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Impact of water vapour on polymer classification using in situ short-wave infrared hyperspectral imaging
Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.
Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.
2022 (English)In: Journal of Spectral Imaging, E-ISSN 2040-4565, Vol. 11, article id a5Article in journal (Refereed) Published
Abstract [en]

Hyperspectral remote sensing is known to suffer from wavelength bands blocked by atmospheric gases. Short-wave infrared hyperspectral imaging at in situ installations is shown to be affected by water vapour even if the pathlength of light through air is only hundreds of centimetres. This impact is especially noticeable with large variations of relative humidity, the coefficient of variation reaching 5 % in our test case. Using repeated calibrations of imaging system at the same relative humidity as in the measurement, we were able to reduce the coefficient of variation to 1 %. The measurement variations are also shown to induce significant error in material classification. Polymer type identification was selected as the test case for material classification. The measurement variations due to the change in relative humidity are shown to result in 20 % classification error at its minimum. With repeated calibrations or by eliminating the most affected wavelength bands from measurements, we were able to reduce the classification error to less than 1 %. Such improvement of measurement and classification precision may be important for industrial applications such as waste sorting, polymer classification etc. 

Place, publisher, year, edition, pages
2022. Vol. 11, article id a5
Keywords [en]
calibration, humidity, hyperspectral imaging, infrared, InGaAs, material classification, plastic detection, waste sorting
National Category
Atom and Molecular Physics and Optics
Identifiers
URN: urn:nbn:se:miun:diva-45755DOI: 10.1255/jsi.2022.a5Scopus ID: 2-s2.0-85134844293OAI: oai:DiVA.org:miun-45755DiVA, id: diva2:1685498
Available from: 2022-08-03 Created: 2022-08-03 Last updated: 2023-12-31Bibliographically approved
In thesis
1. Hyperspectral imaging for in-situ applications: Methods to improve the classification of materials using hyperspectral imaging
Open this publication in new window or tab >>Hyperspectral imaging for in-situ applications: Methods to improve the classification of materials using hyperspectral imaging
2024 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis addresses several research questions related to in-situ hyperspectral imaging systems, proposes measurement methods for more accurate imaging, and examines the impact of the methods on material classification.

First, the thesis investigates the possibility of successfully calibrating a hyperspectral imaging system using a low-cost PTFE reference. A hyperspectral imaging system and practical calibration procedure using an inexpensive calibration reference are introduced. This reference enables accurate measurement of a material’s reflectance spectra independent of lighting and the camera’s spectral distribution of intensity and sensitivity. The study presents experiments conducted on winter roads covered with water, snow, and ice. The results show the robustness of the calibration and the suitability of the system for classifying materials.

The thesis further focuses on increasing the dynamic range (DR) of line scanning hyperspectral cameras. A method that relies on the use of multiple exposures is proposed to increase DR, benefiting applications such as plastic detection and polymer sorting. Experiments show that the proposed method can increase the DR for hyperspectral SWIR imaging from 43 dB to 73 dB. Material classification experiments reveal significant accuracy improvements with multiple exposures for large dynamic ranges.

The thesis also examines the effect of variations in relative humidity. It shows that even minor changes in humidity can significantly affect measurements. Frequent calibration and pruning of active wavelength bands are proposed as solutions to reduce the classification error rate for polymers from 20% to less than 1%.

The thesis also investigates the classification of colored materials by combining visible and infrared imaging. The classification algorithm shows high overall accuracy, close to 99.9% for one test case, which also shows the potential of this approach.

Finally, the use of infrared hyperspectral imaging combined with Convolutional Neural Networks (CNN) for the classification of black polymers is evaluated. CNN outperforms all traditional classification algorithms, further demonstrating the potential of the proposed method. Further research on larger and more diversified material samples is recommended.

Place, publisher, year, edition, pages
Sundsvall: Mid Sweden University, 2024. p. 61
Series
Mid Sweden University doctoral thesis, ISSN 1652-893X ; 403
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:miun:diva-50202 (URN)978-91-89786-49-3 (ISBN)
Public defence
2024-01-25, O102, Holmgatan 10, Sundsvall, 09:00 (English)
Opponent
Supervisors
Note

Vid tidpunkten för disputationen var följande delarbeten opublicerade: delarbete 4 accepterat och delarbete 5 inskickat.

At the time of the doctoral defence the following papers were unpublished: paper 4 accepted and paper 5 submitted.

Available from: 2024-01-02 Created: 2023-12-31 Last updated: 2024-01-02Bibliographically approved

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Shaikh, Muhammad SaadThörnberg, Benny

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