Cross-Domain Machine Learning Approaches using Hyperspectral Imaging for Plastics litter detection-Reference-Cited by-同舟云学术

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Cross-Domain Machine Learning Approaches using Hyperspectral Imaging for Plastics litter detection

Published:2024-04-15 Issue: Volume: Page:36-40
ISSN:
Container-title:2024 IEEE Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (M2GARSS)
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Author:

Bouchelaghem Soufyane¹,Tibermacine Imad Eddine²,Balsi Marco¹,Moroni Monica³,Napoli Christian²

Affiliation:

1. Sapienza University of Rome,Elettronica e Telecomunicazioni (DIET),Dipartimento di Ingegneria Dell’Informazione,Rome,Italy

2. Sapienza University of Rome,Automation and Management Engineering (DIAG),Department of Computer,Rome,Italy

3. Sapienza University of Rome,Dipartimento di Ingegneria Civile Edile e Ambientale,Rome,Italy

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10537232/10537239/10537535.pdf?arnumber=10537535

Reference17 articles.

1. Production, use, and fate of all plastics ever made

2. Environmental and health hazard ranking and assessment of plastic polymers based on chemical composition

3. Baseline report on plastic waste;Tsakona,2020

4. Plastic classification via in-line hyperspectral camera analysis and unsupervised machine learning

5. Plastic Classification With NIR Hyperspectral Images and Deep Learning

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1. Hybrid models for direct normal irradiance forecasting: a case study of Ghardaia zone (Algeria);Natural Hazards;2024-07-22

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