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Conference Paper

Function Assignment of Plastics based on Hyperspectral Satellite Images and High-Resolution Data Using Deep Learning Algorithms

Authors
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Zhou,  Shanyu
1.4 Remote Sensing, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

Mou,  Lichao
External Organizations;

Zhang,  Lixian
External Organizations;

Hua,  Yuansheng
External Organizations;

/persons/resource/charlyk

Kaufmann,  Hermann
1.4 Remote Sensing, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

Zhu,  Xiaoxiang
External Organizations;

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Citation

Zhou, S., Mou, L., Zhang, L., Hua, Y., Kaufmann, H., Zhu, X. (2023): Function Assignment of Plastics based on Hyperspectral Satellite Images and High-Resolution Data Using Deep Learning Algorithms - Proceedings, IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium (Pasadena, CA, USA 2023).
https://doi.org/10.1109/IGARSS52108.2023.10283116


Cite as: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5025740
Abstract
Plastic pollution is becoming an increasingly prominent problem and the function of plastics determines whether they need to be recycled or not. In order to explore the possibility of using satellite imagery to classify the functionality of plastics, this study proposes a two-stage workflow: firstly, a classification map is obtained based on hyperspectral satellite imagery to generate plastic types, and then using these identified plastic coverage areas, a deep learning algorithm is used to assign functionality to these classified plastic areas based on sentinel-2 imagery. By comparing five leading-edge image classification models, classification accuracies of up to 74% were achieved, demonstrating the feasibility of using deep learning models trained on satellite images to identify plastic features.