This paper presents a comparative study of the Mamba YOLO model and recent YOLO variants (YOLOv8, YOLOv9, YOLOv10) for aircraft detection in satellite images, which remains a subject of interest for security and aviation experts. In this study, all aforementioned YOLO models were trained on the HRPlanesv2 dataset, containing 2120 high-resolution Google Earth images with 14,335 aircraft annotations. While Mamba YOLO shows good feature extraction and parameter efficiency, it underperforms in mAP compared to YOLOv8-10 and requires more training time due to its complex structure. However, it addresses network bottlenecks and opens up potential for future enhancements. The current work highlights the strengths and limitations of Mamba YOLO, pointing out areas for improvement in real-time satellite object detection.