Recently, the emergence of a large number of Synthetic Aperture Radar (SAR) sensors and target datasets has made it possible to unify downstream tasks with self-supervised learning techniques, which can pave the way for building the foundation model in the SAR target recognition field. The major challenge of self-supervised learning for SAR target recognition lies in the generalizable representation learning in low data quality and noise.To address the aforementioned problem, we propose a knowledge-guided predictive architecture that uses local masked patches to predict the multiscale SAR feature representations of unseen context. The core of the proposed architecture lies in combining traditional SAR domain feature extraction with state-of-the-art scalable self-supervised learning for accurate generalized feature representations. The proposed framework is validated on various downstream datasets (MSTAR, FUSAR-Ship, SAR-ACD and SSDD), and can bring consistent performance improvement for SAR target recognition. The experimental results strongly demonstrate the unified performance improvement of the self-supervised learning technique for SAR target recognition across diverse targets, scenes and sensors.

翻译：近期，大量合成孔径雷达（SAR）传感器与目标数据集的涌现，使得通过自监督学习技术统一下游任务成为可能，这将为构建SAR目标识别领域的基础模型铺平道路。SAR目标识别自监督学习的主要挑战在于应对低数据质量与噪声条件下具有泛化能力的表征学习。针对上述问题，我们提出一种知识引导的预测架构，该架构利用局部遮蔽补丁预测未见上下文的SAR多尺度特征表征。该架构的核心在于将传统SAR领域特征提取与先进的可扩展自监督学习相结合，以实现精确的通用特征表征。所提框架在多个下游数据集（MSTAR、FUSAR-Ship、SAR-ACD及SSDD）上完成验证，能够为SAR目标识别带来一致性的性能提升。实验结果充分证明了自监督学习技术在不同目标、场景与传感器条件下对SAR目标识别的统一性能提升能力。