SAR Few-Shot Recognition based on Inner-Loop Update Optimization of Meta-Learning

At present, due to the limitations of the imaging environment and observation conditions, the automatic target recognition of synthetic aperture radar (SAR-ATR) encounters a severe shortage of target samples, which leads to poor recognition and unstable performance for few-shot targets. To address the above issues, this paper proposes an inner-loop parameter update method based on meta-adaptive hyper-parameter learning, called Mada-SGD, to achieve the goal of efficient recognition of few-shot SAR targets. In Mada-SGD, an adaptive hyper-parameter update strategy is introduced to automatically learn the initialization, weight factor, update factor and update direction in the meta-learner, it effectively solves the problem of parameter update in the meta-learning model and improves the fast adaptation of few-sample SAR targets. In addition, Mada-SGD learns the weight distribution information of initialization parameters by fully considering the correlation information between multi-step updates, which is similar to a memory mechanism and improves the feature extraction and representation ability of few-shot SAR targets. The experimental results on the customized MSTAR dataset show that the proposed Mada-SGD is able to achieve the state-of-the-art few-shot SAR target recognition performance, which verifies its effectiveness and reliability.