High-Resolution Aerial Detection of Marine Plastic Litter by Hyperspectral Sensing
An automatic custom-made procedure is developed to identify macroplastic debris loads in coastal and marine environment, through hyperspectral imaging from unmanned aerial vehicles (UAVs). Results obtained during a remote-sensing field campaign carried out in the seashore of Sassari (Sardinia, Italy...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-04-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/13/8/1557 |
| _version_ | 1827694847254855680 |
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| author | Marco Balsi Monica Moroni Valter Chiarabini Giovanni Tanda |
| author_facet | Marco Balsi Monica Moroni Valter Chiarabini Giovanni Tanda |
| author_sort | Marco Balsi |
| collection | DOAJ |
| description | An automatic custom-made procedure is developed to identify macroplastic debris loads in coastal and marine environment, through hyperspectral imaging from unmanned aerial vehicles (UAVs). Results obtained during a remote-sensing field campaign carried out in the seashore of Sassari (Sardinia, Italy) are presented. A push-broom-sensor-based spectral device, carried onboard a DJI Matrice 600 drone, was employed for the acquisition of spectral data in the range 900−1700 nm. The hyperspectral platform was realized by assembling commercial devices, whereas algorithms for mosaicking, post-flight georeferencing, and orthorectification of the acquired images were developed in-house. Generation of the hyperspectral cube was based on mosaicking visible-spectrum images acquired synchronously with the hyperspectral lines, by performing correlation-based registration and applying the same translations, rotations, and scale changes to the hyperspectral data. Plastics detection was based on statistically relevant feature selection and Linear Discriminant Analysis, trained on a manually labeled sample. The results obtained from the inspection of either the beach site or the sea water facing the beach clearly show the successful separate identification of polyethylene (PE) and polyethylene terephthalate (PET) objects through the post-processing data treatment based on the developed classifier algorithm. As a further implementation of the procedure described, direct real-time processing, by an embedded computer carried onboard the drone, permitted the immediate plastics identification (and visual inspection in synchronized images) during the UAV survey, as documented by short video sequences provided in this research paper. |
| first_indexed | 2024-03-10T12:14:28Z |
| format | Article |
| id | doaj.art-76a384f6eedb4bdc8f93bafa85f672c0 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T12:14:28Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-76a384f6eedb4bdc8f93bafa85f672c02023-11-21T15:57:57ZengMDPI AGRemote Sensing2072-42922021-04-01138155710.3390/rs13081557High-Resolution Aerial Detection of Marine Plastic Litter by Hyperspectral SensingMarco Balsi0Monica Moroni1Valter Chiarabini2Giovanni Tanda3Dipartimento di Ingegneria dell’Informazione, Elettronica e Telecomunicazioni (DIET), Università di Roma La Sapienza, 00184 Rome, ItalyDipartimento di Ingegneria Civile, Edile e Ambientale (DICEA), Università di Roma La Sapienza, 00184 Rome, ItalyKIM-RemoteSensing GmbH, 9020 Klagenfurt am Wörthersee, AustriaDipartimento di Ingegneria Meccanica, Energetica, Gestionale e dei Trasporti (DIME), Università Degli Studi di Genova, 16145 Genoa, ItalyAn automatic custom-made procedure is developed to identify macroplastic debris loads in coastal and marine environment, through hyperspectral imaging from unmanned aerial vehicles (UAVs). Results obtained during a remote-sensing field campaign carried out in the seashore of Sassari (Sardinia, Italy) are presented. A push-broom-sensor-based spectral device, carried onboard a DJI Matrice 600 drone, was employed for the acquisition of spectral data in the range 900−1700 nm. The hyperspectral platform was realized by assembling commercial devices, whereas algorithms for mosaicking, post-flight georeferencing, and orthorectification of the acquired images were developed in-house. Generation of the hyperspectral cube was based on mosaicking visible-spectrum images acquired synchronously with the hyperspectral lines, by performing correlation-based registration and applying the same translations, rotations, and scale changes to the hyperspectral data. Plastics detection was based on statistically relevant feature selection and Linear Discriminant Analysis, trained on a manually labeled sample. The results obtained from the inspection of either the beach site or the sea water facing the beach clearly show the successful separate identification of polyethylene (PE) and polyethylene terephthalate (PET) objects through the post-processing data treatment based on the developed classifier algorithm. As a further implementation of the procedure described, direct real-time processing, by an embedded computer carried onboard the drone, permitted the immediate plastics identification (and visual inspection in synchronized images) during the UAV survey, as documented by short video sequences provided in this research paper.https://www.mdpi.com/2072-4292/13/8/1557hyperspectralplasticsremote sensingmarine litterreflectanceUAV-based technique |
| spellingShingle | Marco Balsi Monica Moroni Valter Chiarabini Giovanni Tanda High-Resolution Aerial Detection of Marine Plastic Litter by Hyperspectral Sensing Remote Sensing hyperspectral plastics remote sensing marine litter reflectance UAV-based technique |
| title | High-Resolution Aerial Detection of Marine Plastic Litter by Hyperspectral Sensing |
| title_full | High-Resolution Aerial Detection of Marine Plastic Litter by Hyperspectral Sensing |
| title_fullStr | High-Resolution Aerial Detection of Marine Plastic Litter by Hyperspectral Sensing |
| title_full_unstemmed | High-Resolution Aerial Detection of Marine Plastic Litter by Hyperspectral Sensing |
| title_short | High-Resolution Aerial Detection of Marine Plastic Litter by Hyperspectral Sensing |
| title_sort | high resolution aerial detection of marine plastic litter by hyperspectral sensing |
| topic | hyperspectral plastics remote sensing marine litter reflectance UAV-based technique |
| url | https://www.mdpi.com/2072-4292/13/8/1557 |
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