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.
Ice Detection on Aircraft Surface Using Machine Learning Approaches Based on Hyperspectral and Multispectral Images / Musci, Maria Angela; Mazzara, Luigi; Lingua, Andrea Maria. - In: DRONES. - ISSN 2504-446X. - ELETTRONICO. - 4:3(2020), pp. 1-26.
|Titolo:||Ice Detection on Aircraft Surface Using Machine Learning Approaches Based on Hyperspectral and Multispectral Images|
|Data di pubblicazione:||2020|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.3390/drones4030045|
|Appare nelle tipologie:||1.1 Articolo in rivista|