Multi-Task Representation Learning Network for Few-Shot Sar Automatic Target Recognition

Abstract

Deep learning-based automatic target recognition (ATR) methods can perform well with sufficient training samples, yet their performance will degrade significantly when the number of training samples available is quite small. Therefore, this paper proposes a multi-task representation learning network to achieve few-shot synthetic aperture radar (SAR) target recognition. On the one hand, the proposed network can perceive the input transformation, identity itself and class discrimination simultaneously. On the other hand, the proposed network can also extract the morphological features of the target and realize the feature refinement by channel attention mechanism. The powerful feature learning ability of the proposed network provides a guarantee for feature extraction under the condition of a few samples. Experiments on moving and stationary target acquisition and recognition (MSTAR) data set validate the effectiveness of the proposed network.