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.

通过使用本文提出的具有知识引导预测架构，通过预测未见上下文的多尺度 SAR 特征表示，将传统 SAR 领域特征提取与最先进的可扩展自监督学习相结合，以准确的广义特征表示进行 SAR 目标识别，这一方法在各种下游数据集上验证成功，并在 SAR 目标识别中实现了一致的性能提升。