In the contemporary defense industry and the realm of air traffic safety, the identification of aircraft on land and in the air is of paramount importance. Contemporary radar systems have the capacity to track aircraft; however, these systems are inherently dependent on human intervention, thereby introducing a heightened risk of undesirable events. Image processing techniques have emerged as a pivotal component in the detection of aircraft. Specifically, methodologies such as image classification, object detection, and segmentation facilitate the precise detection and tracking of aircraft. However, for direct detection, segmentation models and object detection methods must be employed. In this study, aircraft segmentation and detection were performed using satellite imagery, with the U-Net segmentation model and the YOLO object detection model being utilized. The dataset comprised a total of 103 satellite images, with each image containing one or more aircraft. Various performance metrics were obtained during the training and testing phases of the models. The highest validation IoU (Intersection over Union) of 61.3% and validation F1 score of 85.1% were reported from the U-Net segmentation model, while an F1 score of 79.8% and a mAP (mean average precision) of 77.7% were obtained from the YOLOv5-m object detection model.