Plastics waste identification in river ecosystems by multispectral proximal sensing: a preliminary methodology study

Giglio, Michaela De; Dubbini, Marco; Cortesi, I.; Maraviglia, M.; Parisi, E. I.; Tucci, Grazia

doi:10.1111/wej.12652

Cited by 11 publications

(9 citation statements)

References 14 publications

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“…Recently, remote sensing (RS) that collects multi-to hyperspectral imagery has started to show far-reaching potential for the detection and monitoring of riverine and marine plastic pollution [13,14]. For example, Topouzelis, et al [15] and Themistocleous, et al [16] successfully identified large artificial plastic targets in coastal zones using Sentinel-2 imagery.…”

Section: Introductionmentioning

confidence: 99%

Advancing Floating Macroplastic Detection from Space Using Experimental Hyperspectral Imagery

et al. 2021

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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.

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Section: Introductionmentioning

confidence: 99%

Advancing Floating Macroplastic Detection from Space Using Experimental Hyperspectral Imagery

et al. 2021

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“…Finally, it is possible to capture up to 25 bands in different spectral ranges in a single shot. These new features allow spectral imaging to expand to new areas of use that were unthinkable a few years ago, such as disease [ 5 ] or water stress detection [ 6 ] in crops from on-board drones or autonomous robots, food inspection [ 7 ], material classification [ 8 ], cancer diagnosis [ 9 ], or plant phenotyping [ 10 ], among others. A difference between hyperspectral and multispectral sensing technology is the extent of the reflectance spectrum captured.…”

Section: Contextmentioning

confidence: 99%

A novel ground truth multispectral image dataset with weight, anthocyanins, and Brix index measures of grape berries tested for its utility in machine learning pipelines

et al. 2022

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Background The combination of computer vision devices such as multispectral cameras coupled with artificial intelligence has provided a major leap forward in image-based analysis of biological processes. Supervised artificial intelligence algorithms require large ground truth image datasets for model training, which allows to validate or refute research hypotheses and to carry out comparisons between models. However, public datasets of images are scarce and ground truth images are surprisingly few considering the numbers required for training algorithms. Results We created a dataset of 1,283 multidimensional arrays, using berries from five different grape varieties. Each array has 37 images of wavelengths between 488.38 and 952.76 nm obtained from single berries. Coupled to each multispectral image, we added a dataset with measurements including, weight, anthocyanin content, and Brix index for each independent grape. Thus, the images have paired measures, creating a ground truth dataset. We tested the dataset with 2 neural network algorithms: multilayer perceptron (MLP) and 3-dimensional convolutional neural network (3D-CNN). A perfect (100% accuracy) classification model was fit with either the MLP or 3D-CNN algorithms. Conclusions This is the first public dataset of grape ground truth multispectral images. Associated with each multispectral image, there are measures of the weight, anthocyanins, and Brix index. The dataset should be useful to develop deep learning algorithms for classification, dimensionality reduction, regression, and prediction analysis.

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“…Recently, remote sensing (RS) collecting multi-to hyperspectral imagery has started to show far-reaching potential for detection and monitoring of riverine and marine plastic pollution [13,14]. For example, Topouzelis et al [15] and Themistocleous et al [16] successfully identified large artificial plastic targets in coastal zones using Sentinel-2 imagery.…”

Section: Introductionmentioning

confidence: 99%

Advancing floating macroplastic detection from space using hyperspectral imagery

Tasseron¹,

Emmerik²,

Peller³

et al. 2022

Preprint

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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 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 databaseof a unique mix of 40 virgin macroplastic items and vegetation. Our double camera setup covered the visual to shortwave infrared (VIS-SWIR) range from 400-1700 nm in a dark room experiment with controlled illumination. The cameras scanned the samples floating in water and captured high-resolution images in 336 spectral bands. Using these resulting reflectance spectra as a baseline, a linear discriminant analysis was done to determine which wavelengths are more useful for discriminating between water and mixed floating debris, and vegetation and plastics. We then examined current Sentinel-2 and Worldview-3 satellite techniques, and the Normalised Vegetation Difference Index (NDVI) and Floating Debris Index (FDI) 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.

show abstract

Plastics waste identification in river ecosystems by multispectral proximal sensing: a preliminary methodology study

Cited by 11 publications

References 14 publications

Advancing Floating Macroplastic Detection from Space Using Experimental Hyperspectral Imagery

Advancing Floating Macroplastic Detection from Space Using Experimental Hyperspectral Imagery

A novel ground truth multispectral image dataset with weight, anthocyanins, and Brix index measures of grape berries tested for its utility in machine learning pipelines

Advancing floating macroplastic detection from space using hyperspectral imagery

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