SARMAE：面向 SAR 表征学习的掩码自编码器
SARMAE: Masked Autoencoder for SAR Representation Learning
把 SAR 特有的相干斑噪声显式注入 MAE 的掩码重建流程，并用配对光学图像作为语义锚框约束特征对齐，这套「噪声感知预训练 + 跨模态语义约束」思路可迁移到其他低质量或强噪声成像域（如超声、低光照、去噪前表征）的自监督预训练。
原文依据 we design Speckle-Aware Representation Enhancement (SARE), which injects SAR-specific speckle noise into Masked Autoencoder
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论合成孔径雷达（SAR）影像在全天候、昼夜不停的遥感应用中发挥着关键作用。然而，现有的面向 SAR 的深度学习受制于数据稀缺，而 SAR 影像中具有物理成因的相干斑噪声又进一步阻碍了细粒度语义表征的学习。为应对这些挑战，我们提出 SARMAE，一种噪声感知的掩码自编码器，用于自监督 SAR 表征学习。具体而言，我们构建了 SAR-1M，这是首个百万规模的 SAR 数据集，并附带配对的光学图像，以支撑大规模预训练。在此基础上，我们设计了相干斑感知表征增强（SARE），将 SAR 特有的相干斑噪声注入掩码自编码器，以促进噪声感知且鲁棒的表征学习。此外，我们引入语义锚框表征约束（SARC），利用配对的光学先验对齐 SAR 特征并保证语义一致性。在多个 SAR 数据集上的大量实验表明，SARMAE 在分类、检测和分割任务上均取得最优性能。代码、数据与模型见 https://github.com/MiliLab/SARMAE。
英文原文摘要 1213 字符
Synthetic Aperture Radar (SAR) imagery plays a critical role in all-weather, day-and-night remote sensing applications. However, existing SAR-oriented deep learning is constrained by data scarcity, while the physically grounded speckle noise in SAR imagery further hampers fine-grained semantic representation learning. To address these challenges, we propose SARMAE, a Noise-Aware Masked Autoencoder for self-supervised SAR representation learning. Specifically, we construct SAR-1M, the first million-scale SAR dataset, with additional paired optical images, to enable large-scale pre-training. Building upon this, we design Speckle-Aware Representation Enhancement (SARE), which injects SAR-specific speckle noise into Masked Autoencoder to facilitate noise-aware and robust representation learning. Furthermore, we introduce Semantic Anchor Representation Constraint (SARC), which leverages paired optical priors to align SAR features and ensure semantic consistency. Extensive experiments across multiple SAR datasets demonstrate that SARMAE achieves state-of-the-art performance on classification, detection, and segmentation tasks. Code, data, and models are available at https://github.com/MiliLab/SARMAE.