Plastic Litter Detection in the Environment Using Hyperspectral Aerial Remote Sensing and Machine Learning
Plastic waste has become a critical environmental issue, necessitating effective methods for detection and monitoring. This article presents a machine-learning-based methodology and embedded solution to detect plastic waste in the environment using an airborne hyperspectral sensor operating in the s...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/5/938 |
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| author | Marco Balsi Monica Moroni Soufyane Bouchelaghem |
| author_facet | Marco Balsi Monica Moroni Soufyane Bouchelaghem |
| author_sort | Marco Balsi |
| collection | DOAJ |
| description | Plastic waste has become a critical environmental issue, necessitating effective methods for detection and monitoring. This article presents a machine-learning-based methodology and embedded solution to detect plastic waste in the environment using an airborne hyperspectral sensor operating in the short-wave infrared (SWIR) band. Experimental data were obtained from drone flights in several case studies in natural and controlled environments. Data were preprocessed to simply equalize the spectra across the whole band and across different environmental conditions, and machine learning techniques were applied to detect plastics even in real-time. Several algorithms for spectrum calibration, feature selection, and classification were optimized and compared to obtain an optimal solution that has high-quality results under cross-validation. This way, deploying the system in different environments without requiring complicated manual adjustments or re-learning is possible. The results of this work prove the feasibility of the proposed plastic litter detection approach using high-definition aerial remote sensing, with high specificity to plastic polymers that are not obtained using visible and NIR data. |
| format | Article |
| id | doaj-art-ec51f74189234e8a93b6ee83f6b6cd53 |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-ec51f74189234e8a93b6ee83f6b6cd532025-08-20T02:06:13ZengMDPI AGRemote Sensing2072-42922025-03-0117593810.3390/rs17050938Plastic Litter Detection in the Environment Using Hyperspectral Aerial Remote Sensing and Machine LearningMarco Balsi0Monica Moroni1Soufyane Bouchelaghem2Department of Information Engineering, Electronics and Telecommunications (DIET), Sapienza University of Rome, 00184 Rome, ItalyDepartment of Civil, Building, and Environmental Engineering (DICEA), Sapienza University of Rome, 00184 Rome, ItalyDepartment of Information Engineering, Electronics and Telecommunications (DIET), Sapienza University of Rome, 00184 Rome, ItalyPlastic waste has become a critical environmental issue, necessitating effective methods for detection and monitoring. This article presents a machine-learning-based methodology and embedded solution to detect plastic waste in the environment using an airborne hyperspectral sensor operating in the short-wave infrared (SWIR) band. Experimental data were obtained from drone flights in several case studies in natural and controlled environments. Data were preprocessed to simply equalize the spectra across the whole band and across different environmental conditions, and machine learning techniques were applied to detect plastics even in real-time. Several algorithms for spectrum calibration, feature selection, and classification were optimized and compared to obtain an optimal solution that has high-quality results under cross-validation. This way, deploying the system in different environments without requiring complicated manual adjustments or re-learning is possible. The results of this work prove the feasibility of the proposed plastic litter detection approach using high-definition aerial remote sensing, with high specificity to plastic polymers that are not obtained using visible and NIR data.https://www.mdpi.com/2072-4292/17/5/938dronesenvironmental monitoringhyperspectral sensorsplastic waste |
| spellingShingle | Marco Balsi Monica Moroni Soufyane Bouchelaghem Plastic Litter Detection in the Environment Using Hyperspectral Aerial Remote Sensing and Machine Learning Remote Sensing drones environmental monitoring hyperspectral sensors plastic waste |
| title | Plastic Litter Detection in the Environment Using Hyperspectral Aerial Remote Sensing and Machine Learning |
| title_full | Plastic Litter Detection in the Environment Using Hyperspectral Aerial Remote Sensing and Machine Learning |
| title_fullStr | Plastic Litter Detection in the Environment Using Hyperspectral Aerial Remote Sensing and Machine Learning |
| title_full_unstemmed | Plastic Litter Detection in the Environment Using Hyperspectral Aerial Remote Sensing and Machine Learning |
| title_short | Plastic Litter Detection in the Environment Using Hyperspectral Aerial Remote Sensing and Machine Learning |
| title_sort | plastic litter detection in the environment using hyperspectral aerial remote sensing and machine learning |
| topic | drones environmental monitoring hyperspectral sensors plastic waste |
| url | https://www.mdpi.com/2072-4292/17/5/938 |
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