FUNCTION ASSIGNMENT OF PLASTICS BASED ON HYPERSPECTRAL SATELLITE IMAGES AND HIGH-RESOLUTION DATA USING DEEP LEARNING ALGORITHMS

被引：0

作者：

Zhou, Shanyu ^{[1
,2
]}

Mou, Lichao ^{[1
]}

Zhang, Lixian ^{[3
]}

Hua, Yuansheng ^{[1
]}

Kaufmann, Hermann ^{[2
]}

Zhu, Xiaoxiang ^{[1
]}

机构：

[1] Tech Univ Munich, Data Sci Earth Observat, D-80333 Munich, Germany

[2] German Res Ctr Geosci GFZ, Remote Sensing & Geoinformat, D-14473 Potsdam, Germany

[3] Tsinghua Univ, Dept Earth Syst Sci, Minist Educ, Key Lab Earth Syst Modeling, Beijing 100084, Peoples R China

来源：

IGARSS 2023 - 2023 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM | 2023年

关键词：

Sentinel-2; Plastic detection; Deep learning; Image processing; Classification;

D O I：

10.1109/IGARSS52108.2023.10283116

中图分类号：

P [天文学、地球科学];

学科分类号：

07 ;

摘要：

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.

引用

页码：7257 / 7260

页数：4

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