A Reinforcement One-shot Active Learning Approach for Aircraft Type Recognition

Target recognition is an important aspect of air traffic management, but the study on automatic aircraft identification is still in the exploratory stage. Rapid aircraft processing and accurate aircraft type recognition remain challenging tasks due to the high-speed movement of the aircraft against complex backgrounds. Active learning, as a promising research topic of machine learning in recent decades, can use less labeled data to obtain the same model accuracy as supervised learning, which greatly reduces the cost of labeling a dataset. Instead of manually developing policies of accessing the labels of desired instances, an improved active learning approach, which can not only learn to classify samples using small supervision but additionally capture a relatively optimal label query strategy, was developed by employing the reinforcement learning in the process of decision-making. The proposed model was first tested with the Amsterdam Library of Object Images (ALOI) dataset and then used to perform aircraft type recognition on one-month real-world flight track data. Our method offers a satisfactory solution for learning new concepts rapidly from a small amount of data, which well meets the needs of aircraft type recognition task in practical application.