2024, 46(10): 3928-3935.
doi: 10.11999/JEIT240140
Abstract:
Improving the generalization performance of methods under limited sample conditions is an important research direction in Synthetic Aperture Radar Automatic Target Recognition (SAR ATR). Addressing the fundamental problem in this field, a causal model is established in this paper for SAR ATR, demonstrating that interferences in SAR images, such as background and speckle, can be neglected under sufficient sample conditions. However, under limited sample conditions, these factors become confounding variables, introducing spurious correlations into the extracted SAR image features and affecting the generalization of SAR ATR. To accurately identify and eliminate these spurious effects in the features, this paper proposes a limited-sample SAR ATR method via dual consistency, which includes an intra-class feature consistency mask and effect-consistency loss. Firstly, based on the principle that discriminative features should have intra-class consistency and inter-class differences, the intra-class feature consistency mask is used to capture the consistent discriminative features of the target, subtracting the confounded part in the target features, and identifying the spurious effects introduced by interferences. Secondly, based on the idea of invariant risk minimization, the effect-consistency loss transforms the data requirement of empirical risk minimization into a need for labeling the similarity among effects of different samples, reducing the data demand for eliminating spurious effects and removing the spurious effects in the features. Thus, the limited-sample SAR ATR method proposed in this paper achieves true causal feature extraction and accurate recognition performance. Experiments on two benchmark datasets validate the effectiveness of this method which can achieve superior performance of SAR target recognition with limited sample.