Small-Sample SAR Target Recognition Using a Multimodal Views Contrastive Learning Method

Self-supervised contrastive learning methods offer a promising approach to the small-sample synthetic aperture radar (SAR) automatic target recognition (ATR) problem by autonomously acquiring valuable visual representations from unlabeled data. However, current self-supervised contrastive learning methods primarily generate supervisory signals through augmented views of the original images, thereby underutilizing the rich information inherent in SAR images. To overcome this limitation, we integrate SAR targets’ geometric and physical properties, as captured in SAR target segmentation semantic maps and attribute scattering center reconstruction maps into the contrastive learning stage. Moreover, we propose a novel multimodal views contrastive learning method which contains two stages. In the contrastive learning stage, we leverage a large amount of unlabeled data for both intra-modal and cross-modal contrastive learning, thereby transferring discriminative information from these two views to the original image features to learn the feature representation. In the supervised training stage, the linear classifier is trained using a small number of labeled samples to partition the feature representation space and migrate to the downstream recognition task. Experiment results demonstrate that the proposed method achieves superior recognition performance in SAR small-sample ATR tasks and exhibits robust generalization capabilities, thereby providing additional discriminative information that augments target representation.