  The growing Synthetic Aperture Radar (SAR) data has the potential to build a
foundation model through Self-Supervised Learning (SSL) methods, which can
achieve various SAR Automatic Target Recognition (ATR) tasks with pre-training
in large-scale unlabeled data and fine-tuning in small labeled samples. SSL
aims to construct supervision signals directly from the data, which minimizes
the need for expensive expert annotation and maximizes the use of the expanding
data pool for a foundational model. This study investigates an effective SSL
method for SAR ATR, which can pave the way for a foundation model in SAR ATR.
The primary obstacles faced in SSL for SAR ATR are the small targets in remote
sensing and speckle noise in SAR images, corresponding to the SSL approach and
signals. To overcome these challenges, we present a novel Joint-Embedding
Predictive Architecture for SAR ATR (SAR-JEPA), which leverages local masked
patches to predict the multi-scale SAR gradient representations of unseen
context. The key aspect of SAR-JEPA is integrating SAR domain features to
ensure high-quality self-supervised signals as target features. Besides, we
employ local masks and multi-scale features to accommodate the various small
targets in remote sensing. By fine-tuning and evaluating our framework on three
target recognition datasets (vehicle, ship, and aircraft) with four other
datasets as pre-training, we demonstrate its outperformance over other SSL
methods and its effectiveness with increasing SAR data. This study showcases
the potential of SSL for SAR target recognition across diverse targets, scenes,
and sensors.
