Ice Detection on Aircraft Surface Using Machine Learning Approaches Based on Hyperspectral and Multispectral Images
Aircraft ground de-icing operations play a critical role in flight safety. However, to handle the aircraft de-icing, a considerable quantity of de-icing fluids is commonly employed. Moreover, some pre-flight inspections are carried out with engines running; thus, a large amount of fuel is wasted, an...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-08-01
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| Series: | Drones |
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| Online Access: | https://www.mdpi.com/2504-446X/4/3/45 |
| _version_ | 1797557306442907648 |
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| author | Maria Angela Musci Luigi Mazzara Andrea Maria Lingua |
| author_facet | Maria Angela Musci Luigi Mazzara Andrea Maria Lingua |
| author_sort | Maria Angela Musci |
| collection | DOAJ |
| description | Aircraft ground de-icing operations play a critical role in flight safety. However, to handle the aircraft de-icing, a considerable quantity of de-icing fluids is commonly employed. Moreover, some pre-flight inspections are carried out with engines running; thus, a large amount of fuel is wasted, and CO<sub>2</sub> is emitted. This implies substantial economic and environmental impacts. In this context, the European project (reference call: MANUNET III 2018, project code: MNET18/ICT-3438) called SEI (Spectral Evidence of Ice) aims to provide innovative tools to identify the ice on aircraft and improve the efficiency of the de-icing process. The project includes the design of a low-cost UAV (uncrewed aerial vehicle) platform and the development of a quasi-real-time ice detection methodology to ensure a faster and semi-automatic activity with a reduction of applied operating time and de-icing fluids. The purpose of this work, developed within the activities of the project, is defining and testing the most suitable sensor using a radiometric approach and machine learning algorithms. The adopted methodology consists of classifying ice through spectral imagery collected by two different sensors: multispectral and hyperspectral camera. Since the UAV prototype is under construction, the experimental analysis was performed with a simulation dataset acquired on the ground. The comparison among the two approaches, and their related algorithms (random forest and support vector machine) for image processing, was presented: practical results show that it is possible to identify the ice in both cases. Nonetheless, the hyperspectral camera guarantees a more reliable solution reaching a higher level of accuracy of classified iced surfaces. |
| first_indexed | 2024-03-10T17:15:17Z |
| format | Article |
| id | doaj.art-74d8c37af75f4f0dbe1d0ad54fefaf13 |
| institution | Directory Open Access Journal |
| issn | 2504-446X |
| language | English |
| last_indexed | 2024-03-10T17:15:17Z |
| publishDate | 2020-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj.art-74d8c37af75f4f0dbe1d0ad54fefaf132023-11-20T10:31:13ZengMDPI AGDrones2504-446X2020-08-01434510.3390/drones4030045Ice Detection on Aircraft Surface Using Machine Learning Approaches Based on Hyperspectral and Multispectral ImagesMaria Angela Musci0Luigi Mazzara1Andrea Maria Lingua2DIATI, Department of Environment, Land and Infrastructure Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, ItalyDIATI, Department of Environment, Land and Infrastructure Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, ItalyDIATI, Department of Environment, Land and Infrastructure Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, ItalyAircraft ground de-icing operations play a critical role in flight safety. However, to handle the aircraft de-icing, a considerable quantity of de-icing fluids is commonly employed. Moreover, some pre-flight inspections are carried out with engines running; thus, a large amount of fuel is wasted, and CO<sub>2</sub> is emitted. This implies substantial economic and environmental impacts. In this context, the European project (reference call: MANUNET III 2018, project code: MNET18/ICT-3438) called SEI (Spectral Evidence of Ice) aims to provide innovative tools to identify the ice on aircraft and improve the efficiency of the de-icing process. The project includes the design of a low-cost UAV (uncrewed aerial vehicle) platform and the development of a quasi-real-time ice detection methodology to ensure a faster and semi-automatic activity with a reduction of applied operating time and de-icing fluids. The purpose of this work, developed within the activities of the project, is defining and testing the most suitable sensor using a radiometric approach and machine learning algorithms. The adopted methodology consists of classifying ice through spectral imagery collected by two different sensors: multispectral and hyperspectral camera. Since the UAV prototype is under construction, the experimental analysis was performed with a simulation dataset acquired on the ground. The comparison among the two approaches, and their related algorithms (random forest and support vector machine) for image processing, was presented: practical results show that it is possible to identify the ice in both cases. Nonetheless, the hyperspectral camera guarantees a more reliable solution reaching a higher level of accuracy of classified iced surfaces.https://www.mdpi.com/2504-446X/4/3/45hyperspectral imagesmultispectral datamachine learningice detection |
| spellingShingle | Maria Angela Musci Luigi Mazzara Andrea Maria Lingua Ice Detection on Aircraft Surface Using Machine Learning Approaches Based on Hyperspectral and Multispectral Images Drones hyperspectral images multispectral data machine learning ice detection |
| title | Ice Detection on Aircraft Surface Using Machine Learning Approaches Based on Hyperspectral and Multispectral Images |
| title_full | Ice Detection on Aircraft Surface Using Machine Learning Approaches Based on Hyperspectral and Multispectral Images |
| title_fullStr | Ice Detection on Aircraft Surface Using Machine Learning Approaches Based on Hyperspectral and Multispectral Images |
| title_full_unstemmed | Ice Detection on Aircraft Surface Using Machine Learning Approaches Based on Hyperspectral and Multispectral Images |
| title_short | Ice Detection on Aircraft Surface Using Machine Learning Approaches Based on Hyperspectral and Multispectral Images |
| title_sort | ice detection on aircraft surface using machine learning approaches based on hyperspectral and multispectral images |
| topic | hyperspectral images multispectral data machine learning ice detection |
| url | https://www.mdpi.com/2504-446X/4/3/45 |
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