The growing Synthetic Aperture Radar (SAR) data can build a foundation model using self-supervised learning (SSL) methods, which can achieve various SAR automatic target recognition (ATR) tasks with pretraining in large-scale unlabeled data and fine-tuning in small-labeled samples. SSL aims to construct supervision signals directly from the data, minimizing the need for expensive expert annotation and maximizing the use of the expanding data pool for a foundational model. This study investigates an effective SSL method for SAR ATR, which can pave the way for a foundation model in SAR ATR. The primary obstacles faced in SSL for SAR ATR are small targets in remote sensing and speckle noise in SAR images, corresponding to the SSL approach and signals. To overcome these challenges, we present a novel joint-embedding predictive architecture for SAR ATR (SAR-JEPA) thatleverages local masked patches to predict the multi-scale SAR gradient representations of an unseen context. The key aspect of SAR-JEPA is integrating SAR domain features to ensure high-quality self-supervised signals as target features. In addition, we employ local masks and multi-scale features to accommodate various small targets in remote sensing. By fine-tuning and evaluating our framework on three target recognition datasets (vehicle, ship, and aircraft) with four other datasets as pretraining, we demonstrate its outperformance over other SSL methods and its effectiveness as the SAR data increases. This study demonstrates the potential of SSL for the recognition of SAR targets across diverse targets, scenes, and sensors. Our codes and weights are available in https://github.com/waterdisappear/SAR-JEPA.

中文翻译：

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预测梯度更好：利用联合嵌入预测架构探索 SAR ATR 的自监督学习


不断增长的合成孔径雷达（SAR）数据可以使用自监督学习（SSL）方法构建基础模型，通过大规模无标签数据的预训练和小规模无标签数据的微调，可以实现各种SAR自动目标识别（ATR）任务。 - 标记样品。 SSL 旨在直接从数据构建监督信号，最大限度地减少对昂贵的专家注释的需求，并最大限度地利用基础模型的扩展数据池。本研究研究了一种有效的 SAR ATR 的 SSL 方法，可以为 SAR ATR 的基础模型铺平道路。 SSL用于SAR ATR面临的主要障碍是遥感中的小目标和SAR图像中的散斑噪声，对应于SSL方法和信号。为了克服这些挑战，我们提出了一种新颖的 SAR ATR 联合嵌入预测架构（SAR-JEPA），该架构利用局部屏蔽补丁来预测不可见上下文的多尺度 SAR 梯度表示。 SAR-JEPA的关键是集成SAR域特征，以确保高质量的自监督信号作为目标特征。此外，我们采用局部掩模和多尺度特征来适应遥感中的各种小目标。通过在三个目标识别数据集（车辆、船舶和飞机）上微调和评估我们的框架以及其他四个数据集作为预训练，我们证明了其优于其他 SSL 方法的性能以及随着 SAR 数据增加的有效性。这项研究展示了 SSL 在识别不同目标、场景和传感器的 SAR 目标方面的潜力。我们的代码和权重可在 https://github.com/waterdisappear/SAR-JEPA 中找到。