Keypoint Detection-Based Aircraft Fine-Grained Recognition for High-Resolution Optical Remote Sensing Images

Humans are capable of identifying aircraft based on quantitative features such as aspect ratio, engine count, wingspan, and structural configuration. Inspired by this, a keypoint-based aircraft identification approach is proposed to address the challenge of fine-grained aircraft recognition in high-resolution remote sensing images. First, a dataset of aircraft labeled with keypoints is built, in which aircraft are reclassified into types according to the similarity of keypoint distributions to improve extraction stability and versatility. Then, a keypoint extraction method with topological constraints is proposed, leveraging the nadir imaging characteristics of remote sensing and accounting for the relationships among keypoints. Subsequently, distinctive quantitative features for identification are selected through representativeness and effectiveness analyses for the following matching algorithm. Finally, a comprehensive template matching-based identification strategy is proposed to recognize targets based on quantitative descriptions derived from keypoints. This novel solution achieves significantly more accurate identification than traditional regression–classification approaches, improving recognition accuracy by over 3% on average. Moreover, the method extends aircraft identification capabilities from closed-set to open-set recognition, demonstrating substantial value for the precise interpretation of aircraft targets in high-resolution optical imagery.