A Study on the Monitoring of Floating Marine Macro-Litter Using a Multi-Spectral Sensor and Classification Based on Deep Learning
Increasing global plastic usage has raised critical concerns regarding marine pollution. This study addresses the pressing issue of floating marine macro-litter (FMML) by developing a novel monitoring system using a multi-spectral sensor and drones along the southern coast of South Korea. Subsequent...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/23/4347 |
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| author | Youchul Jeong Jisun Shin Jong-Seok Lee Ji-Yeon Baek Daniel Schläpfer Sin-Young Kim Jin-Yong Jeong Young-Heon Jo |
| author_facet | Youchul Jeong Jisun Shin Jong-Seok Lee Ji-Yeon Baek Daniel Schläpfer Sin-Young Kim Jin-Yong Jeong Young-Heon Jo |
| author_sort | Youchul Jeong |
| collection | DOAJ |
| description | Increasing global plastic usage has raised critical concerns regarding marine pollution. This study addresses the pressing issue of floating marine macro-litter (FMML) by developing a novel monitoring system using a multi-spectral sensor and drones along the southern coast of South Korea. Subsequently, a convolutional neural network (CNN) model was utilized to classify four distinct marine litter materials: film, fiber, fragment, and foam. Automatic atmospheric correction with the drone data atmospheric correction (DROACOR) method, which is specifically designed for currently available drone-based sensors, ensured consistent reflectance across altitudes in the FMML dataset. The CNN models exhibited promising performance, with precision, recall, and F1 score values of 0.9, 0.88, and 0.89, respectively. Furthermore, gradient-weighted class activation mapping (Grad-CAM), an object recognition technique, allowed us to interpret the classification performance. Overall, this study will shed light on successful FMML identification using multi-spectral observations for broader applications in diverse marine environments. |
| format | Article |
| id | doaj-art-b6906fab00124a069f86c0733c3d7d30 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-b6906fab00124a069f86c0733c3d7d302024-12-13T16:30:30ZengMDPI AGRemote Sensing2072-42922024-11-011623434710.3390/rs16234347A Study on the Monitoring of Floating Marine Macro-Litter Using a Multi-Spectral Sensor and Classification Based on Deep LearningYouchul Jeong0Jisun Shin1Jong-Seok Lee2Ji-Yeon Baek3Daniel Schläpfer4Sin-Young Kim5Jin-Yong Jeong6Young-Heon Jo7Marine Research Institute, Pusan National University, Busan 46241, Republic of KoreaMarine Research Institute, Pusan National University, Busan 46241, Republic of KoreaBK21 School of Earth and Environmental Systems, Pusan National University, Busan 46241, Republic of KoreaBK21 School of Earth and Environmental Systems, Pusan National University, Busan 46241, Republic of KoreaReSe Applications LLC, 9500 Wil, SwitzerlandBK21 School of Earth and Environmental Systems, Pusan National University, Busan 46241, Republic of KoreaMarine Disaster Research Department, Korea Institute of Ocean Science and Technology, Busan 49111, Republic of KoreaMarine Research Institute, Pusan National University, Busan 46241, Republic of KoreaIncreasing global plastic usage has raised critical concerns regarding marine pollution. This study addresses the pressing issue of floating marine macro-litter (FMML) by developing a novel monitoring system using a multi-spectral sensor and drones along the southern coast of South Korea. Subsequently, a convolutional neural network (CNN) model was utilized to classify four distinct marine litter materials: film, fiber, fragment, and foam. Automatic atmospheric correction with the drone data atmospheric correction (DROACOR) method, which is specifically designed for currently available drone-based sensors, ensured consistent reflectance across altitudes in the FMML dataset. The CNN models exhibited promising performance, with precision, recall, and F1 score values of 0.9, 0.88, and 0.89, respectively. Furthermore, gradient-weighted class activation mapping (Grad-CAM), an object recognition technique, allowed us to interpret the classification performance. Overall, this study will shed light on successful FMML identification using multi-spectral observations for broader applications in diverse marine environments.https://www.mdpi.com/2072-4292/16/23/4347floating marine macro-litterunmanned aerial vehiclemulti-spectral sensoratmospheric correctionreflectance retrievalconvolutional neural network |
| spellingShingle | Youchul Jeong Jisun Shin Jong-Seok Lee Ji-Yeon Baek Daniel Schläpfer Sin-Young Kim Jin-Yong Jeong Young-Heon Jo A Study on the Monitoring of Floating Marine Macro-Litter Using a Multi-Spectral Sensor and Classification Based on Deep Learning Remote Sensing floating marine macro-litter unmanned aerial vehicle multi-spectral sensor atmospheric correction reflectance retrieval convolutional neural network |
| title | A Study on the Monitoring of Floating Marine Macro-Litter Using a Multi-Spectral Sensor and Classification Based on Deep Learning |
| title_full | A Study on the Monitoring of Floating Marine Macro-Litter Using a Multi-Spectral Sensor and Classification Based on Deep Learning |
| title_fullStr | A Study on the Monitoring of Floating Marine Macro-Litter Using a Multi-Spectral Sensor and Classification Based on Deep Learning |
| title_full_unstemmed | A Study on the Monitoring of Floating Marine Macro-Litter Using a Multi-Spectral Sensor and Classification Based on Deep Learning |
| title_short | A Study on the Monitoring of Floating Marine Macro-Litter Using a Multi-Spectral Sensor and Classification Based on Deep Learning |
| title_sort | study on the monitoring of floating marine macro litter using a multi spectral sensor and classification based on deep learning |
| topic | floating marine macro-litter unmanned aerial vehicle multi-spectral sensor atmospheric correction reflectance retrieval convolutional neural network |
| url | https://www.mdpi.com/2072-4292/16/23/4347 |
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