Borrowing least squares analysis from spectral unmixing to classify plastics in SWIR hyperspectral images

Mehrübeoğlu, Mehrübe; Sickle, Austin Van; McLauchlan, Lifford

doi:10.1117/12.2584007

Cited by 5 publications

(8 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Floating plastic patches smaller than the pixel size of current satellite-acquired imagery can indeed be detected [16]. The latter can be realised by spectral unmixing techniques and algorithms [50,51]. A hyperspectral understanding of these mixed pixels could lead to new insights into highly sensitive wavelengths for the detection of floating plastic debris.…”

Section: Synthesis and Outlookmentioning

confidence: 99%

Advancing Floating Macroplastic Detection from Space Using Experimental Hyperspectral Imagery

et al. 2021

View full text Add to dashboard Cite

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.

show abstract

Section: Synthesis and Outlookmentioning

confidence: 99%

Advancing Floating Macroplastic Detection from Space Using Experimental Hyperspectral Imagery

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Based on the knowledge that macroplastics reflect light in a unique way compared to other floating litter and natural or anthropogenic background materials [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19], this study addressed two objectives. First, an understanding of the difference between lab-based and field-based hyperspectral imaging was made by comparing the associated hyperspectral signatures.…”

Section: Synthesis and Outlookmentioning

confidence: 99%

“…Developed by NASA, this library is widely used in estimating vegetation abundance and classifying mineral surfaces [49,50]. Including hyperspectral signatures of plastics as found in [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19] and this study in such reference libraries is essential. This can either be done as an addition to existing libraries, or by the establishment of a completely new open-access library specifically designed for plastics.…”

Section: Synthesis and Outlookmentioning

confidence: 99%

“…In their paper, Du et al [57] showed that the combination of SID (β) and SAM (α) improved the detection and classification of sample panels with different spectral signatures. They proposed and tested the SIDSAM score γ, calculated (7). In addition to using SID and SAM separately, the SIDSAM score is also applied to see whether it provides a more accurate classification of the hyperspectral images used in this study.…”

Section: Sam Sid and Sid-sam In Matlabmentioning

confidence: 99%

“…Recent studies have shown plastics are characterised by unique spectral reflectance signatures in the near infrared (NIR) to shortwave infrared (SWIR) part of the electromagnetic spectrum, especially in the 1100 -1700 nm range. Most of the studies focused on characterising the reflection signatures in controlled environments of virgin plastics [5,[7][8][9][10], marine or riverbank-harvested plastics [11][12][13], or a combination of virgin plastics and harvested plastics [14][15][16][17][18]. Only few experiments with hyperspectral imaging systems to detect macroplastics have been performed in aquatic environments [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Towards Robust River Plastic Detection: Combining Lab and Field-based Hyperspectral Imagery

Tasseron¹,

Schreyers²,

Peller³

et al. 2022

Preprint

View full text Add to dashboard Cite

Plastic pollution in aquatic ecosystems has increased dramatically in the last five decades, with strong impacts on human and aquatic life. Recent studies endorse the need for innovative approaches to monitor the presence, abundance, and types of plastic in these ecosystems. One approach gaining rapid traction is the use of multi- and hyperspectral cameras. However, most experiments using this approach have been conducted in controlled environments, making findings challenging to apply in natural environments. We present a method linking lab- and field-based identification of macroplastics using hyperspectral data (1150-1675 nm). Experiments using riverbank-harvested macroplastics were set up in (1) a laboratory environment, and (2) on the banks of the Rhine River. Representative pixel selections of eleven lab-based images (n = 786,264 pixels) and two field-based images (n = 40,289 pixels) were used to analyse the differences between these two environments. Next, classifier algorithms such as support vector machines (SVM), spectral angle mappers (SAM) and spectral information divergence (SID) were applied, because of their robustness to varying light conditions and high accuracies in mapping spectral similarities. Our results showed that SAM classifiers are most robust in separating plastic debris from natural or anthropogenic background elements. By applying lab-based data for plastic detection in field-based images, user accuracies for plastics to up to 93.6\% (n = 8,370 plastic pixels) were attained. This study provides key fundamental insights in linking lab-based data to plastic detection in the field. With this paper we aim to contribute to the development of future spectral missions to detect and monitor plastic pollution in aquatic ecosystems.

show abstract

Toward Robust River Plastic Detection: Combining Lab and Field‐Based Hyperspectral Imagery

Tasseron

Schreyers

Peller

et al. 2022

Earth and Space Science

View full text Add to dashboard Cite

Plastic pollution in aquatic ecosystems has increased dramatically in the last five decades, with strong impacts on human and aquatic life. Recent studies endorse the need for innovative approaches to monitor the presence, abundance, and types of plastic in these ecosystems. One approach gaining rapid traction is the use of multi‐ and hyperspectral cameras. However, most experiments using this approach were in controlled environments, making findings challenging to apply in natural environments. We present a method linking lab‐ and field‐based identification of macroplastics using hyperspectral data (1,150–1,675 nm). Experiments using riverbank‐harvested macroplastics were set up in a laboratory environment, and on the banks of the Rhine River. Representative pixel selections of eleven lab‐based images (n = 786,264 pixels) and two field‐based images (n = 40,289 pixels) were used to analyze the differences between these environments. Next, classifier algorithms such as support vector machines (SVM), spectral angle mapper (SAM) and spectral information divergence (SID) were applied, because of their robustness to varying light conditions and high accuracies in mapping spectral similarities. Our results showed that SAM classifiers are most robust in separating plastic pixels from background elements. By applying lab‐based data for plastic detection in field‐based images, user accuracies for plastics to up to 93.6% (n = 8,370 plastic pixels) were attained. This study provides key fundamental insights in linking lab‐based data to plastic detection in the field. With this paper we aim to contribute to the development of future spectral missions to detect and monitor plastic pollution in aquatic ecosystems.

show abstract

Borrowing least squares analysis from spectral unmixing to classify plastics in SWIR hyperspectral images

Cited by 5 publications

References 8 publications

Advancing Floating Macroplastic Detection from Space Using Experimental Hyperspectral Imagery

Advancing Floating Macroplastic Detection from Space Using Experimental Hyperspectral Imagery

Towards Robust River Plastic Detection: Combining Lab and Field-based Hyperspectral Imagery

Toward Robust River Plastic Detection: Combining Lab and Field‐Based Hyperspectral Imagery

Contact Info

Product

Resources

About