  In real-world scenarios, it may not always be possible to collect hundreds of
labeled samples per class for training deep learning-based SAR Automatic Target
Recognition (ATR) models. This work specifically tackles the few-shot SAR ATR
problem, where only a handful of labeled samples may be available to support
the task of interest. Our approach is composed of two stages. In the first, a
global representation model is trained via self-supervised learning on a large
pool of diverse and unlabeled SAR data. In the second stage, the global model
is used as a fixed feature extractor and a classifier is trained to partition
the feature space given the few-shot support samples, while simultaneously
being calibrated to detect anomalous inputs. Unlike competing approaches which
require a pristine labeled dataset for pretraining via meta-learning, our
approach learns highly transferable features from unlabeled data that have
little-to-no relation to the downstream task. We evaluate our method in
standard and extended MSTAR operating conditions and find it to achieve high
accuracy and robust out-of-distribution detection in many different few-shot
settings. Our results are particularly significant because they show the merit
of a global model approach to SAR ATR, which makes minimal assumptions, and
provides many axes for extendability.

Computer Vision and Pattern Recognition;Machine Learning