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In autonomous driving, the perception module typically utilizes a combination of millimeter-wave radar and LiDAR. However, when driving in challenging environmental conditions, this perception combination fails to effectively acquire geometric shape information about the surrounding environment. Therefore, we propose an alternative perception approach that employs Synthetic Aperture Radar (SAR). However, existing algorithms heavily rely on large-scale datasets. In light of this, we propose a meta-learning framework, named Sample and Embedding Adaptive Network (Sea-Net), for few-shot SAR image object classification. Furthermore, the semantics of SAR images and traditional optical images differ, and thus data enhancement methods that are effective on optical images are less so on SAR images. Based on this observation, we introduce a self-adaptive augmentation algorithm for the center of the target …