A Deep Learning Approach to Aircraft Detection and Classification in Satellite Imagery Using YOLOv8
Ramesh Kumar Panneerselvam, Sarada Bandi, Sree Datta D · 2025
Identification of civilian aircraft in high resolution satellite imagery is very important for air traffic management, infrastructure planning, and security applications. This paper presents a specific model for identification and classification of civilian aircraft utilizing YOLOv8 and the FAIR1M-2.0 dataset.The dataset was reduced to 11 different aircraft types for achieving high classification accuracy. The annotations are transformed into YOLO format for increased compatibility, and a YOLOv8 Nano model is employed for real-time detection with optimized computational efficiency.To improve the model’s generalizability, the training pipeline uses a multi-component loss function and data augmentation approaches. In the inference stage, the model detects objects, classifies them, and identifies aircraft, all of which are enhanced using the Non-Maximum Suppression technique. The results demonstrate that the suggested model allows quick real-time processing and has excellent classification accuracy, which advances aerial image analysis for tracking civilian aircraft and other related applications.