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。

基于知识引导的预测架构的 SAR ATR 自监督学习

Self-Supervised Learning for SAR ATR with a Knowledge-Guided Predictive  Architecture

There has been a recent emphasis on integrating physical models and deep
neural networks (DNNs) for SAR target recognition, to improve performance and
achieve a higher level of physical interpretability. The attributed scattering
center (ASC) parameters garnered the most interest, being considered as
additional input data or features for fusion in most methods. However, the
performance greatly depends on the ASC optimization result, and the fusion
strategy is not adaptable to different types of physical information.
Meanwhile, the current evaluation scheme is inadequate to assess the model's
robustness and generalizability. Thus, we propose a physics inspired hybrid
attention (PIHA) mechanism and the once-for-all (OFA) evaluation protocol to
address the above issues. PIHA leverages the high-level semantics of physical
information to activate and guide the feature group aware of local semantics of
target, so as to re-weight the feature importance based on knowledge prior. It
is flexible and generally applicable to various physical models, and can be
integrated into arbitrary DNNs without modifying the original architecture. The
experiments involve a rigorous assessment using the proposed OFA, which entails
training and validating a model on either sufficient or limited data and
evaluating on multiple test sets with different data distributions. Our method
outperforms other state-of-the-art approaches in 12 test scenarios with same
ASC parameters. Moreover, we analyze the working mechanism of PIHA and evaluate
various PIHA enabled DNNs. The experiments also show PIHA is effective for
different physical information. The source code together with the adopted
physical information is available at this https URL

研究在 SAR 目标识别中引入物理模型和深度神经网络的融合，通过引入属性散射中心参数等数据进行性能改进和物理可解释性提高；然而，当前评估方案不足以评估模型的稳健性和泛化能力。因此，本文提出了基于物理的混合注意力机制和一劳永逸的评估协议来解决这些问题，并在多个测试集上对模型进行了严格评估，实验表明我们的方法在 12 个具有相同属性散射中心参数的测试场景中表现优于其他最先进的方法。