Recent developments in Quantum Computing have paved the way for an enhancement of computing capabilities. Quantum Machine Learning (QML) aims at developing Machine Learning (ML) models specifically designed for quantum computers. The availability of the first quantum processors enabled further research, in particular the exploration of possible practical applications of QML algorithms. This presentation will introduce two different quantum formulations of the Support Vector Machine algorithm. Their performance and limitations will be presented for a binary classification problem based on satellite images.