Bi-Similarity Prototypical Network with Capsule-Based Embedding for Few-Shot SAR Target Recognition
Sen Liu, Xuelian Yu, Haohao Ren, Lin Zou, Yun Zhou, Xuegang Wang
Abstract
This paper proposes a Bi -similarity prototypical network with capsule-based embedding to solve the problem of few-shot SAR target recognition. The proposed method comprises two procedures, i.e., feature embedding module and Bi-similarity reasoning module. Specifically, we build a feature embed-ding network with capsule operation, which can enable a feature embedding network to extract more informative features by effectively encoding relative spatial relationships between features. To reason the identity of target robustly, we develop a reasoning module based on Bi-similarity metric. Moreover, a mixed loss is proposed to train a discriminative representation space with both intra-class aggregation and inter-class separation. Experimental results on moving and stationary target acquisition and recognition (MSTAR) dataset show that the proposed method is effective and superior to some state-of-art methods in few-shot SAR target recognition tasks.