부산대학교 도서관

Synthetic aperture radar automatic target recognition (SAR ATR) methods often struggle due to inadequate training data. In this letter, we introduce a causal interventional ATR method (CIATR), specifically designed to address the challenges posed by limited synthetic aperture radar (SAR) data. This approach is key in revealing the underlying causal relationships among essential factors in ATR, enabling us to achieve the desired causal effect without altering the imaging conditions (ICs). To address the challenges in SAR ATR with limited data, we developed a structural causal model (SCM) based on causal inference principles. This model helps identify how ICs, as confounders, induce spurious correlations between SAR images and their classifications, which can be solved by standard backdoor adjustment. Our implementation of backdoor adjustment begins with data augmentation, employing a spatial-frequency domain hybrid transformation. This step is crucial in estimating the potential effects of varied ICs on SAR images. Following this, a feature discrimination strategy is introduced to incorporate a hybrid similarity measurement. This technique is essential for assessing and mitigating the impact of changing ICs on the features extracted from SAR images, focusing on both structural and vector angle influences. The CIATR method effectively uncovers the true causal relationships between SAR images and their classes, even with limited data. Tested on MSTAR and OpenSARship datasets, our method shows promising performance in limited data scenarios, achieving 75.05% for ten-way five shots.