1.Key Laboratory of Infrared System Detection and Imaging Technology， Chinese Academy of Sciences， Shanghai 200083， China;2.University of Chinese Academy of Sciences， Beijing 100049， China;3.Shanghai Institute of Technical Physics， Chinese Academy of Sciences， Shanghai 200083， China
Supported by Key Laboratory of Shanghai Field Physical Evidence Foundation (2017xcwzk08)
Aiming at the problem of insufficient samples of infrared aircrafts and low accuracy of fine-grained classification， a method of infrared aircraft few-shot classification based on meta learning is proposed. Based on meta learning and combined with multi-scale feature fusion， this method can effectively extract commonness among different classification tasks while reducing computation， and then classify different tasks with fine-tuning. The experiments proved that this method could improve the classification accuracy of mini-ImageNet dataset while reducing the calculation amount by about 70%. The accuracy of fine-grained classification for infrared aircrafts with few samples reached 92.74%.
CHEN Rui-Min, LIU Shi-Jian, MIAO Zhuang, LI Fan-Ming. Infrared aircraft few-shot classification method based on meta learning[J]. Journal of Infrared and Millimeter Waves,2021,40(4):554~560Copy