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Article

Ice Detection on Aircraft Surface Using Machine Learning Approaches Based on Hyperspectral and Multispectral Images

1
DIATI, Department of Environment, Land and Infrastructure Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
2
PIC4SeR, Politecnico Interdepartmental Centre for Service Robotics, 10129 Torino, Italy
*
Author to whom correspondence should be addressed.
Drones 2020, 4(3), 45; https://doi.org/10.3390/drones4030045
Received: 7 July 2020 / Revised: 6 August 2020 / Accepted: 14 August 2020 / Published: 18 August 2020
(This article belongs to the Collection Feature Papers of Drones)
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 CO2 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. View Full-Text
Keywords: hyperspectral images; multispectral data; machine learning; ice detection hyperspectral images; multispectral data; machine learning; ice detection
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MDPI and ACS Style

Musci, M.A.; Mazzara, L.; Lingua, A.M. Ice Detection on Aircraft Surface Using Machine Learning Approaches Based on Hyperspectral and Multispectral Images. Drones 2020, 4, 45. https://doi.org/10.3390/drones4030045

AMA Style

Musci MA, Mazzara L, Lingua AM. Ice Detection on Aircraft Surface Using Machine Learning Approaches Based on Hyperspectral and Multispectral Images. Drones. 2020; 4(3):45. https://doi.org/10.3390/drones4030045

Chicago/Turabian Style

Musci, Maria Angela, Luigi Mazzara, and Andrea Maria Lingua. 2020. "Ice Detection on Aircraft Surface Using Machine Learning Approaches Based on Hyperspectral and Multispectral Images" Drones 4, no. 3: 45. https://doi.org/10.3390/drones4030045

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