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Ice Detection on Aircraft Surface Using Machine Learning Approaches Based on Hyperspectral and Multispectral Images
Content Provider | MDPI |
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Author | Musci, Maria Angela Mazzara, Luigi Lingua, Andrea Maria |
Copyright Year | 2020 |
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 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. |
Starting Page | 45 |
e-ISSN | 2504446X |
DOI | 10.3390/drones4030045 |
Journal | Drones |
Issue Number | 3 |
Volume Number | 4 |
Language | English |
Publisher | MDPI |
Publisher Date | 2020-08-18 |
Access Restriction | Open |
Subject Keyword | Drones Remote Sensing Hyperspectral Images Multispectral Data Machine Learning Ice Detection |
Content Type | Text |
Resource Type | Article |