Hybrid quantum transfer learning for supervised UAV aerial image classification

Unmanned Aerial Vehicles (UAVs) enable high-resolution remote-sensing for land-cover and object mapping, yet conventional deep learning (DL) models often remain sensitive to scale, viewpoint, and illumination variability in UAV imagery. This paper presents QTL-SAIC, a quantum transfer learning framework for supervised aerial image classification that integrates classical feature learning with quantum-enhanced decision making. A pre-trained Capsule Network (CapsNet) extracts compact, discriminative features, which are classified using a variational quantum neural network (QNN) within a hybrid quantum–classical pipeline. The quantum module is fully specified (qubit number, encoding, circuit architecture, measurements, optimization, and execution backend with shot settings) to support reproducibility. Hyperparameters are optimized via the Gannet Optimization Algorithm (GOA), and evaluation follows stratified splits, repeated multi-seed trials and statistical reporting, with class imbalance addressed through cost-sensitive and/or balanced sampling strategies. Results indicate that QTL-SAIC yields competitive and stable performance relative to resource-matched classical baselines. Although no formal quantum advantage is claimed, ablations on circuit depth and noise sensitivity delineate the conditions under which the quantum component contributes to predictive performance.