  The deep neural networks (DNNs) have freed the synthetic aperture radar
automatic target recognition (SAR ATR) from expertise-based feature designing
and demonstrated superiority over conventional solutions. There has been shown
the unique deficiency of ground vehicle benchmarks in shapes of strong
background correlation results in DNNs overfitting the clutter and being
non-robust to unfamiliar surroundings. However, the gap between fixed
background model training and varying background application remains
underexplored. Inspired by contrastive learning, this letter proposes a
solution called Contrastive Feature Alignment (CFA) aiming to learn invariant
representation for robust recognition. The proposed method contributes a mixed
clutter variants generation strategy and a new inference branch equipped with
channel-weighted mean square error (CWMSE) loss for invariant representation
learning. In specific, the generation strategy is delicately designed to better
attract clutter-sensitive deviation in feature space. The CWMSE loss is further
devised to better contrast this deviation and align the deep features activated
by the original images and corresponding clutter variants. The proposed CFA
combines both classification and CWMSE losses to train the model jointly, which
allows for the progressive learning of invariant target representation.
Extensive evaluations on the MSTAR dataset and six DNN models prove the
effectiveness of our proposal. The results demonstrated that the CFA-trained
models are capable of recognizing targets among unfamiliar surroundings that
are not included in the dataset, and are robust to varying signal-to-clutter
ratios.
