A Causal Interventional SAR ATR Method with Limited Data via Dual Consistency

WANG Chenwei; LUO Siyi; HUANG Yulin; PEI Jifang; ZHANG Yin; YANG Jianyu

doi:10.11999/JEIT240140

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WANG Chenwei, LUO Siyi, HUANG Yulin, PEI Jifang, ZHANG Yin, YANG Jianyu. A Causal Interventional SAR ATR Method with Limited Data via Dual Consistency[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240140

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WANG Chenwei, LUO Siyi, HUANG Yulin, PEI Jifang, ZHANG Yin, YANG Jianyu. A Causal Interventional SAR ATR Method with Limited Data via Dual Consistency[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240140

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A Causal Interventional SAR ATR Method with Limited Data via Dual Consistency

doi: 10.11999/JEIT240140

School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China

Funds: The Natural Science Foundation of Sichuan Province (2023NSFSC1970)

Received Date: 2024-03-06
Rev Recd Date: 2024-08-28

Available Online: 2024-09-01

Abstract

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 loss 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 loss 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.
- Synthetic Aperture Radar (SAR),
- Automatic target recognition,
- Limited data,
- Causal inference

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References(18)

References

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