  Convolutional neural networks (CNNs) have dominated the synthetic aperture
radar (SAR) automatic target recognition (ATR) for years. However, under the
limited SAR images, the width and depth of the CNN-based models are limited,
and the widening of the received field for global features in images is
hindered, which finally leads to the low performance of recognition. To address
these challenges, we propose a Convolutional Transformer (ConvT) for SAR ATR
few-shot learning (FSL). The proposed method focuses on constructing a
hierarchical feature representation and capturing global dependencies of local
features in each layer, named global in local. A novel hybrid loss is proposed
to interpret the few SAR images in the forms of recognition labels and
contrastive image pairs, construct abundant anchor-positive and anchor-negative
image pairs in one batch and provide sufficient loss for the optimization of
the ConvT to overcome the few sample effect. An auto augmentation is proposed
to enhance and enrich the diversity and amount of the few training samples to
explore the hidden feature in a few SAR images and avoid the over-fitting in
SAR ATR FSL. Experiments conducted on the Moving and Stationary Target
Acquisition and Recognition dataset (MSTAR) have shown the effectiveness of our
proposed ConvT for SAR ATR FSL. Different from existing SAR ATR FSL methods
employing additional training datasets, our method achieved pioneering
performance without other SAR target images in training.
