastics with potential operational monitoring applications from the Sentinel-2 that recently started regular imaging over the GPGP that will be supported or validated by numerical solutions and net trawling survey.

8 citations