Synthetic aperture radar automatic target recognition (SAR ATR) with limited
data has recently been a hot research topic to enhance weak generalization.
Despite many excellent methods being proposed, a fundamental theory is lacked
to explain what problem the limited SAR data causes, leading to weak
generalization of ATR. In this paper, we establish a causal ATR model
demonstrating that noise N that could be blocked with ample SAR data, becomes
a confounder with limited data for recognition. As a result, it has a
detrimental causal effect damaging the efficacy of feature X extracted from
SAR images, leading to weak generalization of SAR ATR with limited data. The
effect of N on feature can be estimated and eliminated by using backdoor
adjustment to pursue the direct causality between X and the predicted class
Y. However, it is difficult for SAR images to precisely estimate and
eliminated the effect of N on X. The limited SAR data scarcely powers the
majority of existing optimization losses based on empirical risk minimization
(ERM), thus making it difficult to effectively eliminate N's effect. To
tackle with difficult estimation and elimination of N's effect, we propose a
dual invariance comprising the inner-class invariant proxy and the
noise-invariance loss. Motivated by tackling change with invariance, the
inner-class invariant proxy facilitates precise estimation of N's effect on
X by obtaining accurate invariant features for each class with the limited
data. The noise-invariance loss transitions the ERM's data quantity necessity
into a need for noise environment annotations, effectively eliminating N's
effect on X by cleverly applying the previous N's estimation as the noise
environment annotations. Experiments on three benchmark datasets indicate that
the proposed method achieves superior performance